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Published April 15, 2010 | public
Journal Article

An efficient algorithm for multistate protein design based on FASTER


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.

Additional Information

© 2009 Wiley Periodicals, Inc. Received 10 October 2006; Revised 27 April 2009; Accepted 7 June 2009. Published online 27 July 2009. Contract/grant sponsors: Howard Hughes Medical Institute, Ralph M. Parsons Foundation, IBM Shared University Research Grant, Defense Advanced Research Projects Agency. The authors thank Kyle Lassila, Christina Vizcarra, Jennifer Keeffe, and an anonymous reviewer for their insightful comments.

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