Division of Chemistry and Chemical Engineering, California Institute of Technology, MC 114-96, 1200 E. California Blvd., Pasadena, California 91125, USA.
J Comput Chem. 2010 Apr 15;31(5):904-16. doi: 10.1002/jcc.21375.
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.
大多数用于计算蛋白质设计的方法都涉及在单个固定主链结构的背景下选择侧链构象。相比之下，多态设计（MSD）方法允许通过多个结构或化学状态的能量贡献同时驱动序列选择。当设计目标是相关状态的集合而不是单个结构时，或者当蛋白质序列必须采用几种不同的构象才能发挥功能时，这种方法预计会很有用。MSD 还可以与明确的负设计一起使用，以建议具有改变的结构、结合或催化特异性的序列。我们报告了基于 FASTER（MSD-FASTER）的高效多态设计优化算法的实现细节。我们对该算法进行了一系列计算测试，发现它通常适用于各种多态设计问题；具有大量状态和许多设计位置的设计是完全可行的。MSD-FASTER 和多态设计蒙特卡罗的直接比较表明，MSD-FASTER 更一致地发现低能量序列。MSD-FASTER 可能表现更好，因为氨基酸取代是基于能量而不是随机选择的，并且同时应用多个取代。通过提高效率，MSD-FASTER 应该允许蛋白质设计人员更有效地测试得分更高的序列，从而加速改进评分函数和计算蛋白质设计模型的开发。
