Collective variable driven molecular dynamics to improve protein–protein docking scoring
•Protein–protein interactions are fundamental in most cellular processes.•Hydrogen bond networks play a key role in molecular recognition.•MD is the tool by excellence to explore the protein potential energy landscape.•Collective variable driven MD have been able to accelerate rare events. In biophy...
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Veröffentlicht in: | Computational biology and chemistry 2014-04, Vol.49, p.1-6 |
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creator | Masone, Diego Grosdidier, Solène |
description | •Protein–protein interactions are fundamental in most cellular processes.•Hydrogen bond networks play a key role in molecular recognition.•MD is the tool by excellence to explore the protein potential energy landscape.•Collective variable driven MD have been able to accelerate rare events.
In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein–protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein–protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations. |
doi_str_mv | 10.1016/j.compbiolchem.2013.12.003 |
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In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein–protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein–protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations.</description><identifier>ISSN: 1476-9271</identifier><identifier>EISSN: 1476-928X</identifier><identifier>DOI: 10.1016/j.compbiolchem.2013.12.003</identifier><identifier>PMID: 24509001</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Biology ; Collective variable ; Computation ; Docking ; Dynamical systems ; Hydrogen Bonding ; Hydrogen bonds ; Mathematical models ; Molecular dynamics ; Molecular Dynamics Simulation ; Proteins - chemistry ; Proteins - metabolism ; Protein–protein ; Scoring</subject><ispartof>Computational biology and chemistry, 2014-04, Vol.49, p.1-6</ispartof><rights>2014 Elsevier Ltd</rights><rights>Copyright © 2014 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c413t-9337427b20f679a5f0193697a52b7198af5a3e14d4976d357195950a59cb913e3</citedby><cites>FETCH-LOGICAL-c413t-9337427b20f679a5f0193697a52b7198af5a3e14d4976d357195950a59cb913e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compbiolchem.2013.12.003$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24509001$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Masone, Diego</creatorcontrib><creatorcontrib>Grosdidier, Solène</creatorcontrib><title>Collective variable driven molecular dynamics to improve protein–protein docking scoring</title><title>Computational biology and chemistry</title><addtitle>Comput Biol Chem</addtitle><description>•Protein–protein interactions are fundamental in most cellular processes.•Hydrogen bond networks play a key role in molecular recognition.•MD is the tool by excellence to explore the protein potential energy landscape.•Collective variable driven MD have been able to accelerate rare events.
In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein–protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein–protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations.</description><subject>Biology</subject><subject>Collective variable</subject><subject>Computation</subject><subject>Docking</subject><subject>Dynamical systems</subject><subject>Hydrogen Bonding</subject><subject>Hydrogen bonds</subject><subject>Mathematical models</subject><subject>Molecular dynamics</subject><subject>Molecular Dynamics Simulation</subject><subject>Proteins - chemistry</subject><subject>Proteins - metabolism</subject><subject>Protein–protein</subject><subject>Scoring</subject><issn>1476-9271</issn><issn>1476-928X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkc1u2zAMx4Vhw9qmfYXB2GmXuKRkWdFuQ7Z-AAV2aYFhF0GW6U2ZbWWSE6C3vsPecE9SBUmLHnshKfHPD_DH2EeEEgHr81XpwrBufOjdbxpKDihK5CWAeMOOsVL1XPPFj7fPscIjdpLSCoALAPmeHfFKggbAY_ZzGfqe3OS3VGxt9LbpqWhjfo7FEHJm09tYtPejHbxLxRQKP6xjyOpsJ_Lj_4d_h6hog_vjx19FciFmf8redbZPdHbwM3Z38e12eTW_-X55vfxyM3cVimmuhVAVVw2Hrlbayg5Qi1orK3mjUC9sJ60grNpKq7oVMv9JLcFK7RqNgsSMfdr3zXv83VCazOCTo763I4VNMigF6EVdV-oVUuScL3YbzNjnvdTFkFKkzqyjH2y8Nwhmh8GszEsMZofBIDcZQy7-cJizaQZqn0uf7p4FX_cCyofZeoomOU-jo9bHTMO0wb9mziN-iqC9</recordid><startdate>201404</startdate><enddate>201404</enddate><creator>Masone, Diego</creator><creator>Grosdidier, Solène</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201404</creationdate><title>Collective variable driven molecular dynamics to improve protein–protein docking scoring</title><author>Masone, Diego ; Grosdidier, Solène</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-9337427b20f679a5f0193697a52b7198af5a3e14d4976d357195950a59cb913e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Biology</topic><topic>Collective variable</topic><topic>Computation</topic><topic>Docking</topic><topic>Dynamical systems</topic><topic>Hydrogen Bonding</topic><topic>Hydrogen bonds</topic><topic>Mathematical models</topic><topic>Molecular dynamics</topic><topic>Molecular Dynamics Simulation</topic><topic>Proteins - chemistry</topic><topic>Proteins - metabolism</topic><topic>Protein–protein</topic><topic>Scoring</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Masone, Diego</creatorcontrib><creatorcontrib>Grosdidier, Solène</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computational biology and chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Masone, Diego</au><au>Grosdidier, Solène</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Collective variable driven molecular dynamics to improve protein–protein docking scoring</atitle><jtitle>Computational biology and chemistry</jtitle><addtitle>Comput Biol Chem</addtitle><date>2014-04</date><risdate>2014</risdate><volume>49</volume><spage>1</spage><epage>6</epage><pages>1-6</pages><issn>1476-9271</issn><eissn>1476-928X</eissn><abstract>•Protein–protein interactions are fundamental in most cellular processes.•Hydrogen bond networks play a key role in molecular recognition.•MD is the tool by excellence to explore the protein potential energy landscape.•Collective variable driven MD have been able to accelerate rare events.
In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study, we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein–protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein–protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>24509001</pmid><doi>10.1016/j.compbiolchem.2013.12.003</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biology Collective variable Computation Docking Dynamical systems Hydrogen Bonding Hydrogen bonds Mathematical models Molecular dynamics Molecular Dynamics Simulation Proteins - chemistry Proteins - metabolism Protein–protein Scoring |
title | Collective variable driven molecular dynamics to improve protein–protein docking scoring |
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