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Protein Science (2007), 16:2030-2041. Published by Cold Spring Harbor Laboratory Press. Copyright © 2007 The Protein Society
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Modeling mutations in protein structures

Eric Feyfant1, Andrej Sali2, and András Fiser3,4

1 Wyeth Research, Chemical and Screening Sciences, Cambridge, Massachusetts 02421, USA
2 Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, and California Institute for Quantitative Biomedical Research, University of California at San Francisco, San Francisco, California 94158, USA
3 Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York 10461, USA
4 Institute of Enzymology and Alfred Renyi Institute of Mathematics, Hungarian Academy of Sciences, H-1113 Budapest, Hungary

(RECEIVED March 5, 2007; FINAL REVISION June 19, 2007; ACCEPTED June 19, 2007)

We describe an automated method for the modeling of point mutations in protein structures. The protein is represented by all non-hydrogen atoms. The scoring function consists of several types of physical potential energy terms and homology-derived restraints. The optimization method implements a combination of conjugate gradient minimization and molecular dynamics with simulated annealing. The testing set consists of 717 pairs of known protein structures differing by a single mutation. Twelve variations of the scoring function were tested in three different environments of the mutated residue. The best-performing protocol optimizes all the atoms of the mutated residue, with respect to a scoring function that includes molecular mechanics energy terms for bond distances, angles, dihedral angles, peptide bond planarity, and non-bonded atomic contacts represented by Lennard-Jones potential, dihedral angle restraints derived from the aligned homologous structure, and a statistical potential for non-bonded atomic interactions extracted from a large set of known protein structures. The current method compares favorably with other tested approaches, especially when predicting long and flexible side-chains. In addition to the thoroughness of the conformational search, sampled degrees of freedom, and the scoring function type, the accuracy of the method was also evaluated as a function of the flexibility of the mutated side-chain, the relative volume change of the mutated residue, and its residue type. The results suggest that further improvement is likely to be achieved by concentrating on the improvement of the scoring function, in addition to or instead of increasing the variety of sampled conformations.

Keywords: point mutation; protein structure; comparative modeling


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