Davey James A, Chica Roberto A
Department of Chemistry and Biomolecular Sciences, University of Ottawa, 10 Marie-Curie, Ottawa, ON, Canada, K1N 6N5.
Methods Mol Biol. 2017;1529:161-179. doi: 10.1007/978-1-4939-6637-0_7.
The ability of computational protein design (CPD) to identify protein sequences possessing desired characteristics in vast sequence spaces makes it a highly valuable tool in the protein engineering toolbox. CPD calculations are typically performed using a single-state design (SSD) approach in which amino-acid sequences are optimized on a single protein structure. Although SSD has been successfully applied to the design of numerous protein functions and folds, the approach can lead to the incorrect rejection of desirable sequences because of the combined use of a fixed protein backbone template and a set of rigid rotamers. This fixed backbone approximation can be addressed by using multistate design (MSD) with backbone ensembles. MSD improves the quality of predicted sequences by using ensembles approximating conformational flexibility as input templates instead of a single fixed protein structure. In this chapter, we present a step-by-step guide to the implementation and analysis of MSD calculations with backbone ensembles. Specifically, we describe ensemble generation with the PertMin protocol, execution of MSD calculations for recapitulation of Streptococcal protein G domain β1 mutant stability, and analysis of computational predictions by sequence binning. Furthermore, we provide a comparison between MSD and SSD calculation results and discuss the benefits of multistate approaches to CPD.
计算蛋白质设计(CPD)能够在庞大的序列空间中识别具有所需特性的蛋白质序列,这使其成为蛋白质工程工具箱中极具价值的工具。CPD计算通常使用单状态设计(SSD)方法进行,即在单一蛋白质结构上优化氨基酸序列。尽管SSD已成功应用于众多蛋白质功能和折叠的设计,但由于固定蛋白质主链模板和一组刚性旋转异构体的联合使用,该方法可能导致对理想序列的错误排除。这种固定主链近似可以通过使用具有主链集合的多状态设计(MSD)来解决。MSD通过使用近似构象灵活性的集合作为输入模板而非单一固定蛋白质结构,提高了预测序列的质量。在本章中,我们提供了一份使用主链集合进行MSD计算的实施和分析的分步指南。具体而言,我们描述了使用PertMin协议生成集合、执行MSD计算以重现链球菌蛋白G结构域β1突变体的稳定性,以及通过序列分类分析计算预测结果。此外,我们比较了MSD和SSD的计算结果,并讨论了多状态方法对CPD的益处。