Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction
ABSTRACT The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspo...
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Veröffentlicht in: | Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2015-05, Vol.83 (5), p.881-890 |
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description | ABSTRACT
The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/. Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc. |
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The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/. Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.</description><identifier>ISSN: 0887-3585</identifier><identifier>EISSN: 1097-0134</identifier><identifier>DOI: 10.1002/prot.24782</identifier><identifier>PMID: 25693513</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>decoy discrimination ; docking decoy ; energy landscape ; knowledge-based potential energy function ; ligand-receptor systems ; Molecular Docking Simulation ; neural network ; Neural Networks (Computer) ; optimized potential ; Protein Folding ; protein structure prediction ; Proteins - chemistry ; Thermodynamics</subject><ispartof>Proteins, structure, function, and bioinformatics, 2015-05, Vol.83 (5), p.881-890</ispartof><rights>2015 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3932-819d41eadcb2ea064fdb7cdfad1832f4934beda46c06afb64d6003f6d7f419b63</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fprot.24782$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fprot.24782$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,778,782,1414,27911,27912,45561,45562</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25693513$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chae, Myong-Ho</creatorcontrib><creatorcontrib>Krull, Florian</creatorcontrib><creatorcontrib>Knapp, Ernst-Walter</creatorcontrib><title>Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction</title><title>Proteins, structure, function, and bioinformatics</title><addtitle>Proteins</addtitle><description>ABSTRACT
The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/. Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.</description><subject>decoy discrimination</subject><subject>docking decoy</subject><subject>energy landscape</subject><subject>knowledge-based potential energy function</subject><subject>ligand-receptor systems</subject><subject>Molecular Docking Simulation</subject><subject>neural network</subject><subject>Neural Networks (Computer)</subject><subject>optimized potential</subject><subject>Protein Folding</subject><subject>protein structure prediction</subject><subject>Proteins - chemistry</subject><subject>Thermodynamics</subject><issn>0887-3585</issn><issn>1097-0134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9P3DAQxa2qqGy3vfABUKReuJjasWPHR0Rhi4QaqLYqh0qWE08kQ_7VdtTST1-HBQ6cOHk0_j2_8TyEDig5poTknyc_xuOcyzJ_g1aUKIkJZfwtWpGylJgVZbGP3odwSwgRiol3aD8vUlFQtkK_qim63v0Dm1kXohkawBYmGCwMMTNx7PFknMe1CQmZxpjaznTZl6q6ytrRZ4s5uCEL0c9NnD2kDljXRDcOH9Bea7oAHx_PNfpxfrY9_Yovq83F6cklbphiOS6pspyCsU2dgyGCt7aWjW2NpSXLW64Yr8EaLhoiTFsLbgUhrBVWtpyqWrA1Otq9m4b5PUOIunehga4zA4xz0FRIwYpCEvYalEnFWDJdo08v0Ntx9kP6yELlihZSyUQdPlJz3YPVk3e98ff6acUJoDvgj-vg_vmeEr2Ep5f96Yfw9NX3avtQJQ3eaVIk8PdZY_ydXuYr9M9vG32e_Dfb6xt9w_4D3Mmcmg</recordid><startdate>201505</startdate><enddate>201505</enddate><creator>Chae, Myong-Ho</creator><creator>Krull, Florian</creator><creator>Knapp, Ernst-Walter</creator><general>Blackwell Publishing Ltd</general><general>Wiley Subscription Services, Inc</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7QL</scope><scope>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7TK</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>201505</creationdate><title>Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction</title><author>Chae, Myong-Ho ; Krull, Florian ; Knapp, Ernst-Walter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3932-819d41eadcb2ea064fdb7cdfad1832f4934beda46c06afb64d6003f6d7f419b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>decoy discrimination</topic><topic>docking decoy</topic><topic>energy landscape</topic><topic>knowledge-based potential energy function</topic><topic>ligand-receptor systems</topic><topic>Molecular Docking Simulation</topic><topic>neural network</topic><topic>Neural Networks (Computer)</topic><topic>optimized potential</topic><topic>Protein Folding</topic><topic>protein structure prediction</topic><topic>Proteins - chemistry</topic><topic>Thermodynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chae, Myong-Ho</creatorcontrib><creatorcontrib>Krull, Florian</creatorcontrib><creatorcontrib>Knapp, Ernst-Walter</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Proteins, structure, function, and bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chae, Myong-Ho</au><au>Krull, Florian</au><au>Knapp, Ernst-Walter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction</atitle><jtitle>Proteins, structure, function, and bioinformatics</jtitle><addtitle>Proteins</addtitle><date>2015-05</date><risdate>2015</risdate><volume>83</volume><issue>5</issue><spage>881</spage><epage>890</epage><pages>881-890</pages><issn>0887-3585</issn><eissn>1097-0134</eissn><abstract>ABSTRACT
The DOcking decoy‐based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance‐dependent atom‐pair interactions. To optimize the atom‐pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand–receptor systems (or just pairs). Thus, a total of 8609 ligand–receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand–receptor systems, 1000 evenly sampled docking decoys with 0–10 Å interface root‐mean‐square‐deviation (iRMSD) were generated with a method used before for protein–protein docking. A neural network‐based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel‐like energy landscape for the interaction between these hypothetical ligand–receptor systems. Thus, our method hierarchically models the overall funnel‐like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom‐pair‐based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation‐dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand–receptor systems and their decoys are freely available at http://agknapp.chemie.fu‐berlin.de/doop/. Proteins 2015; 83:881–890. © 2015 Wiley Periodicals, Inc.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>25693513</pmid><doi>10.1002/prot.24782</doi><tpages>10</tpages></addata></record> |
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subjects | decoy discrimination docking decoy energy landscape knowledge-based potential energy function ligand-receptor systems Molecular Docking Simulation neural network Neural Networks (Computer) optimized potential Protein Folding protein structure prediction Proteins - chemistry Thermodynamics |
title | Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction |
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