A distance‐dependent atomic knowledge‐based potential for improved protein structure selection

Hui Lu; Jeffrey Skolnick

doi:10.1002/prot.1087

journal article Jun 21, 2001

A distance‐dependent atomic knowledge‐based potential for improved protein structure selection

Hui Lu

Jeffrey Skolnick

Proteins: Structure, Function, and Bioinformatics Vol. 44 No. 3 pp. 223-232 · Wiley

View at Publisher Save 10.1002/prot.1087

Abstract

AbstractA heavy atom distance‐dependent knowledge‐based pairwise potential has been developed. This statistical potential is first evaluated and optimized with the native structure z‐scores from gapless threading. The potential is then used to recognize the native and near‐native structures from both published decoy test sets, as well as decoys obtained from our group's protein structure prediction program. In the gapless threading test, there is an average z‐score improvement of 4 units in the optimized atomic potential over the residue‐based quasichemical potential. Examination of the z‐scores for individual pairwise distance shells indicates that the specificity for the native protein structure is greatest at pairwise distances of 3.5–6.5 Å, i.e., in the first solvation shell. On applying the current atomic potential to test sets obtained from the web, composed of native protein and decoy structures, the current generation of the potential performs better than residue‐based potentials as well as the other published atomic potentials in the task of selecting native and near‐native structures. This newly developed potential is also applied to structures of varying quality generated by our group's protein structure prediction program. The current atomic potential tends to pick lower RMSD structures than do residue‐based contact potentials. In particular, this atomic pairwise interaction potential has better selectivity especially for near‐native structures. As such, it can be used to select near‐native folds generated by structure prediction algorithms as well as for protein structure refinement. Proteins 2001;44:223–232. © 2001 Wiley‐Liss, Inc.

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References

40

[1]

10.1016/s0959-440x(97)80025-5

[2]

10.1016/s0959-440x(97)80029-2

[3]

How to Derive a Protein Folding Potential? A New Approach to an Old Problem

Leonid A. Mirny, Eugene I. Shakhnovich

Journal of Molecular Biology 10.1006/jmbi.1996.0704

[4]

10.1016/s0959-440x(99)80026-8

[5]

An empirical energy potential with a reference state for protein fold and sequence recognition

Sanzo Miyazawa, Robert L. Jernigan

Proteins: Structure, Function, and Bioinformatics 10.1002/(sici)1097-0134(19990815)36:3<357::aid-prot10>3.0.co;2-u

[6]

Effective energy functions for protein structure prediction

T Lazaridis

Current Opinion in Structural Biology 10.1016/s0959-440x(00)00063-4

[7]

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

Bernard R. Brooks, Robert E. Bruccoleri, Barry D. Olafson et al.

Journal of Computational Chemistry 10.1002/jcc.540040211

[8]

Cornell W "A second generation force field for the simulation of proteins, nucleic acids and organic molecules" Biochemistry (1995)

[9]

Structure-derived potentials and protein simulations

Robert L Jernigan, Ivet Bahar

Current Opinion in Structural Biology 10.1016/s0959-440x(96)80075-3

[10]

10.1002/pro.5560050607

[11]

10.1006/jmbi.1996.0175

[12]

Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes

Marcos R. Betancourt, D. Thirumalai

Protein Science 10.1110/ps.8.2.361

[13]

10.1002/(sici)1097-0134(20000101)38:1<3::aid-prot2>3.0.co;2-s

[14]

10.1002/1097-0134(20001001)41:1<40::aid-prot70>3.0.co;2-u

[15]

Pathways to a Protein Folding Intermediate Observed in a 1-Microsecond Simulation in Aqueous Solution

Yong Duan, Peter A. Kollman

Science 10.1126/science.282.5389.740

[16]

A new approach to protein fold recognition

David T. Jones, W. R. Taylort, J. M. Thornton

Nature 10.1038/358086a0

[17]

A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure

James U. Bowie, Roland Lüthy, David Eisenberg

Science 10.1126/science.1853201

[18]

Topology fingerprint approach to the inverse protein folding problem

Adam Godzik, Andrzej Kolinski, Jeffrey Skolnick

Journal of Molecular Biology 10.1016/0022-2836(92)90693-e

[19]

10.1002/prot.340160110

[20]

Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functions

Kim T. Simons, Charles Kooperberg, Enoch S. Huang et al.

Journal of Molecular Biology 10.1006/jmbi.1997.0959

[21]

MONSSTER: a method for folding globular proteins with a small number of distance restraints

Jeffrey Skolnick, Andrzej Kolinski, Angel R. Ortiz

Journal of Molecular Biology 10.1006/jmbi.1996.0720

[22]

10.1002/prot.340140310

[23]

10.1002/pro.5560020508

[24]

10.1021/jp973371j

[25]

10.1093/protein/9.8.637

[26]

10.1016/s1359-0278(96)00042-9

[27]

10.1006/jmbi.1997.1479

[28]

10.1006/jmbi.1996.0868

[29]

10.1006/jmbi.1995.0195

[30]

Accurate reconstruction of all-atom protein representations from side-chain-based low-resolution models

Michael Feig, Piotr Rotkiewicz, Andrzej Kolinski et al.

Proteins: Structure, Function, and Bioinformatics 10.1002/1097-0134(20001001)41:1<86::aid-prot110>3.0.co;2-y

[31]

10.1006/jmbi.1996.0256

[32]

Generalized comparative modeling (GENECOMP): A combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement

A. Kolinski, M.R. Betancourt, D. Kihara et al.

Proteins: Structure, Function, and Bioinformatics 10.1002/prot.1080

[33]

Fischer D "Assessing the performance of fold recognition methods by means of a comprehensive benchmark" Pacific Symp Biocomput (1996)

[34]

10.1002/pro.5560010313

[35]

10.1002/(sici)1097-0134(19991201)37:4<592::aid-prot10>3.0.co;2-2

[36]

Defrosting the frozen approximation: PROSPECTOR? A new approach to threading

Jeffrey Skolnick, Daisuke Kihara

Proteins: Structure, Function, and Bioinformatics 10.1002/1097-0134(20010215)42:3<319::aid-prot30>3.0.co;2-a

[37]

Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins

Kim T. Simons, Ingo Ruczinski, Charles Kooperberg et al.

Proteins: Structure, Function, and Bioinformatics 10.1002/(sici)1097-0134(19990101)34:1<82::aid-prot7>3.0.co;2-a

[38]

10.1103/physrevlett.80.4823

[39]

10.1002/(sici)1097-0134(19990515)35:3<353::aid-prot9>3.0.co;2-e

[40]

10.1002/(sici)1097-0134(19990601)35:4<447::aid-prot8>3.0.co;2-o

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References

Details

Published: Jun 21, 2001
Vol/Issue: 44(3)
Pages: 223-232
License: View

Authors

H

Hui Lu

China University of Petroleum, East China

J

Jeffrey Skolnick

Georgia Institute of Technology

Cite This Article

Hui Lu, Jeffrey Skolnick (2001). A distance‐dependent atomic knowledge‐based potential for improved protein structure selection. Proteins: Structure, Function, and Bioinformatics, 44(3), 223-232. https://doi.org/10.1002/prot.1087

A distance‐dependent atomic knowledge‐based potential for improved protein structure selection

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