Evolution-Based Functional Decomposition of Proteins
The essential biological properties of proteins-folding, biochemical activities, and the capacity to adapt-arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid...
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| Veröffentlicht in: | PLoS computational biology 2016-06, Vol.12 (6), p.e1004817-e1004817 |
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| creator | Rivoire, Olivier Reynolds, Kimberly A Ranganathan, Rama |
| description | The essential biological properties of proteins-folding, biochemical activities, and the capacity to adapt-arise from the global pattern of interactions between amino acid residues. The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment-a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation. |
| doi_str_mv | 10.1371/journal.pcbi.1004817 |
| format | Article |
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The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment-a model for how sector properties might be differentially tuned in members of a protein family. 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This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Rivoire O, Reynolds KA, Ranganathan R (2016) Evolution-Based Functional Decomposition of Proteins. PLoS Comput Biol 12(6): e1004817. doi:10.1371/journal.pcbi.1004817</rights><rights>2016 Rivoire et al 2016 Rivoire et al</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Rivoire O, Reynolds KA, Ranganathan R (2016) Evolution-Based Functional Decomposition of Proteins. 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The statistical coupling analysis (SCA) is an approach to defining this pattern that involves the study of amino acid coevolution in an ensemble of sequences comprising a protein family. This approach indicates a functional architecture within proteins in which the basic units are coupled networks of amino acids termed sectors. This evolution-based decomposition has potential for new understandings of the structural basis for protein function. To facilitate its usage, we present here the principles and practice of the SCA and introduce new methods for sector analysis in a python-based software package (pySCA). We show that the pattern of amino acid interactions within sectors is linked to the divergence of functional lineages in a multiple sequence alignment-a model for how sector properties might be differentially tuned in members of a protein family. This work provides new tools for studying proteins and for generally testing the concept of sectors as the principal units of function and adaptive variation.</description><subject>Algorithms</subject><subject>Amino acid sequencing</subject><subject>Amino acids</subject><subject>Binding Sites</subject><subject>Biology and Life Sciences</subject><subject>Computer Simulation</subject><subject>Decomposition</subject><subject>Evolution</subject><subject>Evolution, Molecular</subject><subject>Experiments</subject><subject>GTP-Binding Proteins - chemical synthesis</subject><subject>GTP-Binding Proteins - chemistry</subject><subject>GTP-Binding Proteins - ultrastructure</subject><subject>Methods</subject><subject>Models, Chemical</subject><subject>Molecular Docking Simulation - methods</subject><subject>Observations</subject><subject>Phylogenetics</subject><subject>Physical Sciences</subject><subject>Protein Binding</subject><subject>Protein folding</subject><subject>Research and Analysis Methods</subject><subject>Sequence Alignment - methods</subject><subject>Sequence Analysis, Protein - methods</subject><subject>Software</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqVkktv1DAQxyMEog_4BghW4gKHLHb8vlQqpYWVKkA8zpYzdhavknixk6p8e5xuWnURB5APnhn_Zvyf0RTFM4yWmAj8ZhPG2Jt2uYXaLzFCVGLxoDjEjJFSECYf3rMPiqOUNghlU_HHxUElKkY5l4cFPb8K7Tj40JdvTXJ2cTH2MLmmXbxzELptSH7yF6FZfI5hcL5PT4pHjWmTezrfx8X3i_NvZx_Ky0_vV2enlyVwzocSBLGcV0RagWwNwDhtsKFIKAtgec2QwMhhWTOLHPCagFBYcuqIkHVTc3JcvNjV3bYh6bnhpLFEWb2SjGRitSNsMBu9jb4z8ZcOxuubQIhrbeLgoXVaAK1ITYl0ilNGnQSGDQcmKWp4o2iudTL_Ntads-D6IZp2r-j-S-9_6HW40lQqJPkk99VcIIafo0uD7nwC17amd2G80S15HoL6B1QoohBnuMroyz_Qvw9iuaPWJvfq-yZkiZCPdZ2H0LvG5_gpFYqhSqJJweu9hMwM7npYmzElvfr65T_Yj_ss3bEQQ0rRNXcDxEhPi3srX0-Lq-fFzWnP7w__Lul2U8lvx9ToFA</recordid><startdate>20160601</startdate><enddate>20160601</enddate><creator>Rivoire, Olivier</creator><creator>Reynolds, Kimberly A</creator><creator>Ranganathan, Rama</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20160601</creationdate><title>Evolution-Based Functional Decomposition of Proteins</title><author>Rivoire, Olivier ; Reynolds, Kimberly A ; Ranganathan, Rama</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c666t-c73d66238d70dbcc564f1a4079dccd6b50710e18b5d0ec6b3c791864e378bfb63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Amino acid sequencing</topic><topic>Amino acids</topic><topic>Binding Sites</topic><topic>Biology and Life Sciences</topic><topic>Computer Simulation</topic><topic>Decomposition</topic><topic>Evolution</topic><topic>Evolution, Molecular</topic><topic>Experiments</topic><topic>GTP-Binding Proteins - chemical synthesis</topic><topic>GTP-Binding Proteins - chemistry</topic><topic>GTP-Binding Proteins - ultrastructure</topic><topic>Methods</topic><topic>Models, Chemical</topic><topic>Molecular Docking Simulation - methods</topic><topic>Observations</topic><topic>Phylogenetics</topic><topic>Physical Sciences</topic><topic>Protein Binding</topic><topic>Protein folding</topic><topic>Research and Analysis Methods</topic><topic>Sequence Alignment - methods</topic><topic>Sequence Analysis, Protein - methods</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rivoire, Olivier</creatorcontrib><creatorcontrib>Reynolds, Kimberly A</creatorcontrib><creatorcontrib>Ranganathan, Rama</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</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><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - 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| subjects | Algorithms Amino acid sequencing Amino acids Binding Sites Biology and Life Sciences Computer Simulation Decomposition Evolution Evolution, Molecular Experiments GTP-Binding Proteins - chemical synthesis GTP-Binding Proteins - chemistry GTP-Binding Proteins - ultrastructure Methods Models, Chemical Molecular Docking Simulation - methods Observations Phylogenetics Physical Sciences Protein Binding Protein folding Research and Analysis Methods Sequence Alignment - methods Sequence Analysis, Protein - methods Software |
| title | Evolution-Based Functional Decomposition of Proteins |
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