Literature DB >> 15759641

Representing structure-function relationships in mechanistically diverse enzyme superfamilies.

Scott C H Pegg1, Shoshana Brown, Sunil Ojha, Conrad C Huang, Thomas E Ferrin, Patricia C Babbitt.   

Abstract

The prediction of protein function from structure or sequence data remains a problem best addressed by leveraging information available from previously determined structure-function relationships. In the case of enzymes, the study of mechanistically diverse superfamilies can provide a rich source of structure-function information useful in functional determination and enzyme engineering. To access these relationships using a computational resource, several issues must be addressed regarding the representation of enzyme function, the organization of structure-function relationships in the superfamily context, the handling of misannotations, and reliability of classifications and evidence. We discuss here our approaches to solving these problems in the development of a Structure-Function Linkage Database (SFLD) (online at http://sfld.rbvi.ucsf.edu).

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Year:  2005        PMID: 15759641

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  14 in total

1.  Structure-based function inference using protein family-specific fingerprints.

Authors:  Deepak Bandyopadhyay; Jun Huan; Jinze Liu; Jan Prins; Jack Snoeyink; Wei Wang; Alexander Tropsha
Journal:  Protein Sci       Date:  2006-06       Impact factor: 6.725

2.  Defining sequence space and reaction products within the cyanuric acid hydrolase (AtzD)/barbiturase protein family.

Authors:  Jennifer L Seffernick; Jasmine S Erickson; Stephan M Cameron; Seunghee Cho; Anthony G Dodge; Jack E Richman; Michael J Sadowsky; Lawrence P Wackett
Journal:  J Bacteriol       Date:  2012-06-22       Impact factor: 3.490

3.  Target selection and annotation for the structural genomics of the amidohydrolase and enolase superfamilies.

Authors:  Ursula Pieper; Ranyee Chiang; Jennifer J Seffernick; Shoshana D Brown; Margaret E Glasner; Libusha Kelly; Narayanan Eswar; J Michael Sauder; Jeffrey B Bonanno; Subramanyam Swaminathan; Stephen K Burley; Xiaojing Zheng; Mark R Chance; Steven C Almo; John A Gerlt; Frank M Raushel; Matthew P Jacobson; Patricia C Babbitt; Andrej Sali
Journal:  J Struct Funct Genomics       Date:  2009-02-14

4.  Quantitative comparison of catalytic mechanisms and overall reactions in convergently evolved enzymes: implications for classification of enzyme function.

Authors:  Daniel E Almonacid; Emmanuel R Yera; John B O Mitchell; Patricia C Babbitt
Journal:  PLoS Comput Biol       Date:  2010-03-12       Impact factor: 4.475

5.  Seq2Ref: a web server to facilitate functional interpretation.

Authors:  Wenlin Li; Qian Cong; Lisa N Kinch; Nick V Grishin
Journal:  BMC Bioinformatics       Date:  2013-01-28       Impact factor: 3.169

6.  Discovery of a dipeptide epimerase enzymatic function guided by homology modeling and virtual screening.

Authors:  Chakrapani Kalyanaraman; Heidi J Imker; Alexander A Fedorov; Elena V Fedorov; Margaret E Glasner; Patricia C Babbitt; Steven C Almo; John A Gerlt; Matthew P Jacobson
Journal:  Structure       Date:  2008-11-12       Impact factor: 5.006

7.  MACiE: a database of enzyme reaction mechanisms.

Authors:  Gemma L Holliday; Gail J Bartlett; Daniel E Almonacid; Noel M O'Boyle; Peter Murray-Rust; Janet M Thornton; John B O Mitchell
Journal:  Bioinformatics       Date:  2005-09-27       Impact factor: 6.937

8.  Exploring the evolution of novel enzyme functions within structurally defined protein superfamilies.

Authors:  Nicholas Furnham; Ian Sillitoe; Gemma L Holliday; Alison L Cuff; Roman A Laskowski; Christine A Orengo; Janet M Thornton
Journal:  PLoS Comput Biol       Date:  2012-03-01       Impact factor: 4.475

9.  Is EC class predictable from reaction mechanism?

Authors:  Neetika Nath; John B O Mitchell
Journal:  BMC Bioinformatics       Date:  2012-04-24       Impact factor: 3.169

10.  Rapid catalytic template searching as an enzyme function prediction procedure.

Authors:  Jerome P Nilmeier; Daniel A Kirshner; Sergio E Wong; Felice C Lightstone
Journal:  PLoS One       Date:  2013-05-10       Impact factor: 3.240

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