Development of novel statistical potentials for protein fold recognition
The need to perform large-scale studies of protein fold recognition, structure prediction and protein–protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of...
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Veröffentlicht in: | Current opinion in structural biology 2004-04, Vol.14 (2), p.225-232 |
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description | The need to perform large-scale studies of protein fold recognition, structure prediction and protein–protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction. |
doi_str_mv | 10.1016/j.sbi.2004.03.002 |
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New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.</description><subject>Algorithms</subject><subject>Computer Simulation</subject><subject>Crystallography, X-Ray</subject><subject>Models, Molecular</subject><subject>Protein Folding</subject><subject>Proteins - chemistry</subject><subject>Thermodynamics</subject><issn>0959-440X</issn><issn>1879-033X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LAzEQhoMotlZ_gBfZk7ddJ5tsssGT-FWh4EWht7DJzkrKdlM3acF_b0oL3jzNDHnmZfIQck2hoEDF3aoIxhUlAC-AFQDlCZnSWqocGFuekimoSuWcw3JCLkJYAYCgvD4nE1qBYjWrp2T-hDvs_WaNQ8x8lw0-jVmITXQhOtv02cbH9OaaPmSdH7PNmGY3pL5vsxGt_xpcdH64JGddYvDqWGfk8-X543GeL95f3x4fFrllXMW8MoxVLVhrkduqUZIrqYQRxnQoqeCGci6YMB1QlLXsKiGkqYWgDWOl4JLNyO0hNx3yvcUQ9doFi33fDOi3QUta8xJUmUB6AO3oQxix05vRrZvxR1PQe316pZM-vdengemkL-3cHMO3Zo3t38bRVwLuDwCmL-4cjjpYh4PF1iUXUbfe_RP_CymHgIA</recordid><startdate>20040401</startdate><enddate>20040401</enddate><creator>Buchete, N-V</creator><creator>Straub, JE</creator><creator>Thirumalai, D</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></search><sort><creationdate>20040401</creationdate><title>Development of novel statistical potentials for protein fold recognition</title><author>Buchete, N-V ; Straub, JE ; Thirumalai, D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-5b335d0ccce4c5a9749796b6bbfe7164b144636bf01e787f5667b8661a3326473</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Algorithms</topic><topic>Computer Simulation</topic><topic>Crystallography, X-Ray</topic><topic>Models, Molecular</topic><topic>Protein Folding</topic><topic>Proteins - chemistry</topic><topic>Thermodynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Buchete, N-V</creatorcontrib><creatorcontrib>Straub, JE</creatorcontrib><creatorcontrib>Thirumalai, D</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><jtitle>Current opinion in structural biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Buchete, N-V</au><au>Straub, JE</au><au>Thirumalai, D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of novel statistical potentials for protein fold recognition</atitle><jtitle>Current opinion in structural biology</jtitle><addtitle>Curr Opin Struct Biol</addtitle><date>2004-04-01</date><risdate>2004</risdate><volume>14</volume><issue>2</issue><spage>225</spage><epage>232</epage><pages>225-232</pages><issn>0959-440X</issn><eissn>1879-033X</eissn><abstract>The need to perform large-scale studies of protein fold recognition, structure prediction and protein–protein interactions has led to novel developments of residue-level minimal models of proteins. 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subjects | Algorithms Computer Simulation Crystallography, X-Ray Models, Molecular Protein Folding Proteins - chemistry Thermodynamics |
title | Development of novel statistical potentials for protein fold recognition |
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