String kernels for protein sequence comparisons: improved fold recognition.
Identifieur interne : 000D61 ( PubMed/Curation ); précédent : 000D60; suivant : 000D62String kernels for protein sequence comparisons: improved fold recognition.
Auteurs : Saghi Nojoomi [États-Unis] ; Patrice Koehl [États-Unis]Source :
- BMC bioinformatics [ 1471-2105 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA-Directed RNA Polymerases, Proteins.
- chemical , classification : DNA-Directed RNA Polymerases.
- methods : Sequence Analysis, Protein.
- Algorithms, Phylogeny, Protein Folding, Sequence Alignment, Sequence Homology, Amino Acid.
Abstract
The amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity.
DOI: 10.1186/s12859-017-1560-9
PubMed: 28245816
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<term>DNA-Directed RNA Polymerases (chemistry)</term>
<term>DNA-Directed RNA Polymerases (classification)</term>
<term>Phylogeny</term>
<term>Protein Folding</term>
<term>Proteins (chemistry)</term>
<term>Sequence Alignment</term>
<term>Sequence Analysis, Protein (methods)</term>
<term>Sequence Homology, Amino Acid</term>
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<keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term>
<term>Alignement de séquences</term>
<term>Analyse de séquence de protéine ()</term>
<term>DNA-directed RNA polymerases ()</term>
<term>Phylogénie</term>
<term>Pliage des protéines</term>
<term>Protéines ()</term>
<term>Similitude de séquences d'acides aminés</term>
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<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA-Directed RNA Polymerases</term>
<term>Proteins</term>
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<keywords scheme="MESH" type="chemical" qualifier="classification" xml:lang="en"><term>DNA-Directed RNA Polymerases</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Sequence Analysis, Protein</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>Phylogeny</term>
<term>Protein Folding</term>
<term>Sequence Alignment</term>
<term>Sequence Homology, Amino Acid</term>
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<term>Alignement de séquences</term>
<term>Analyse de séquence de protéine</term>
<term>DNA-directed RNA polymerases</term>
<term>Phylogénie</term>
<term>Pliage des protéines</term>
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<front><div type="abstract" xml:lang="en">The amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity.</div>
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<Title>BMC bioinformatics</Title>
<ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation>
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<ArticleTitle>String kernels for protein sequence comparisons: improved fold recognition.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">The amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">In this study, we develop an alignment free alternative to these methods that is based on the concept of string kernels. Starting from recently proposed kernels on the discrete space of protein sequences (Shen et al, Found. Comput. Math., 2013,14:951-984), we introduce our own version, SeqKernel. Its implementation depends on two parameters, a coefficient that tunes the substitution matrix and the maximum length of k-mers that it includes. We provide an exhaustive analysis of the impacts of these two parameters on the performance of SeqKernel for fold recognition. We show that with the right choice of parameters, use of the SeqKernel similarity measure improves fold recognition compared to the use of traditional alignment-based methods. We illustrate the application of SeqKernel to inferring phylogeny on RNA polymerases and show that it performs as well as methods based on multiple sequence alignments.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">We have presented and characterized a new alignment free method based on a mathematical kernel for scoring the similarity of protein sequences. We discuss possible improvements of this method, as well as an extension of its applications to other modeling methods that rely on sequence comparison.</AbstractText>
</Abstract>
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<ForeName>Saghi</ForeName>
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<AffiliationInfo><Affiliation>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616, USA.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616, USA. pakoehl@ucdavis.edu.</Affiliation>
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