A Constraint Solver for Flexible Protein Model
This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geomet...
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Veröffentlicht in: | The Journal of artificial intelligence research 2013-12, Vol.48, p.953-1000 |
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creator | Campeotto, F. Dal Palù, A. Dovier, A. Fioretto, F. Pontelli, E. |
description | This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction.
The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems. |
doi_str_mv | 10.1613/jair.4193 |
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The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems.</description><identifier>ISSN: 1076-9757</identifier><identifier>EISSN: 1076-9757</identifier><identifier>EISSN: 1943-5037</identifier><identifier>DOI: 10.1613/jair.4193</identifier><language>eng</language><publisher>San Francisco: AI Access Foundation</publisher><subject>Amino acids ; Artificial intelligence ; Constraint modelling ; Empirical analysis ; Filtration ; Geometric constraints ; Multibody systems ; Proteins ; Solvers ; Structural analysis</subject><ispartof>The Journal of artificial intelligence research, 2013-12, Vol.48, p.953-1000</ispartof><rights>2013. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1027-7dffc5cf4e46d4a6e47c86872e36bf54d2952830010530fd264747a0467e6e873</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Campeotto, F.</creatorcontrib><creatorcontrib>Dal Palù, A.</creatorcontrib><creatorcontrib>Dovier, A.</creatorcontrib><creatorcontrib>Fioretto, F.</creatorcontrib><creatorcontrib>Pontelli, E.</creatorcontrib><title>A Constraint Solver for Flexible Protein Model</title><title>The Journal of artificial intelligence research</title><description>This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. Each multi-body is a system composed of body elements, connected by joint relationships and constrained by geometric properties. The emphasis of this investigation is the use of multi-body systems to model native conformations of protein structures---where each body represents an entity of the protein (e.g., an amino acid, a small peptide) and the geometric constraints are related to the spatial properties of the composing atoms. The paper explores the use of the proposed class of constraints to support a variety of different structural analysis of proteins, such as loop modeling and structure prediction.
The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems.</description><subject>Amino acids</subject><subject>Artificial intelligence</subject><subject>Constraint modelling</subject><subject>Empirical analysis</subject><subject>Filtration</subject><subject>Geometric constraints</subject><subject>Multibody systems</subject><subject>Proteins</subject><subject>Solvers</subject><subject>Structural analysis</subject><issn>1076-9757</issn><issn>1076-9757</issn><issn>1943-5037</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpNkE1Lw0AQhhdRsFYP_oMFTx4S93uTYwm2ChUF9bxsk1lIiNk6m4r-exPqwdPM4eF9Zx5CrjnLueHyrvMt5oqX8oQsOLMmK622p__2c3KRUscYL5UoFiRf0SoOaUTfDiN9jf0XIA0R6bqH73bXA33BOEI70KfYQH9JzoLvE1z9zSV5X9-_VQ_Z9nnzWK22Wc2ZsJltQqh1HRQo0yhvQNm6MIUVIM0uaNWIUotCTlcwLVlohFFWWc-UsWCgsHJJbo65e4yfB0ij6-IBh6nSCa0VZ3x6aKJuj1SNMSWE4PbYfnj8cZy5WYebdbhZh_wFV41QNw</recordid><startdate>20131230</startdate><enddate>20131230</enddate><creator>Campeotto, F.</creator><creator>Dal Palù, A.</creator><creator>Dovier, A.</creator><creator>Fioretto, F.</creator><creator>Pontelli, E.</creator><general>AI Access Foundation</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20131230</creationdate><title>A Constraint Solver for Flexible Protein Model</title><author>Campeotto, F. ; Dal Palù, A. ; Dovier, A. ; Fioretto, F. ; Pontelli, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1027-7dffc5cf4e46d4a6e47c86872e36bf54d2952830010530fd264747a0467e6e873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Amino acids</topic><topic>Artificial intelligence</topic><topic>Constraint modelling</topic><topic>Empirical analysis</topic><topic>Filtration</topic><topic>Geometric constraints</topic><topic>Multibody systems</topic><topic>Proteins</topic><topic>Solvers</topic><topic>Structural analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Campeotto, F.</creatorcontrib><creatorcontrib>Dal Palù, A.</creatorcontrib><creatorcontrib>Dovier, A.</creatorcontrib><creatorcontrib>Fioretto, F.</creatorcontrib><creatorcontrib>Pontelli, E.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>The Journal of artificial intelligence research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Campeotto, F.</au><au>Dal Palù, A.</au><au>Dovier, A.</au><au>Fioretto, F.</au><au>Pontelli, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Constraint Solver for Flexible Protein Model</atitle><jtitle>The Journal of artificial intelligence research</jtitle><date>2013-12-30</date><risdate>2013</risdate><volume>48</volume><spage>953</spage><epage>1000</epage><pages>953-1000</pages><issn>1076-9757</issn><eissn>1076-9757</eissn><eissn>1943-5037</eissn><abstract>This paper proposes the formalization and implementation of a novel class of constraints aimed at modeling problems related to placement of multi-body systems in the 3-dimensional space. 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The declarative nature of a constraint-based encoding provides elaboration tolerance and the ability to make use of any additional knowledge in the analysis studies. The filtering capabilities of the proposed constraints also allow to control the number of representative solutions that are withdrawn from the conformational space of the protein, by means of criteria driven by uniform distribution sampling principles. In this scenario it is possible to select the desired degree of precision and/or number of solutions. The filtering component automatically excludes configurations that violate the spatial and geometric properties of the composing multi-body system. The paper illustrates the implementation of a constraint solver based on the multi-body perspective and its empirical evaluation on protein structure analysis problems.</abstract><cop>San Francisco</cop><pub>AI Access Foundation</pub><doi>10.1613/jair.4193</doi><tpages>48</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Amino acids Artificial intelligence Constraint modelling Empirical analysis Filtration Geometric constraints Multibody systems Proteins Solvers Structural analysis |
title | A Constraint Solver for Flexible Protein Model |
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