Literature DB >> 15229883

Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm.

Jeffrey Skolnick1, Daisuke Kihara, Yang Zhang.   

Abstract

This article describes the PROSPECTOR_3 threading algorithm, which combines various scoring functions designed to match structurally related target/template pairs. Each variant described was found to have a Z-score above which most identified templates have good structural (threading) alignments, Z(struct) (Z(good)). 'Easy' targets with accurate threading alignments are identified as single templates with Z > Z(good) or two templates, each with Z > Z(struct), having a good consensus structure in mutually aligned regions. 'Medium' targets have a pair of templates lacking a consensus structure, or a single template for which Z(struct) < Z < Z(good). PROSPECTOR_3 was applied to a comprehensive Protein Data Bank (PDB) benchmark composed of 1491 single domain proteins, 41-200 residues long and no more than 30% identical to any threading template. Of the proteins, 878 were found to be easy targets, with 761 having a root mean square deviation (RMSD) from native of less than 6.5 A. The average contact prediction accuracy was 46%, and on average 17.6 residue continuous fragments were predicted with RMSD values of 2.0 A. There were 606 medium targets identified, 87% (31%) of which had good structural (threading) alignments. On average, 9.1 residue, continuous fragments with RMSD of 2.5 A were predicted. Combining easy and medium sets, 63% (91%) of the targets had good threading (structural) alignments compared to native; the average target/template sequence identity was 22%. Only nine targets lacked matched templates. Moreover, PROSPECTOR_3 consistently outperforms PSIBLAST. Similar results were predicted for open reading frames (ORFS) < or =200 residues in the M. genitalium, E. coli and S. cerevisiae genomes. Thus, progress has been made in identification of weakly homologous/analogous proteins, with very high alignment coverage, both in a comprehensive PDB benchmark as well as in genomes. Copyright 2004 Wiley-Liss, Inc.

Entities:  

Mesh:

Substances:

Year:  2004        PMID: 15229883     DOI: 10.1002/prot.20106

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  75 in total

1.  Improving threading algorithms for remote homology modeling by combining fragment and template comparisons.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Proteins       Date:  2010-07

2.  Application of sparse NMR restraints to large-scale protein structure prediction.

Authors:  Wei Li; Yang Zhang; Jeffrey Skolnick
Journal:  Biophys J       Date:  2004-08       Impact factor: 4.033

3.  Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Biophys J       Date:  2004-10       Impact factor: 4.033

4.  TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets.

Authors:  Seung Yup Lee; Jeffrey Skolnick
Journal:  Biophys J       Date:  2010-11-03       Impact factor: 4.033

5.  Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.

Authors:  Hongyi Zhou; Yaoqi Zhou
Journal:  Proteins       Date:  2005-02-01

6.  The protein structure prediction problem could be solved using the current PDB library.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-14       Impact factor: 11.205

7.  The effect of long-range interactions on the secondary structure formation of proteins.

Authors:  Daisuke Kihara
Journal:  Protein Sci       Date:  2005-06-29       Impact factor: 6.725

8.  TASSER-Lite: an automated tool for protein comparative modeling.

Authors:  Shashi Bhushan Pandit; Yang Zhang; Jeffrey Skolnick
Journal:  Biophys J       Date:  2006-09-08       Impact factor: 4.033

9.  Sequence representation and prediction of protein secondary structure for structural motifs in twilight zone proteins.

Authors:  Lukasz Kurgan; Kanaka Durga Kedarisetti
Journal:  Protein J       Date:  2006-12       Impact factor: 2.371

10.  FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proteins       Date:  2010-12-06
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.