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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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">String kernels for protein sequence comparisons: improved fold recognition.</title><author><name sortKey="Nojoomi, Saghi" sort="Nojoomi, Saghi" uniqKey="Nojoomi S" first="Saghi" last="Nojoomi">Saghi Nojoomi</name><affiliation wicri:level="1"><nlm:affiliation>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616</wicri:regionArea></affiliation></author><author><name sortKey="Koehl, Patrice" sort="Koehl, Patrice" uniqKey="Koehl P" first="Patrice" last="Koehl">Patrice Koehl</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616, USA. pakoehl@ucdavis.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:28245816</idno><idno type="pmid">28245816</idno><idno type="doi">10.1186/s12859-017-1560-9</idno><idno type="wicri:Area/PubMed/Corpus">000D61</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000D61</idno><idno type="wicri:Area/PubMed/Curation">000D61</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000D61</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">String kernels for protein sequence comparisons: improved fold recognition.</title><author><name sortKey="Nojoomi, Saghi" sort="Nojoomi, Saghi" uniqKey="Nojoomi S" first="Saghi" last="Nojoomi">Saghi Nojoomi</name><affiliation wicri:level="1"><nlm:affiliation>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616</wicri:regionArea></affiliation></author><author><name sortKey="Koehl, Patrice" sort="Koehl, Patrice" uniqKey="Koehl P" first="Patrice" last="Koehl">Patrice Koehl</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616, USA. pakoehl@ucdavis.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616</wicri:regionArea></affiliation></author></analytic><series><title level="j">BMC bioinformatics</title><idno type="eISSN">1471-2105</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><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></keywords><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></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA-Directed RNA Polymerases</term><term>Proteins</term></keywords><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></keywords><keywords scheme="MESH" 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></keywords></textClass></profileDesc></teiHeader><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></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28245816</PMID><DateCompleted><Year>2017</Year><Month>10</Month><Day>18</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2105</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>1</Issue><PubDate><Year>2017</Year><Month>Feb</Month><Day>28</Day></PubDate></JournalIssue><Title>BMC bioinformatics</Title><ISOAbbreviation>BMC Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>String kernels for protein sequence comparisons: improved fold recognition.</ArticleTitle><Pagination><MedlinePgn>137</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12859-017-1560-9</ELocationID><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><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nojoomi</LastName><ForeName>Saghi</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Koehl</LastName><ForeName>Patrice</ForeName><Initials>P</Initials><Identifier Source="ORCID">http://orcid.org/0000-0002-0908-068X</Identifier><AffiliationInfo><Affiliation>Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616, USA. pakoehl@ucdavis.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>02</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Bioinformatics</MedlineTA><NlmUniqueID>100965194</NlmUniqueID><ISSNLinking>1471-2105</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011506">Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 2.7.7.6</RegistryNumber><NameOfSubstance UI="D012321">DNA-Directed RNA Polymerases</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012321" MajorTopicYN="N">DNA-Directed RNA Polymerases</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName><QualifierName UI="Q000145" MajorTopicYN="N">classification</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010802" MajorTopicYN="N">Phylogeny</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017510" MajorTopicYN="N">Protein Folding</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011506" MajorTopicYN="N">Proteins</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016415" MajorTopicYN="N">Sequence Alignment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020539" MajorTopicYN="N">Sequence Analysis, Protein</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017386" MajorTopicYN="Y">Sequence Homology, Amino Acid</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Alignment free methods</Keyword><Keyword MajorTopicYN="N">Kernel</Keyword><Keyword MajorTopicYN="N">Protein 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