Literature DB >> 14962924

A perturbation-based method for calculating explicit likelihood of evolutionary co-variance in multiple sequence alignments.

John P Dekker1, Anthony Fodor, Richard W Aldrich, Gary Yellen.   

Abstract

MOTIVATION: The constituent amino acids of a protein work together to define its structure and to facilitate its function. Their interdependence should be apparent in the evolutionary record of each protein family: positions in the sequence of a protein family that are intimately associated in space or in function should co-vary in evolution. A recent approach by Ranganathan and colleagues proposes to look at subsets of a protein family, selected for their sequence at one position, to see how this affects variation at other positions.
RESULTS: We present a quantitative algorithm for assessing covariation with this approach, based on explicit likelihood calculations. By applying our algorithm to 138 Pfam families with at least one member of known structure, we demonstrate that our method has improved power in finding physically close residues in crystal structures, compared to that of Ranganathan and colleagues. SUPPLEMENTARY INFORMATION: www.afodor.net/bioinfosup.html

Mesh:

Year:  2004        PMID: 14962924     DOI: 10.1093/bioinformatics/bth128

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  57 in total

1.  Engagement of arginine finger to ATP triggers large conformational changes in NtrC1 AAA+ ATPase for remodeling bacterial RNA polymerase.

Authors:  Baoyu Chen; Tatyana A Sysoeva; Saikat Chowdhury; Liang Guo; Sacha De Carlo; Jeffrey A Hanson; Haw Yang; B Tracy Nixon
Journal:  Structure       Date:  2010-11-10       Impact factor: 5.006

Review 2.  Bile salt hydrolases: Structure and function, substrate preference, and inhibitor development.

Authors:  Zixing Dong; Byong H Lee
Journal:  Protein Sci       Date:  2018-09-24       Impact factor: 6.725

3.  Requirements for efficient correction of ΔF508 CFTR revealed by analyses of evolved sequences.

Authors:  Juan L Mendoza; André Schmidt; Qin Li; Emmanuel Nuvaga; Tyler Barrett; Robert J Bridges; Andrew P Feranchak; Chad A Brautigam; Philip J Thomas
Journal:  Cell       Date:  2012-01-20       Impact factor: 41.582

Review 4.  Using Evolution to Guide Protein Engineering: The Devil IS in the Details.

Authors:  Liskin Swint-Kruse
Journal:  Biophys J       Date:  2016-07-12       Impact factor: 4.033

5.  Validation of coevolving residue algorithms via pipeline sensitivity analysis: ELSC and OMES and ZNMI, oh my!

Authors:  Christopher A Brown; Kevin S Brown
Journal:  PLoS One       Date:  2010-06-01       Impact factor: 3.240

6.  Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores.

Authors:  Daniel J Parente; J Christian J Ray; Liskin Swint-Kruse
Journal:  Proteins       Date:  2015-11-17

7.  Identification of biochemically neutral positions in liver pyruvate kinase.

Authors:  Tyler A Martin; Tiffany Wu; Qingling Tang; Larissa L Dougherty; Daniel J Parente; Liskin Swint-Kruse; Aron W Fenton
Journal:  Proteins       Date:  2020-06-27

8.  Integration of evolutionary features for the identification of functionally important residues in major facilitator superfamily transporters.

Authors:  Jouhyun Jeon; Jae-Seong Yang; Sanguk Kim
Journal:  PLoS Comput Biol       Date:  2009-10-02       Impact factor: 4.475

9.  Predicting helix-helix interactions from residue contacts in membrane proteins.

Authors:  Allan Lo; Yi-Yuan Chiu; Einar Andreas Rødland; Ping-Chiang Lyu; Ting-Yi Sung; Wen-Lian Hsu
Journal:  Bioinformatics       Date:  2009-02-25       Impact factor: 6.937

10.  Interrogating and predicting tolerated sequence diversity in protein folds: application to E. elaterium trypsin inhibitor-II cystine-knot miniprotein.

Authors:  Jennifer L Lahti; Adam P Silverman; Jennifer R Cochran
Journal:  PLoS Comput Biol       Date:  2009-09-04       Impact factor: 4.475

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