Design of protein-ligand binding based on the molecular-mechanics energy model

被引:37
作者
Boas, F. Edward [1 ]
Harbury, Pehr B. [1 ]
机构
[1] Stanford Univ, Sch Med, Dept Biochem, Stanford, CA 94305 USA
关键词
protein design; generalized Born; force field; dissociation constant; structure prediction;
D O I
10.1016/j.jmb.2008.04.001
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
While the molecular-mechanics field has standardized on a few potential energy functions, computational protein design efforts are based on potentials that are unique to individual laboratories. Here we show that a standard molecular-mechanics potential energy function without any modifications can be used to engineer protein-ligand binding. A molecular-mechanics potential is used to reconstruct the coordinates of various binding sites with an average root-mean-square error of 0.61 angstrom and to reproduce known ligand-induced side-chain conformational shifts. Within a series of 34 mutants, the calculation can always distinguish between weak (K-d > 1 mM) and tight (K-d < 10 mu M) binding sequences. Starting from partial coordinates of the ribose-binding protein lacking the ligand and the 10 primary contact residues, the molecular-mechanics potential is used to redesign a ribose-binding site. Out of a search space of 2 x 10(12) sequences, the calculation selects a point mutant of the native protein as the top solution (experimental K-d = 17 mu M) and the native protein as the second best solution (experimental K-d = 210 nM). The quality of the predictions depends on the accuracy of the generalized Born electrostatics model, treatment of protonation equilibria, high-resolution rotamer sampling, a final local energy minimization step, and explicit modeling of the bound, unbound, and unfolded states. The application of unmodified molecular-mechanics potentials to protein design links two fields in a mutually beneficial way. Design provides a new avenue for testing molecular-mechanics energy functions, and future improvements in these energy functions will presumably lead to more accurate design results. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码:415 / 424
页数:10
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