sequencing predicted amino: Topics by Science.gov
Protein location prediction using atomic composition and global features of the amino acid sequence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cherian, Betsy Sheena, E-mail: betsy.skb@gmail.com; Nair, Achuthsankar S.

2010-01-22

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectivelymoreÂ Â» used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.Â«Â less

Predicting protein amidation sites by orchestrating amino acid sequence features

NASA Astrophysics Data System (ADS)

Zhao, Shuqiu; Yu, Hua; Gong, Xiujun

2017-08-01

Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.

Prediction of beta-turns from amino acid sequences using the residue-coupled model.

PubMed

Guruprasad, K; Shukla, S

2003-04-01

We evaluated the prediction of beta-turns from amino acid sequences using the residue-coupled model with an enlarged representative protein data set selected from the Protein Data Bank. Our results show that the probability values derived from a data set comprising 425 protein chains yielded an overall beta-turn prediction accuracy 68.74%, compared with 94.7% reported earlier on a data set of 30 proteins using the same method. However, we noted that the overall beta-turn prediction accuracy using probability values derived from the 30-protein data set reduces to 40.74% when tested on the data set comprising 425 protein chains. In contrast, using probability values derived from the 425 data set used in this analysis, the overall beta-turn prediction accuracy yielded consistent results when tested on either the 30-protein data set (64.62%) used earlier or a more recent representative data set comprising 619 protein chains (64.66%) or on a jackknife data set comprising 476 representative protein chains (63.38%). We therefore recommend the use of probability values derived from the 425 representative protein chains data set reported here, which gives more realistic and consistent predictions of beta-turns from amino acid sequences.

GCPred: a web tool for guanylyl cyclase functional centre prediction from amino acid sequence.

PubMed

Xu, Nuo; Fu, Dongfang; Li, Shiang; Wang, Yuxuan; Wong, Aloysius

2018-06-15

GCPred is a webserver for the prediction of guanylyl cyclase (GC) functional centres from amino acid sequence. GCs are enzymes that generate the signalling molecule cyclic guanosine 3', 5'-monophosphate from guanosine-5'-triphosphate. A novel class of GC centres (GCCs) has been identified in complex plant proteins. Using currently available experimental data, GCPred is created to automate and facilitate the identification of similar GCCs. The server features GCC values that consider in its calculation, the physicochemical properties of amino acids constituting the GCC and the conserved amino acids within the centre. From user input amino acid sequence, the server returns a table of GCC values and graphs depicting deviations from mean values. The utility of this server is demonstrated using plant proteins and the human interleukin-1 receptor-associated kinase family of proteins as example. The GCPred server is available at http://gcpred.com. Supplementary data are available at Bioinformatics online.

Fast computational methods for predicting protein structure from primary amino acid sequence

DOEpatents

Agarwal, Pratul Kumar [Knoxville, TN

2011-07-19

The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

Amino-acid sequence and predicted three-dimensional structure of pea seed (Pisum sativum) ferritin.

PubMed Central

Lobreaux, S; Yewdall, S J; Briat, J F; Harrison, P M

1992-01-01

The iron storage protein, ferritin, is widely distributed in the living kingdom. Here the complete cDNA and derived amino-acid sequence of pea seed ferritin are described, together with its predicted secondary structure, namely a four-helix-bundle fold similar to those of mammalian ferritins, with a fifth short helix at the C-terminus. An N-terminal extension of 71 residues contains a transit peptide (first 47 residues) responsible for plastid targetting as in other plant ferritins, and this is cleaved before assembly. The second part of the extension (24 residues) belongs to the mature subunit; it is cleaved during germination. The amino-acid sequence of pea seed ferritin is aligned with those of other ferritins (49% amino-acid identity with H-chains and 40% with L-chains of human liver ferritin in the aligned region). A three-dimensional model has been constructed by fitting the aligned sequence to the coordinates of human H-chains, with appropriate modifications. A folded conformation with an 11-residue helix is predicted for the N-terminal extension. As in mammalian ferritins, 24 subunits assemble into a hollow shell. In pea seed ferritin, its N-terminal extension is exposed on the outside surface of the shell. Within each pea subunit is a ferroxidase centre resembling those of human ferritin H-chains except for a replacement of Glu-62 by His. The channel at the 4-fold-symmetry axes defined by E-helices, is predicted to be hydrophilic in plant ferritins, whereas it is hydrophobic in mammalian ferritins. Images Fig. 3. Fig. 5. Fig. 6. PMID:1472006

Dipeptide Sequence Determination: Analyzing Phenylthiohydantoin Amino Acids by HPLC

NASA Astrophysics Data System (ADS)

Barton, Janice S.; Tang, Chung-Fei; Reed, Steven S.

2000-02-01

Amino acid composition and sequence determination, important techniques for characterizing peptides and proteins, are essential for predicting conformation and studying sequence alignment. This experiment presents improved, fundamental methods of sequence analysis for an upper-division biochemistry laboratory. Working in pairs, students use the Edman reagent to prepare phenylthiohydantoin derivatives of amino acids for determination of the sequence of an unknown dipeptide. With a single HPLC technique, students identify both the N-terminal amino acid and the composition of the dipeptide. This method yields good precision of retention times and allows use of a broad range of amino acids as components of the dipeptide. Students learn fundamental principles and techniques of sequence analysis and HPLC.

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition.

PubMed

Tamura, Takeyuki; Akutsu, Tatsuya

2007-11-30

Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html.

Amino-terminal sequence of glycoprotein D of herpes simplex virus types 1 and 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eisenberg, R.J.; Long, D.; Hogue-Angeletti, R.

1984-01-01

Glycoprotein D (gD) of herpes simplex virus is a structural component of the virion envelope which stimulates production of high titers of herpes simplex virus type-common neutralizing antibody. The authors caried out automated N-terminal amino acid sequencing studies on radiolabeled preparations of gD-1 (gD of herpes simplex virus type 1) and gD-2 (gD of herpes simplex virus type 2). Although some differences were noted, particularly in the methionine and alanine profiles for gD-1 and gD-2, the amino acid sequence of a number of the first 30 residues of the amino terminus of gD-1 and gD-2 appears to be quite similar.moreÂ Â» For both proteins, the first residue is a lysine. When we compared out sequence data for gD-1 with those predicted by nucleic acid sequencing, the two sequences could be aligned (with one exception) starting at residue 26 (lysine) of the predicted sequence. Thus, the first 25 amino acids of the predicted sequence are absent from the polypeptides isolated from infected cells.Â«Â less

Predicted secondary structure similarity in the absence of primary amino acid sequence homology: hepatitis B virus open reading frames.

PubMed Central

Schaeffer, E; Sninsky, J J

1984-01-01

Proteins that are related evolutionarily may have diverged at the level of primary amino acid sequence while maintaining similar secondary structures. Computer analysis has been used to compare the open reading frames of the hepatitis B virus to those of the woodchuck hepatitis virus at the level of amino acid sequence, and to predict the relative hydrophilic character and the secondary structure of putative polypeptides. Similarity is seen at the levels of relative hydrophilicity and secondary structure, in the absence of sequence homology. These data reinforce the proposal that these open reading frames encode viral proteins. Computer analysis of this type can be more generally used to establish structural similarities between proteins that do not share obvious sequence homology as well as to assess whether an open reading frame is fortuitous or codes for a protein. PMID:6585835

GASP: Gapped Ancestral Sequence Prediction for proteins

PubMed Central

Edwards, Richard J; Shields, Denis C

2004-01-01

Background The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments. Results Here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy. Conclusions GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alike. PMID:15350199

Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences.

PubMed

Chen, Peng; Li, Jinyan; Wong, Limsoon; Kuwahara, Hiroyuki; Huang, Jianhua Z; Gao, Xin

2013-08-01

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Copyright Â© 2013 Wiley Periodicals, Inc.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

PubMed

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright ÃÂ© 2010 Elsevier Ltd. All rights reserved.

Partial amino acid sequence of the branched chain amino acid aminotransferase (TmB) of E. coli JA199 pDU11

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feild, M.J.; Armstrong, F.B.

1987-05-01

E. coli JA199 pDU11 harbors a multicopy plasmid containing the ilv GEDAY gene cluster of S. typhimurium. TmB, gene product of ilv E, was purified, crystallized, and subjected to Edman degradation using a gas phase sequencer. The intact protein yielded an amino terminal 31 residue sequence. Both carboxymethylated apoenzyme and (/sup 3/H)-NaBH-reduced holoenzyme were then subjected to digestion by trypsin. The digests were fractionated using reversed phase HPLC, and the peptides isolated were sequenced. The borohydride-treated holoenzyme was used to isolate the cofactor-binding peptide. The peptide is 27 residues long and a comparison with known sequences of other aminotransferases revealedmoreÂ Â» limited homology. Peptides accounting for 211 of 288 predicted residues have been sequenced, including 9 residues of the carboxyl terminus. Comparison of peptides with the inferred amino acid sequence of the E. coli K-12 enzyme has helped determine the sequence of the amino terminal 59 residues; only two differences between the sequences are noted in this region.Â«Â less

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.

PubMed

Saravanan, Konda Mani; Dunker, A Keith; Krishnaswamy, Sankaran

2017-12-27

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

Sequence Alignment to Predict Across Species Susceptibility ...

EPA Pesticide Factsheets

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target, so it is amenable to variable degrees of protein characterization, depending on available information about the chemical/protein interaction and the molecular target itself. To allow for flexibility in the analysis, a layered strategy was adopted for the tool. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of candidate orthologs), the second level evaluates sequence similarity within selected domains (e.g., ligand-binding domain, DNA binding domain), and the third level of analysis compares individual amino acid residue positions identified as being of importance for protein conformation and/or ligand binding upon chemical perturbation. Each level of the SeqAPASS analysis provides increasing evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further ev

Amino acid sequence analysis of the annexin super-gene family of proteins.

PubMed

Barton, G J; Newman, R H; Freemont, P S; Crumpton, M J

1991-06-15

The annexins are a widespread family of calcium-dependent membrane-binding proteins. No common function has been identified for the family and, until recently, no crystallographic data existed for an annexin. In this paper we draw together 22 available annexin sequences consisting of 88 similar repeat units, and apply the techniques of multiple sequence alignment, pattern matching, secondary structure prediction and conservation analysis to the characterisation of the molecules. The analysis clearly shows that the repeats cluster into four distinct families and that greatest variation occurs within the repeat 3 units. Multiple alignment of the 88 repeats shows amino acids with conserved physicochemical properties at 22 positions, with only Gly at position 23 being absolutely conserved in all repeats. Secondary structure prediction techniques identify five conserved helices in each repeat unit and patterns of conserved hydrophobic amino acids are consistent with one face of a helix packing against the protein core in predicted helices a, c, d, e. Helix b is generally hydrophobic in all repeats, but contains a striking pattern of repeat-specific residue conservation at position 31, with Arg in repeats 4 and Glu in repeats 2, but unconserved amino acids in repeats 1 and 3. This suggests repeats 2 and 4 may interact via a buried saltbridge. The loop between predicted helices a and b of repeat 3 shows features distinct from the equivalent loop in repeats 1, 2 and 4, suggesting an important structural and/or functional role for this region. No compelling evidence emerges from this study for uteroglobin and the annexins sharing similar tertiary structures, or for uteroglobin representing a derivative of a primordial one-repeat structure that underwent duplication to give the present day annexins. The analyses performed in this paper are re-evaluated in the Appendix, in the light of the recently published X-ray structure for human annexin V. The structure confirms most of

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

PubMed Central

Zhang, Long; Jia, Lianyin; Ren, Yazhou

2017-01-01

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study. PMID:29117139

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.

PubMed

Wang, Jun; Zhang, Long; Jia, Lianyin; Ren, Yazhou; Yu, Guoxian

2017-11-08

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae , DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

77 FR 65537 - Requirements for Patent Applications Containing Nucleotide Sequence and/or Amino Acid Sequence...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-29

... DEPARTMENT OF COMMERCE Patent and Trademark Office Requirements for Patent Applications Containing Nucleotide Sequence and/or Amino Acid Sequence Disclosures ACTION: Proposed collection; comment request... Patent applications that contain nucleotide and/or amino acid sequence disclosures must include a copy of...

alpha-Amylase gene of Streptomyces limosus: nucleotide sequence, expression motifs, and amino acid sequence homology to mammalian and invertebrate alpha-amylases.

PubMed Central

Long, C M; Virolle, M J; Chang, S Y; Chang, S; Bibb, M J

1987-01-01

The nucleotide sequence of the coding and regulatory regions of the alpha-amylase gene (aml) of Streptomyces limosus was determined. High-resolution S1 mapping was used to locate the 5' end of the transcript and demonstrated that the gene is transcribed from a unique promoter. The predicted amino acid sequence has considerable identity to mammalian and invertebrate alpha-amylases, but not to those of plant, fungal, or eubacterial origin. Consistent with this is the susceptibility of the enzyme to an inhibitor of mammalian alpha-amylases. The amino-terminal sequence of the extracellular enzyme was determined, revealing the presence of a typical signal peptide preceding the mature form of the alpha-amylase. Images PMID:3500166

Amino- and carboxyl-terminal amino acid sequences of proteins coded by gag gene of murine leukemia virus

PubMed Central

Oroszlan, Stephen; Henderson, Louis E.; Stephenson, John R.; Copeland, Terry D.; Long, Cedric W.; Ihle, James N.; Gilden, Raymond V.

1978-01-01

The amino- and carboxyl-terminal amino acid sequences of proteins (p10, p12, p15, and p30) coded by the gag gene of Rauscher and AKR murine leukemia viruses were determined. Among these proteins, p15 from both viruses appears to have a blocked amino end. Proline was found to be the common NH2 terminus of both p30s and both p12s, and alanine of both p10s. The amino-terminal sequences of p30s are identical, as are those of p10s, while the p12 sequences are clearly distinctive but also show substantial homology. The carboxyl-terminal amino acids of both viral p30s and p12s are leucine and phenylalanine, respectively. Rauscher leukemia virus p15 has tyrosine as the carboxyl terminus while AKR virus p15 has phenylalanine in this position. The compositional and sequence data provide definite chemical criteria for the identification of analogous gag gene products and for the comparison of viral proteins isolated in different laboratories. On the basis of amino acid sequences and the previously proposed H-p15-p12-p30-p10-COOH peptide sequence in the precursor polyprotein, a model for cleavage sites involved in the post-translational processing of the precursor coded for by the gag gene is proposed. PMID:206897

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

PubMed

Du, Q S; Ma, Y; Xie, N Z; Huang, R B

2014-01-01

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PubMed Central

Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

2009-01-01

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains. PMID:19936254

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

PubMed

Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen

2008-01-07

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

The complete amino acid sequence of human skeletal-muscle fructose-bisphosphate aldolase.

PubMed Central

Freemont, P S; Dunbar, B; Fothergill-Gilmore, L A

1988-01-01

The complete amino acid sequence of human skeletal-muscle fructose-bisphosphate aldolase, comprising 363 residues, was determined. The sequence was deduced by automated sequencing of CNBr-cleavage, o-iodosobenzoic acid-cleavage, trypsin-digest and staphylococcal-proteinase-digest fragments. Comparison of the sequence with other class I aldolase sequences shows that the mammalian muscle isoenzyme is one of the most highly conserved enzymes known, with only about 2% of the residues changing per 100 million years. Non-mammalian aldolases appear to be evolving at the same rate as other glycolytic enzymes, with about 4% of the residues changing per 100 million years. Secondary-structure predictions are analysed in an accompanying paper [Sawyer, Fothergill-Gilmore & Freemont (1988) Biochem. J. 249, 789-793]. PMID:3355497

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.

PubMed

Raimondi, Daniele; Orlando, Gabriele; Pancsa, Rita; Khan, Taushif; Vranken, Wim F

2017-08-18

Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.

PreSSAPro: a software for the prediction of secondary structure by amino acid properties.

PubMed

Costantini, Susan; Colonna, Giovanni; Facchiano, Angelo M

2007-10-01

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.

Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences.

PubMed

Ali, Safdar; Majid, Abdul

2015-04-01

The diagnostic of human breast cancer is an intricate process and specific indicators may produce negative results. In order to avoid misleading results, accurate and reliable diagnostic system for breast cancer is indispensable. Recently, several interesting machine-learning (ML) approaches are proposed for prediction of breast cancer. To this end, we developed a novel classifier stacking based evolutionary ensemble system "Can-Evo-Ens" for predicting amino acid sequences associated with breast cancer. In this paper, first, we selected four diverse-type of ML algorithms of NaÃ¯ve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest as base-level classifiers. These classifiers are trained individually in different feature spaces using physicochemical properties of amino acids. In order to exploit the decision spaces, the preliminary predictions of base-level classifiers are stacked. Genetic programming (GP) is then employed to develop a meta-classifier that optimal combine the predictions of the base classifiers. The most suitable threshold value of the best-evolved predictor is computed using Particle Swarm Optimization technique. Our experiments have demonstrated the robustness of Can-Evo-Ens system for independent validation dataset. The proposed system has achieved the highest value of Area Under Curve (AUC) of ROC Curve of 99.95% for cancer prediction. The comparative results revealed that proposed approach is better than individual ML approaches and conventional ensemble approaches of AdaBoostM1, Bagging, GentleBoost, and Random Subspace. It is expected that the proposed novel system would have a major impact on the fields of Biomedical, Genomics, Proteomics, Bioinformatics, and Drug Development. Copyright Â© 2015 Elsevier Inc. All rights reserved.

Synthetic oligonucleotide probes deduced from amino acid sequence data. Theoretical and practical considerations.

PubMed

Lathe, R

1985-05-05

Synthetic probes deduced from amino acid sequence data are widely used to detect cognate coding sequences in libraries of cloned DNA segments. The redundancy of the genetic code dictates that a choice must be made between (1) a mixture of probes reflecting all codon combinations, and (2) a single longer "optimal" probe. The second strategy is examined in detail. The frequency of sequences matching a given probe by chance alone can be determined and also the frequency of sequences closely resembling the probe and contributing to the hybridization background. Gene banks cannot be treated as random associations of the four nucleotides, and probe sequences deduced from amino acid sequence data occur more often than predicted by chance alone. Probe lengths must be increased to confer the necessary specificity. Examination of hybrids formed between unique homologous probes and their cognate targets reveals that short stretches of perfect homology occurring by chance make a significant contribution to the hybridization background. Statistical methods for improving homology are examined, taking human coding sequences as an example, and considerations of codon utilization and dinucleotide frequencies yield an overall homology of greater than 82%. Recommendations for probe design and hybridization are presented, and the choice between using multiple probes reflecting all codon possibilities and a unique optimal probe is discussed.

The amino acid sequence of Staphylococcus aureus penicillinase.

PubMed Central

Ambler, R P

1975-01-01

The amino acid sequence of the penicillinase (penicillin amido-beta-lactamhydrolase, EC 3.5.2.6) from Staphylococcus aureus strain PC1 was determined. The protein consists of a single polypeptide chain of 257 residues, and the sequence was determined by characterization of tryptic, chymotryptic, peptic and CNBr peptides, with some additional evidence from thermolysin and S. aureus proteinase peptides. A mistake in the preliminary report of the sequence is corrected; residues 113-116 are now thought to be -Lys-Lys-Val-Lys- rather than -Lys-Val-Lys-Lys-. Detailed evidence for the amino acid sequence has been deposited as Supplementary Publication SUP 50056 (91 pages) at the British Library (Lending Division), Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies may be obtained on the terms given in Biochem. J. (1975) 145, 5. PMID:1218078

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

PubMed

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

Prediction of glutathionylation sites in proteins using minimal sequence information and their experimental validation.

PubMed

Pal, Debojyoti; Sharma, Deepak; Kumar, Mukesh; Sandur, Santosh K

2016-09-01

S-glutathionylation of proteins plays an important role in various biological processes and is known to be protective modification during oxidative stress. Since, experimental detection of S-glutathionylation is labor intensive and time consuming, bioinformatics based approach is a viable alternative. Available methods require relatively longer sequence information, which may prevent prediction if sequence information is incomplete. Here, we present a model to predict glutathionylation sites from pentapeptide sequences. It is based upon differential association of amino acids with glutathionylated and non-glutathionylated cysteines from a database of experimentally verified sequences. This data was used to calculate position dependent F-scores, which measure how a particular amino acid at a particular position may affect the likelihood of glutathionylation event. Glutathionylation-score (G-score), indicating propensity of a sequence to undergo glutathionylation, was calculated using position-dependent F-scores for each amino-acid. Cut-off values were used for prediction. Our model returned an accuracy of 58% with Matthew's correlation-coefficient (MCC) value of 0.165. On an independent dataset, our model outperformed the currently available model, in spite of needing much less sequence information. Pentapeptide motifs having high abundance among glutathionylated proteins were identified. A list of potential glutathionylation hotspot sequences were obtained by assigning G-scores and subsequent Protein-BLAST analysis revealed a total of 254 putative glutathionable proteins, a number of which were already known to be glutathionylated. Our model predicted glutathionylation sites in 93.93% of experimentally verified glutathionylated proteins. Outcome of this study may assist in discovering novel glutathionylation sites and finding candidate proteins for glutathionylation.

Amino acid sequence of the human fibronectin receptor

PubMed Central

1987-01-01

The amino acid sequence deduced from cDNA of the human placental fibronectin receptor is reported. The receptor is composed of two subunits: an alpha subunit of 1,008 amino acids which is processed into two polypeptides disulfide bonded to one another, and a beta subunit of 778 amino acids. Each subunit has near its COOH terminus a hydrophobic segment. This and other sequence features suggest a structure for the receptor in which the hydrophobic segments serve as transmembrane domains anchoring each subunit to the membrane and dividing each into a large ectodomain and a short cytoplasmic domain. The alpha subunit ectodomain has five sequence elements homologous to consensus Ca2+- binding sites of several calcium-binding proteins, and the beta subunit contains a fourfold repeat strikingly rich in cysteine. The alpha subunit sequence is 46% homologous to the alpha subunit of the vitronectin receptor. The beta subunit is 44% homologous to the human platelet adhesion receptor subunit IIIa and 47% homologous to a leukocyte adhesion receptor beta subunit. The high degree of homology (85%) of the beta subunit with one of the polypeptides of a chicken adhesion receptor complex referred to as integrin complex strongly suggests that the latter polypeptide is the chicken homologue of the fibronectin receptor beta subunit. These receptor subunit homologies define a superfamily of adhesion receptors. The availability of the entire protein sequence for the fibronectin receptor will facilitate studies on the functions of these receptors. PMID:2958481

Complete complementary DNA-derived amino acid sequence of canine cardiac phospholamban.

PubMed Central

Fujii, J; Ueno, A; Kitano, K; Tanaka, S; Kadoma, M; Tada, M

1987-01-01

Complementary DNA (cDNA) clones specific for phospholamban of sarcoplasmic reticulum membranes have been isolated from a canine cardiac cDNA library. The amino acid sequence deduced from the cDNA sequence indicates that phospholamban consists of 52 amino acid residues and lacks an amino-terminal signal sequence. The protein has an inferred mol wt 6,080 that is in agreement with its apparent monomeric mol wt 6,000, estimated previously by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Phospholamban contains two distinct domains, a hydrophilic region at the amino terminus (domain I) and a hydrophobic region at the carboxy terminus (domain II). We propose that domain I is localized at the cytoplasmic surface and offers phosphorylatable sites whereas domain II is anchored into the sarcoplasmic reticulum membrane. PMID:3793929

Statistical potential-based amino acid similarity matrices for aligning distantly related protein sequences.

PubMed

Tan, Yen Hock; Huang, He; Kihara, Daisuke

2006-08-15

Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.

Preferential amino acid sequences in alumina-catalyzed peptide bond formation.

PubMed

BujdÃ¡k, J; Rode, B M

2002-05-21

The catalytic effect of activated alumina on amino acid condensation was investigated. The readiness of amino acids to form peptide sequences was estimated on the basis of the yield of dipeptides and was found to decrease in the order glycine (Gly), alanine (Ala), leucine (Leu), valine (Val), proline (Pro). For example, approximately 15% Gly was converted to the dipeptide (Gly(2)), 5% to cyclic anhydride (cyc(Gly(2))) and small amounts of tri- (Gly(3)) and tetrapeptide (Gly(4)) were formed after 28 days. On the other hand, only trace amounts of Pro(2) were formed from proline under the same conditions. Preferential formation of certain sequences was observed in the mixed reaction systems containing two amino acids. For example, almost ten times more Gly-Val than Val-Gly was formed in the Gly+Val reaction system. The preferred sequences can be explained on the basis of an inductive effect that side groups have on the nucleophilicity and electrophilicity, respectively, of the amino and carboxyl groups. A comparison with published data of amino acid reactions in other reaction systems revealed that the main trends of preferential sequence formation were the same as those described for the salt-induced peptide formation (SIPF) reaction. The results of this work and other previously published papers show that alumina and related mineral surfaces might have played a crucial role in the prebiotic formation of the first peptides on the primitive earth.

Amino acid sequence of tyrosinase from Neurospora crassa.

PubMed Central

Lerch, K

1978-01-01

The amino-acid sequence of tyrosinase from Neurospora crassa (monophenol,dihydroxyphenylalanine:oxygen oxidoreductase, EC 1.14.18.1) is reported. This copper-containing oxidase consists of a single polypeptide chain of 407 amino acids. The primary structure was determined by automated and manual sequence analysis on fragments produced by cleavage with cyanogen bromide and on peptides obtained by digestion with trypsin, pepsin, thermolysin, or chymotrypsin. The amino terminus of the protein is acetylated and the single cysteinyl residue 96 is covalently linked via a thioether bridge to histidyl residue 94. The formation and the possible role of this unusual structure in Neurospora tyrosinase is discussed. Dye-sensitized photooxidation of apotyrosinase and active-site-directed inactivation of the native enzyme indicate the possible involvement of histidyl residues 188, 192, 289, and 305 or 306 as ligands to the active-site copper as well as in the catalytic mechanism of this monooxygenase. PMID:151279

Soil amino acid composition across a boreal forest successional sequence

Treesearch

Nancy R. Werdin-Pfisterer; Knut Kielland; Richard D. Boone

2009-01-01

Soil amino acids are important sources of organic nitrogen for plant nutrition, yet few studies have examined which amino acids are most prevalent in the soil. In this study, we examined the composition, concentration, and seasonal patterns of soil amino acids across a primary successional sequence encompassing a natural gradient of plant productivity and soil...

Protein binding hot spots prediction from sequence only by a new ensemble learning method.

PubMed

Hu, Shan-Shan; Chen, Peng; Wang, Bing; Li, Jinyan

2017-10-01

Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods have been widely used for hot spot prediction through sequence or structure characterization. As the structural information of proteins is not always solved, and thus hot spot identification from amino acid sequences only is more useful for real-life applications. This work proposes a new sequence-based model that combines physicochemical features with the relative accessible surface area of amino acid sequences for hot spot prediction. The model consists of 83 classifiers involving the IBk (Instance-based k means) algorithm, where instances are encoded by important properties extracted from a total of 544 properties in the AAindex1 (Amino Acid Index) database. Then top-performance classifiers are selected to form an ensemble by a majority voting technique. The ensemble classifier outperforms the state-of-the-art computational methods, yielding an F1 score of 0.80 on the benchmark binding interface database (BID) test set. http://www2.ahu.edu.cn/pchen/web/HotspotEC.htm .

Accurate prediction of proteinâprotein interactions from sequence alignments using a Bayesian method

PubMed Central

Burger, Lukas; van Nimwegen, Erik

2008-01-01

Accurate and large-scale prediction of proteinâprotein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of âhub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion

PubMed Central

Thomsen, Martin Christen FrÃ¸lund; Nielsen, Morten

2012-01-01

Seq2Logo is a web-based sequence logo generator. Sequence logos are a graphical representation of the information content stored in a multiple sequence alignment (MSA) and provide a compact and highly intuitive representation of the position-specific amino acid composition of binding motifs, active sites, etc. in biological sequences. Accurate generation of sequence logos is often compromised by sequence redundancy and low number of observations. Moreover, most methods available for sequence logo generation focus on displaying the position-specific enrichment of amino acids, discarding the equally valuable information related to amino acid depletion. Seq2logo aims at resolving these issues allowing the user to include sequence weighting to correct for data redundancy, pseudo counts to correct for low number of observations and different logotype representations each capturing different aspects related to amino acid enrichment and depletion. Besides allowing input in the format of peptides and MSA, Seq2Logo accepts input as Blast sequence profiles, providing easy access for non-expert end-users to characterize and identify functionally conserved/variable amino acids in any given protein of interest. The output from the server is a sequence logo and a PSSM. Seq2Logo is available at http://www.cbs.dtu.dk/biotools/Seq2Logo (14 May 2012, date last accessed). PMID:22638583

WEB-server for search of a periodicity in amino acid and nucleotide sequences

NASA Astrophysics Data System (ADS)

E Frenkel, F.; Skryabin, K. G.; Korotkov, E. V.

2017-12-01

A new web server (http://victoria.biengi.ac.ru/splinter/login.php) was designed and developed to search for periodicity in nucleotide and amino acid sequences. The web server operation is based upon a new mathematical method of searching for multiple alignments, which is founded on the position weight matrices optimization, as well as on implementation of the two-dimensional dynamic programming. This approach allows the construction of multiple alignments of the indistinctly similar amino acid and nucleotide sequences that accumulated more than 1.5 substitutions per a single amino acid or a nucleotide without performing the sequences paired comparisons. The article examines the principles of the web server operation and two examples of studying amino acid and nucleotide sequences, as well as information that could be obtained using the web server.

Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.

PubMed

Chou, Kuo-Chen

2005-01-01

With protein sequences entering into databanks at an explosive pace, the early determination of the family or subfamily class for a newly found enzyme molecule becomes important because this is directly related to the detailed information about which specific target it acts on, as well as to its catalytic process and biological function. Unfortunately, it is both time-consuming and costly to do so by experiments alone. In a previous study, the covariant-discriminant algorithm was introduced to identify the 16 subfamily classes of oxidoreductases. Although the results were quite encouraging, the entire prediction process was based on the amino acid composition alone without including any sequence-order information. Therefore, it is worthy of further investigation. To incorporate the sequence-order effects into the predictor, the 'amphiphilic pseudo amino acid composition' is introduced to represent the statistical sample of a protein. The novel representation contains 20 + 2lambda discrete numbers: the first 20 numbers are the components of the conventional amino acid composition; the next 2lambda numbers are a set of correlation factors that reflect different hydrophobicity and hydrophilicity distribution patterns along a protein chain. Based on such a concept and formulation scheme, a new predictor is developed. It is shown by the self-consistency test, jackknife test and independent dataset tests that the success rates obtained by the new predictor are all significantly higher than those by the previous predictors. The significant enhancement in success rates also implies that the distribution of hydrophobicity and hydrophilicity of the amino acid residues along a protein chain plays a very important role to its structure and function.

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences Â§ 1.821 Nucleotide and/or amino acid sequence disclosures in patent applications. (a) Nucleotide and...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 37 Patents, Trademarks, and Copyrights 1 2011-07-01 2011-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences Â§ 1.821 Nucleotide and/or amino acid sequence disclosures in patent applications. (a) Nucleotide and...

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the CÎ±-N bond (Phi) and the CÎ±-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8Â° and 44.6Â°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

PubMed

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naÃ¯ve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set

Comparative characterization of random-sequence proteins consisting of 5, 12, and 20 kinds of amino acids

PubMed Central

Tanaka, Junko; Doi, Nobuhide; Takashima, Hideaki; Yanagawa, Hiroshi

2010-01-01

Screening of functional proteins from a random-sequence library has been used to evolve novel proteins in the field of evolutionary protein engineering. However, random-sequence proteins consisting of the 20 natural amino acids tend to aggregate, and the occurrence rate of functional proteins in a random-sequence library is low. From the viewpoint of the origin of life, it has been proposed that primordial proteins consisted of a limited set of amino acids that could have been abundantly formed early during chemical evolution. We have previously found that members of a random-sequence protein library constructed with five primitive amino acids show high solubility (Doi et al., Protein Eng Des Sel 2005;18:279â284). Although such a library is expected to be appropriate for finding functional proteins, the functionality may be limited, because they have no positively charged amino acid. Here, we constructed three libraries of 120-amino acid, random-sequence proteins using alphabets of 5, 12, and 20 amino acids by preselection using mRNA display (to eliminate sequences containing stop codons and frameshifts) and characterized and compared the structural properties of random-sequence proteins arbitrarily chosen from these libraries. We found that random-sequence proteins constructed with the 12-member alphabet (including five primitive amino acids and positively charged amino acids) have higher solubility than those constructed with the 20-member alphabet, though other biophysical properties are very similar in the two libraries. Thus, a library of moderate complexity constructed from 12 amino acids may be a more appropriate resource for functional screening than one constructed from 20 amino acids. PMID:20162614

Amino acid sequence of a trypsin inhibitor from a Spirometra (Spirometra erinaceieuropaei).

PubMed

Sanda, A; Uchida, A; Itagaki, T; Kobayashi, H; Inokuchi, N; Koyama, T; Iwama, M; Ohgi, K; Irie, M

2001-12-01

A trypsin inhibitor that is highly homologous with bovine pancreatic trypsin inhibitor (BPTI) was co-purified along with RNase from Spirometra (Spirometra erinaceieuropaei). The amino acid sequence of this inhibitor (SETI) and the nucleotide sequence of the cDNA encoding this protein were determined by protein chemistry and gene technology. SETI contains 68 amino acid residues and has a molecular mass of 7,798 Da. SETI has 31 amino acid residues that are identical with BPTI's sequence, including 6 half-cystine and 5 aromatic amino acid residues. The active site Lys residue in BPTI is replaced by an Arg residue in SETI. SETI is an effective inhibitor of trypsin and moderately inhibits a-chymotrypsin, but less inhibits elastase or subtilisin. SETI was expressed by E. coli containing a PelB vector carrying the SETI encoding cDNA; an expression yield of 0.68 mg/l was obtained. The phylogenetic relationship of SETI and the other BPTI-like trypsin inhibitors was analyzed using most likelihood inference methods.

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2011 CFR

2011-07-01

... for nucleotide and/or amino acid sequence data. 1.822 Section 1.822 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.822 Symbols and format to be used for nucleotide and/or amino acid sequence data. (a) The symbols and format to be used for nucleotide and/or amino acid sequence data shall...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2011 CFR

2011-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by Â§ 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2013 CFR

2013-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by Â§ 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2012 CFR

2012-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by Â§ 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2010 CFR

2010-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by Â§ 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2014 CFR

2014-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences Â§ 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by Â§ 1.52(e)(5). The presentation of the...

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

PubMed

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-11-30

Î²-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of Î²-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class Î²-turns and prediction of the individual Î²-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of Î²-turn and not-Î²-turn. Furthermore NetTurnP shows improved performance on some of the specific Î²-turn types. In the present work, neural network methods have been trained to predict Î²-turn or not and individual Î²-turn types from the primary amino acid sequence. The individual Î²-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of Î²-turn or not is a superset comprised of all Î²-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific Î²-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

Sequences Of Amino Acids For Human Serum Albumin

NASA Technical Reports Server (NTRS)

Carter, Daniel C.

1992-01-01

Sequences of amino acids defined for use in making polypeptides one-third to one-sixth as large as parent human serum albumin molecule. Smaller, chemically stable peptides have diverse applications including service as artificial human serum and as active components of biosensors and chromatographic matrices. In applications involving production of artificial sera from new sequences, little or no concern about viral contaminants. Smaller genetically engineered polypeptides more easily expressed and produced in large quantities, making commercial isolation and production more feasible and profitable.

SeqAPASS: Sequence alignment to predict across-species ...

EPA Pesticide Factsheets

Efforts to shift the toxicity testing paradigm from whole organism studies to those focused on the initiation of toxicity and relevant pathways have led to increased utilization of in vitro and in silico methods. Hence the emergence of high through-put screening (HTS) programs, such as U.S. EPA ToxCast, and application of the adverse outcome pathway (AOP) framework for identifying and defining biological key events triggered upon perturbation of molecular initiating events and leading to adverse outcomes occuring at a level of organization relevant for risk assessment [1]. With these recent initiatives to harness the power of âthe pathwayâ in describing and evaluating toxicity comes the need to extrapolate data beyond the model species. Sequence alignment to predict across-species susceptibilty (SeqAPASS) is a web-based tool that allows the user to begin to understand how broadly HTS data or AOP constructs may plausibly be extrapolated across species, while describing the relative intrinsic susceptibiltiy of different taxa to chemicals with known modes of action (e.g., pharmaceuticals and pesticides). The tool rapidly and strategically assesses available molecular target information to describe protein sequence similarity at the primary amino acid sequence, conserved domain, and individual amino acid residue levels. This in silico approach to species extrapolation was designed to automate and streamline the relatively complex and time-consuming process of co

Complete amino acid sequence of bovine colostrum low-Mr cysteine proteinase inhibitor.

PubMed

Hirado, M; Tsunasawa, S; Sakiyama, F; Niinobe, M; Fujii, S

1985-07-01

The complete amino acid sequence of bovine colostrum cysteine proteinase inhibitor was determined by sequencing native inhibitor and peptides obtained by cyanogen bromide degradation, Achromobacter lysylendopeptidase digestion and partial acid hydrolysis of reduced and S-carboxymethylated protein. Achromobacter peptidase digestion was successfully used to isolate two disulfide-containing peptides. The inhibitor consists of 112 amino acids with an Mr of 12787. Two disulfide bonds were established between Cys 66 and Cys 77 and between Cys 90 and Cys 110. A high degree of homology in the sequence was found between the colostrum inhibitor and human gamma-trace, human salivary acidic protein and chicken egg-white cystatin.

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

PubMed Central

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

Amino acid sequence of the Amur tiger prion protein.

PubMed

Wu, Changde; Pang, Wanyong; Zhao, Deming

2006-10-01

Prion diseases are fatal neurodegenerative disorders in human and animal associated with conformational conversion of a cellular prion protein (PrP(C)) into the pathologic isoform (PrP(Sc)). Various data indicate that the polymorphisms within the open reading frame (ORF) of PrP are associated with the susceptibility and control the species barrier in prion diseases. In the present study, partial Prnp from 25 Amur tigers (tPrnp) were cloned and screened for polymorphisms. Four single nucleotide polymorphisms (T423C, A501G, C511A, A610G) were found; the C511A and A610G nucleotide substitutions resulted in the amino acid changes Lysine171Glutamine and Alanine204Threoine, respectively. The tPrnp amino acid sequence is similar to house cat (Felis catus ) and sheep, but differs significantly from other two cat Prnp sequences that were previously deposited in GenBank.

Nucleotide sequence analysis of the gene encoding the Deinococcus radiodurans surface protein, derived amino acid sequence, and complementary protein chemical studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, J.; Peters, M.; Lottspeich, F.

1987-11-01

The complete nucleotide sequence of the gene encoding the surface (hexagonally packed intermediate (HPI))-layer polypeptide of Deinococcus radiodurans Sark was determined and found to encode a polypeptide of 1036 amino acids. Amino acid sequence analysis of about 30% of the residues revealed that the mature polypeptide consists of at least 978 amino acids. The N terminus was blocked to Edman degradation. The results of proteolytic modification of the HPI layer in situ and M/sub r/ estimations of the HPI polypeptide expressed in Escherichia coli indicated that there is a leader sequence. The N-terminal region contained a very high percentage (29%)moreÂ Â» of threonine and serine, including a cluster of nine consecutive serine or threonine residues, whereas a stretch near the C terminus was extremely rich in aromatic amino acids (29%). The protein contained at least two disulfide bridges, as well as tightly bound reducing sugars and fatty acids.Â«Â less

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

PubMed

Jain, Tushar; Boland, Todd; Lilov, Asparouh; Burnina, Irina; Brown, Michael; Xu, Yingda; VÃ¡squez, Maximiliano

2017-12-01

The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. tushar.jain@adimab.com. Supplementary data are available at Bioinformatics online. Â© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

Streptococcal phosphoenolpyruvate-sugar phosphotransferase system: amino acid sequence and site of ATP-dependent phosphorylation of HPr

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deutscher, J.; Pevec, B.; Beyreuther, K.

1986-10-21

The amino acid sequence of histidine-containing protein (HPr) from Streptococcus faecalis has been determined by direct Edman degradation of intact HPr and by amino acid sequence analysis of tryptic peptides, V8 proteolyptic peptides, thermolytic peptides, and cyanogen bromide cleavage products. HPr from S. faecalis was found to contain 89 amino acid residues, corresponding to a molecular weight of 9438. The amino acid sequence of HPr from S. faecalis shows extended homology to the primary structure of HPr proteins from other bacteria. Besides the phosphoenolpyruvate-dependent phosphorylation of a histidyl residue in HPr, catalyzed by enzyme I of the bacterial phosphotransferase system,moreÂ Â» HPr was also found to be phosphorylated at a seryl residue in an ATP-dependent protein kinase catalyzed reaction. The site of ATP-dependent phosphorylation in HPr of S faecalis has now been determined. (/sup 32/P)P-Ser-HPr was digested with three different proteases, and in each case, a single labeled peptide was isolated. Following digestion with subtilisin, they obtained a peptide with the sequence -(P)Ser-Ile-Met-. Using chymotrypsin, they isolated a peptide with the sequence -Ser-Val-Asn-Leu-Lys-(P)Ser-Ile-Met-Gly-Val-Met-. The longest labeled peptide was obtained with V8 staphylococcal protease. According to amino acid analysis, this peptide contained 36 out of the 89 amino acid residues of HPr. The following sequence of 12 amino acid residues of the V8 peptide was determined: -Tyr-Lys-Gly-Lys-Ser-Val-Asn-Leu-Lys-(P)Ser-Ile-Met-. Thus, the site of ATP-dependent phosphorylation was determined to be Ser-46 within the primary structure of HPr.Â«Â less

NetTurnP â Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

PubMed Central

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-01-01

Î²-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of Î²-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class Î²-turns and prediction of the individual Î²-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of Î²-turn and not-Î²-turn. Furthermore NetTurnP shows improved performance on some of the specific Î²-turn types. In the present work, neural network methods have been trained to predict Î²-turn or not and individual Î²-turn types from the primary amino acid sequence. The individual Î²-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of Î²-turn or not is a superset comprised of all Î²-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC â=â0.50, Qtotalâ=â82.1%, sensitivity â=â75.6%, PPV â=â68.8% and AUC â=â0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 â 0.47. For the type specific Î²-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. Conclusion The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences. PMID:21152409

Correlation between fibroin amino acid sequence and physical silk properties.

PubMed

Fedic, Robert; Zurovec, Michal; Sehnal, Frantisek

2003-09-12

The fiber properties of lepidopteran silk depend on the amino acid repeats that interact during H-fibroin polymerization. The aim of our research was to relate repeat composition to insect biology and fiber strength. Representative regions of the H-fibroin genes were sequenced and analyzed in three pyralid species: wax moth (Galleria mellonella), European flour moth (Ephestia kuehniella), and Indian meal moth (Plodia interpunctella). The amino acid repeats are species-specific, evidently a diversification of an ancestral region of 43 residues, and include three types of regularly dispersed motifs: modifications of GSSAASAA sequence, stretches of tripeptides GXZ where X and Z represent bulky residues, and sequences similar to PVIVIEE. No concatenations of GX dipeptide or alanine, which are typical for Bombyx silkworms and Antheraea silk moths, respectively, were found. Despite different repeat structure, the silks of G. mellonella and E. kuehniella exhibit similar tensile strength as the Bombyx and Antheraea silks. We suggest that in these latter two species, variations in the repeat length obstruct repeat alignment, but sufficiently long stretches of iterated residues get superposed to interact. In the pyralid H-fibroins, interactions of the widely separated and diverse motifs depend on the precision of repeat matching; silk is strong in G. mellonella and E. kuehniella, with 2-3 types of long homogeneous repeats, and nearly 10 times weaker in P. interpunctella, with seven types of shorter erratic repeats. The high proportion of large amino acids in the H-fibroin of pyralids has probably evolved in connection with the spinning habit of caterpillars that live in protective silk tubes and spin continuously, enlarging the tubes on one end and partly devouring the other one. The silk serves as a depot of energetically rich and essential amino acids that may be scarce in the diet.

"De-novo" amino acid sequence elucidation of protein G'e by combined "top-down" and "bottom-up" mass spectrometry.

PubMed

Yefremova, Yelena; Al-Majdoub, Mahmoud; Opuni, Kwabena F M; Koy, Cornelia; Cui, Weidong; Yan, Yuetian; Gross, Michael L; Glocker, Michael O

2015-03-01

Mass spectrometric de-novo sequencing was applied to review the amino acid sequence of a commercially available recombinant protein GÂ´ with great scientific and economic importance. Substantial deviations to the published amino acid sequence (Uniprot Q54181) were found by the presence of 46 additional amino acids at the N-terminus, including a so-called "His-tag" as well as an N-terminal partial Î±-N-gluconoylation and Î±-N-phosphogluconoylation, respectively. The unexpected amino acid sequence of the commercial protein G' comprised 241 amino acids and resulted in a molecular mass of 25,998.9 Â± 0.2 Da for the unmodified protein. Due to the higher mass that is caused by its extended amino acid sequence compared with the original protein G' (185 amino acids), we named this protein "protein G'e." By means of mass spectrometric peptide mapping, the suggested amino acid sequence, as well as the N-terminal partial Î±-N-gluconoylations, was confirmed with 100% sequence coverage. After the protein G'e sequence was determined, we were able to determine the expression vector pET-28b from Novagen with the Xho I restriction enzyme cleavage site as the best option that was used for cloning and expressing the recombinant protein G'e in E. coli. A dissociation constant (K(d)) value of 9.4 nM for protein G'e was determined thermophoretically, showing that the N-terminal flanking sequence extension did not cause significant changes in the binding affinity to immunoglobulins.

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2014 CFR

2014-07-01

...â means those amino acids other than âXaaâ and those nucleotide bases other than ânâdefined in accordance... 37 Patents, Trademarks, and Copyrights 1 2014-07-01 2014-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2013 CFR

2013-07-01

...â means those amino acids other than âXaaâ and those nucleotide bases other than ânâdefined in accordance... 37 Patents, Trademarks, and Copyrights 1 2013-07-01 2013-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2012 CFR

2012-07-01

...â means those amino acids other than âXaaâ and those nucleotide bases other than ânâdefined in accordance... 37 Patents, Trademarks, and Copyrights 1 2012-07-01 2012-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

Complete Amino Acid Sequence of a Copper/Zinc-Superoxide Dismutase from Ginger Rhizome.

PubMed

Nishiyama, Yuki; Fukamizo, Tamo; Yoneda, Kazunari; Araki, Tomohiro

2017-04-01

Superoxide dismutase (SOD) is an antioxidant enzyme protecting cells from oxidative stress. Ginger (Zingiber officinale) is known for its antioxidant properties, however, there are no data on SODs from ginger rhizomes. In this study, we purified SOD from the rhizome of Z. officinale (Zo-SOD) and determined its complete amino acid sequence using N terminal sequencing, amino acid analysis, and de novo sequencing by tandem mass spectrometry. Zo-SOD consists of 151 amino acids with two signatu