MersV1, PubMed, Corpus, bibRecord, 001E93

Supervised learning and prediction of physical interactions between human and HIV proteins.

Identifieur interne : 001E93 ( PubMed/Corpus ); précédent : 001E92; suivant : 001E94

Supervised learning and prediction of physical interactions between human and HIV proteins.

Auteurs : Matthew D. Dyer ; T M Murali ; Bruno W. Sobral

Source :

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases [ 1567-7257 ] ; 2011.

RBID : pubmed:21382517

English descriptors

KwdEn :
- Binding Sites, Computer Simulation, HIV (genetics), HIV (metabolism), HIV Infections (metabolism), Host-Pathogen Interactions, Human Immunodeficiency Virus Proteins (metabolism), Humans, Models, Chemical, Protein Binding.
MESH :
- chemical , metabolism : Human Immunodeficiency Virus Proteins.
- genetics : HIV.
- metabolism : HIV, HIV Infections.
- Binding Sites, Computer Simulation, Host-Pathogen Interactions, Humans, Models, Chemical, Protein Binding.

Abstract

Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions.

DOI: 10.1016/j.meegid.2011.02.022
PubMed: 21382517

Links to Exploration step

pubmed:21382517

Le document en format XML

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<author><name sortKey="Dyer, Matthew D" sort="Dyer, Matthew D" uniqKey="Dyer M" first="Matthew D" last="Dyer">Matthew D. Dyer</name>
<affiliation><nlm:affiliation>Virginia Bioinformatics Institute, Virginia Tech, 1 Washington St, Blacksburg, VA 24061, USA.</nlm:affiliation>
</affiliation>
</author>
<author><name sortKey="Murali, T M" sort="Murali, T M" uniqKey="Murali T" first="T M" last="Murali">T M Murali</name>
</author>
<author><name sortKey="Sobral, Bruno W" sort="Sobral, Bruno W" uniqKey="Sobral B" first="Bruno W" last="Sobral">Bruno W. Sobral</name>
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<author><name sortKey="Dyer, Matthew D" sort="Dyer, Matthew D" uniqKey="Dyer M" first="Matthew D" last="Dyer">Matthew D. Dyer</name>
<affiliation><nlm:affiliation>Virginia Bioinformatics Institute, Virginia Tech, 1 Washington St, Blacksburg, VA 24061, USA.</nlm:affiliation>
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<author><name sortKey="Murali, T M" sort="Murali, T M" uniqKey="Murali T" first="T M" last="Murali">T M Murali</name>
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<author><name sortKey="Sobral, Bruno W" sort="Sobral, Bruno W" uniqKey="Sobral B" first="Bruno W" last="Sobral">Bruno W. Sobral</name>
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<series><title level="j">Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases</title>
<idno type="eISSN">1567-7257</idno>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Binding Sites</term>
<term>Computer Simulation</term>
<term>HIV (genetics)</term>
<term>HIV (metabolism)</term>
<term>HIV Infections (metabolism)</term>
<term>Host-Pathogen Interactions</term>
<term>Human Immunodeficiency Virus Proteins (metabolism)</term>
<term>Humans</term>
<term>Models, Chemical</term>
<term>Protein Binding</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Human Immunodeficiency Virus Proteins</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>HIV</term>
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<keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>HIV</term>
<term>HIV Infections</term>
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<front><div type="abstract" xml:lang="en">Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions.</div>
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<Month>09</Month>
<Day>27</Day>
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<DateRevised><Year>2018</Year>
<Month>11</Month>
<Day>13</Day>
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<JournalIssue CitedMedium="Internet"><Volume>11</Volume>
<Issue>5</Issue>
<PubDate><Year>2011</Year>
<Month>Jul</Month>
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<Title>Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases</Title>
<ISOAbbreviation>Infect. Genet. Evol.</ISOAbbreviation>
</Journal>
<ArticleTitle>Supervised learning and prediction of physical interactions between human and HIV proteins.</ArticleTitle>
<Pagination><MedlinePgn>917-23</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1016/j.meegid.2011.02.022</ELocationID>
<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions.</AbstractText>
<AbstractText Label="METHODOLOGY" NlmCategory="METHODS">We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell.</AbstractText>
<AbstractText Label="SIGNIFICANCE" NlmCategory="CONCLUSIONS">Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.</AbstractText>
<CopyrightInformation>Copyright © 2011 Elsevier B.V. All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Dyer</LastName>
<ForeName>Matthew D</ForeName>
<Initials>MD</Initials>
<AffiliationInfo><Affiliation>Virginia Bioinformatics Institute, Virginia Tech, 1 Washington St, Blacksburg, VA 24061, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Murali</LastName>
<ForeName>T M</ForeName>
<Initials>TM</Initials>
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<Author ValidYN="Y"><LastName>Sobral</LastName>
<ForeName>Bruno W</ForeName>
<Initials>BW</Initials>
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<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>HHSN272200900040C</GrantID>
<Acronym>AI</Acronym>
<Agency>NIAID NIH HHS</Agency>
<Country>United States</Country>
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<Agency>PHS HHS</Agency>
<Country>United States</Country>
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<ArticleDate DateType="Electronic"><Year>2011</Year>
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<MedlineJournalInfo><Country>Netherlands</Country>
<MedlineTA>Infect Genet Evol</MedlineTA>
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<MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName>
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<MeshHeading><DescriptorName UI="D006678" MajorTopicYN="N">HIV</DescriptorName>
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<MeshHeading><DescriptorName UI="D015658" MajorTopicYN="N">HIV Infections</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName>
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<MeshHeading><DescriptorName UI="D054884" MajorTopicYN="N">Host-Pathogen Interactions</DescriptorName>
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<MeshHeading><DescriptorName UI="D054298" MajorTopicYN="N">Human Immunodeficiency Virus Proteins</DescriptorName>
<QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName>
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<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008956" MajorTopicYN="N">Models, Chemical</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011485" MajorTopicYN="N">Protein Binding</DescriptorName>
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Supervised learning and prediction of physical interactions between human and HIV proteins.

Supervised learning and prediction of physical interactions between human and HIV proteins.

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