Protein structure determination by exhaustive search of Protein Data Bank derived databases.
Identifieur interne : 000059 ( PubMed/Checkpoint ); précédent : 000058; suivant : 000060Protein structure determination by exhaustive search of Protein Data Bank derived databases.
Auteurs : Ian Stokes-Rees [États-Unis] ; Piotr SlizSource :
- Proceedings of the National Academy of Sciences of the United States of America [ 1091-6490 ] ; 2010.
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Proteins.
- chemical , genetics : Proteins.
- methods : Computational Biology, Sequence Alignment.
- Amino Acid Sequence, Animals, Databases, Protein, Mice, Models, Molecular, Molecular Sequence Data, Protein Conformation, Sequence Homology, Amino Acid.
Abstract
Parallel sequence and structure alignment tools have become ubiquitous and invaluable at all levels in the study of biological systems. We demonstrate the application and utility of this same parallel search paradigm to the process of protein structure determination, benefitting from the large and growing corpus of known structures. Such searches were previously computationally intractable. Through the method of Wide Search Molecular Replacement, developed here, they can be completed in a few hours with the aide of national-scale federated cyberinfrastructure. By dramatically expanding the range of models considered for structure determination, we show that small (less than 12% structural coverage) and low sequence identity (less than 20% identity) template structures can be identified through multidimensional template scoring metrics and used for structure determination. Many new macromolecular complexes can benefit significantly from such a technique due to the lack of known homologous protein folds or sequences. We demonstrate the effectiveness of the method by determining the structure of a full-length p97 homologue from Trichoplusia ni. Example cases with the MHC/T-cell receptor complex and the EmoB protein provide systematic estimates of minimum sequence identity, structure coverage, and structural similarity required for this method to succeed. We describe how this structure-search approach and other novel computationally intensive workflows are made tractable through integration with the US national computational cyberinfrastructure, allowing, for example, rapid processing of the entire Structural Classification of Proteins protein fragment database.
DOI: 10.1073/pnas.1012095107
PubMed: 21098306
Affiliations:
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We demonstrate the application and utility of this same parallel search paradigm to the process of protein structure determination, benefitting from the large and growing corpus of known structures. Such searches were previously computationally intractable. Through the method of Wide Search Molecular Replacement, developed here, they can be completed in a few hours with the aide of national-scale federated cyberinfrastructure. By dramatically expanding the range of models considered for structure determination, we show that small (less than 12% structural coverage) and low sequence identity (less than 20% identity) template structures can be identified through multidimensional template scoring metrics and used for structure determination. Many new macromolecular complexes can benefit significantly from such a technique due to the lack of known homologous protein folds or sequences. We demonstrate the effectiveness of the method by determining the structure of a full-length p97 homologue from Trichoplusia ni. Example cases with the MHC/T-cell receptor complex and the EmoB protein provide systematic estimates of minimum sequence identity, structure coverage, and structural similarity required for this method to succeed. We describe how this structure-search approach and other novel computationally intensive workflows are made tractable through integration with the US national computational cyberinfrastructure, allowing, for example, rapid processing of the entire Structural Classification of Proteins protein fragment database.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">21098306</PMID><DateCreated><Year>2011</Year><Month>03</Month><Day>22</Day></DateCreated><DateCompleted><Year>2011</Year><Month>05</Month><Day>12</Day></DateCompleted><DateRevised><Year>2015</Year><Month>02</Month><Day>05</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1091-6490</ISSN><JournalIssue CitedMedium="Internet"><Volume>107</Volume><Issue>50</Issue><PubDate><Year>2010</Year><Month>Dec</Month><Day>14</Day></PubDate></JournalIssue><Title>Proceedings of the National Academy of Sciences of the United States of America</Title><ISOAbbreviation>Proc. 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By dramatically expanding the range of models considered for structure determination, we show that small (less than 12% structural coverage) and low sequence identity (less than 20% identity) template structures can be identified through multidimensional template scoring metrics and used for structure determination. Many new macromolecular complexes can benefit significantly from such a technique due to the lack of known homologous protein folds or sequences. We demonstrate the effectiveness of the method by determining the structure of a full-length p97 homologue from Trichoplusia ni. Example cases with the MHC/T-cell receptor complex and the EmoB protein provide systematic estimates of minimum sequence identity, structure coverage, and structural similarity required for this method to succeed. We describe how this structure-search approach and other novel computationally intensive workflows are made tractable through integration with the US national computational cyberinfrastructure, allowing, for example, rapid processing of the entire Structural Classification of Proteins protein fragment database.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Stokes-Rees</LastName><ForeName>Ian</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sliz</LastName><ForeName>Piotr</ForeName><Initials>P</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>P01 GM062580</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType 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Version="1">20376006</PMID></CommentsCorrections></CommentsCorrectionsList><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000595">Amino Acid Sequence</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D019295">Computational Biology</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000379">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D030562">Databases, Protein</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D051379">Mice</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008958">Models, Molecular</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008969">Molecular Sequence Data</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D011487">Protein 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