A distance-dependent atomic knowledge-based potential for improved proteinstructure selection

Authors
Citation
H. Lu et J. Skolnick, A distance-dependent atomic knowledge-based potential for improved proteinstructure selection, PROTEINS, 44(3), 2001, pp. 223-232
Citations number
40
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Biochemistry & Biophysics
Journal title
PROTEINS-STRUCTURE FUNCTION AND GENETICS
ISSN journal
0887-3585 → ACNP
Volume
44
Issue
3
Year of publication
2001
Pages
223 - 232
Database
ISI
SICI code
0887-3585(20010815)44:3<223:ADAKPF>2.0.ZU;2-1
Abstract
A heavy atom distance-dependent knowledge-based pairwise potential has been developed. This statistical potential is first evaluated and optimized wit h the native structure z-scores from gapless threading. The potential is th en used to recognize the native and near-native structures from both publis hed decoy test sets, as well as decoys obtained from our group's protein st ructure prediction program. In the gapless threading test, there is an aver age z-score improvement of 4 units in the optimized atomic potential over t he residue-based quasichemical potential. Examination of the z-scores for i ndividual pairwise distance shells indicates that the specificity for the n ative protein structure is greatest at pairwise distances of 3.5-6.5 Angstr om, i.e., in the first solvation shell. On applying the current atomic pote ntial to test sets obtained from the web, composed of native protein and de coy structures, the current generation of the potential performs better tha n residue-based potentials as well as the other published atomic potentials in the task of selecting native and near-native structures. This newly dev eloped potential is also applied to structures of varying quality generated by our group's protein structure prediction program. The current atomic po tential tends to pick lower RMSD structures than do residue-based contact p otentials. In particular, this atomic pairwise interaction potential has be tter selectivity especially for near-native structures. As such, it can be used to select near-native folds generated by structure prediction algorith ms as well as for protein structure refinement. (C) 2001 Wiley-Liss, Inc.