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An efficient algorithm for multistate protein design based on FASTER
Author(s) -
Allen Benjamin D.,
Mayo Stephen L.
Publication year - 2009
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.21375
Subject(s) - protein design , computer science , context (archaeology) , sequence (biology) , selection (genetic algorithm) , algorithm , monte carlo method , protein structure , mathematics , machine learning , chemistry , paleontology , biochemistry , statistics , biology
Most of the methods that have been developed for computational protein design involve the selection of side‐chain conformations in the context of a single, fixed main‐chain structure. In contrast, multistate design (MSD) methods allow sequence selection to be driven by the energetic contributions of multiple structural or chemical states simultaneously. This methodology is expected to be useful when the design target is an ensemble of related states rather than a single structure, or when a protein sequence must assume several distinct conformations to function. MSD can also be used with explicit negative design to suggest sequences with altered structural, binding, or catalytic specificity. We report implementation details of an efficient multistate design optimization algorithm based on FASTER (MSD‐FASTER). We subjected the algorithm to a battery of computational tests and found it to be generally applicable to various multistate design problems; designs with a large number of states and many designed positions are completely feasible. A direct comparison of MSD‐FASTER and multistate design Monte Carlo indicated that MSD‐FASTER discovers low‐energy sequences much more consistently. MSD‐FASTER likely performs better because amino acid substitutions are chosen on an energetic basis rather than randomly, and because multiple substitutions are applied together. Through its greater efficiency, MSD‐FASTER should allow protein designers to test experimentally better‐scoring sequences, and thus accelerate progress in the development of improved scoring functions and models for computational protein design. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010