Literature DB >> 11866536

SnapDRAGON: a method to delineate protein structural domains from sequence data.

Richard A George1, Jaap Heringa.   

Abstract

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation. Copyright 2002 Elsevier Science Ltd.

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Year:  2002        PMID: 11866536     DOI: 10.1006/jmbi.2001.5387

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  36 in total

1.  Characteristics and prediction of domain linker sequences in multi-domain proteins.

Authors:  Takanori Tanaka; Yutaka Kuroda; Shigeyuki Yokoyama
Journal:  J Struct Funct Genomics       Date:  2003

2.  Rapid protein domain assignment from amino acid sequence using predicted secondary structure.

Authors:  Russell L Marsden; Liam J McGuffin; David T Jones
Journal:  Protein Sci       Date:  2002-12       Impact factor: 6.725

3.  Sequence-based prediction of protein domains.

Authors:  Jinfeng Liu; Burkhard Rost
Journal:  Nucleic Acids Res       Date:  2004-07-07       Impact factor: 16.971

4.  Bayesian data mining of protein domains gives an efficient predictive algorithm and new insight.

Authors:  Rajani R Joshi; Vivekanand V Samant
Journal:  J Mol Model       Date:  2006-10-07       Impact factor: 1.810

5.  Prediction of protein domain boundaries from sequence alone.

Authors:  Oxana V Galzitskaya; Bogdan S Melnik
Journal:  Protein Sci       Date:  2003-04       Impact factor: 6.725

6.  A topological algorithm for identification of structural domains of proteins.

Authors:  Frank Emmert-Streib; Arcady Mushegian
Journal:  BMC Bioinformatics       Date:  2007-07-03       Impact factor: 3.169

7.  HangOut: generating clean PSI-BLAST profiles for domains with long insertions.

Authors:  Bong-Hyun Kim; Qian Cong; Nick V Grishin
Journal:  Bioinformatics       Date:  2010-04-22       Impact factor: 6.937

Review 8.  Molecular physiology of SPAK and OSR1: two Ste20-related protein kinases regulating ion transport.

Authors:  Kenneth B Gagnon; Eric Delpire
Journal:  Physiol Rev       Date:  2012-10       Impact factor: 37.312

9.  A modular kernel approach for integrative analysis of protein domain boundaries.

Authors:  Paul D Yoo; Bing Bing Zhou; Albert Y Zomaya
Journal:  BMC Genomics       Date:  2009-12-03       Impact factor: 3.969

10.  Ab initio and homology based prediction of protein domains by recursive neural networks.

Authors:  Ian Walsh; Alberto J M Martin; Catherine Mooney; Enrico Rubagotti; Alessandro Vullo; Gianluca Pollastri
Journal:  BMC Bioinformatics       Date:  2009-06-26       Impact factor: 3.169

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