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Influence of protein structure databases on the predictive power of statistical pair potentials. Proteins: Struct Func Genet 1998;31:139--149 (1998)
| Venue: | Proteins: Structure, Function, and Genetics |
| Citations: | 15 - 1 self |
BibTeX
@INPROCEEDINGS{Furuichi98influenceof,
author = {Emiko Furuichi and Patrice Koehl},
title = {Influence of protein structure databases on the predictive power of statistical pair potentials. Proteins: Struct Func Genet 1998;31:139--149},
booktitle = {Proteins: Structure, Function, and Genetics},
year = {1998},
pages = {31--139}
}
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Abstract
ABSTRACT A long standing goal in protein structure studies is the development of reliable energy functions that can be used both to verify protein models derived from experimental constraints as well as for theoretical protein folding and inverse folding computer experiments. In that respect, knowledge-based statistical pair potentials have attracted considerable interests recently mainly because they include the essential features of protein structures as well as solvent effects at a low computing cost. However, the basis on which statistical potentials are derived have been questioned. In this paper, we investigate statistical pair potentials derived from protein threedimensional structures, addressing in particular questions related to the form of these potentials, as well as to the content of the database from which they are derived. We have shown that statistical pair potentials depend on the size of the proteins included in the database, and that this dependence can be reduced by considering only pairs of residue close in space (i.e., with a cutoff of 8 A ˚). We have shown also that statistical potentials carry a memory of the quality of the database in terms of the amount and diversity of secondary structure it contains. We find, for example, that potentials derived from a database containing �-proteins will only perform best on �-proteins in fold recognition computer experiments. We believe that this is an overall weakness of these potentials, which must be kept in mind when constructing a database. Proteins 31:139–149, 1998. � 1998 Wiley-Liss, Inc. Key words: protein structure; statistical potentials; protein structure database; assessing protein models
Keyphrases
protein structure statistical pair potential statistical potential struct func genet predictive power protein model theoretical protein folding considerable interest long standing goal protein structure study essential feature low computing cost solvent effect protein structure database inverse folding computer experiment overall weakness protein threedimensional structure experimental constraint secondary structure reliable energy function key word particular question knowledge-based statistical pair potential fold recognition computer experiment
