Zheng Fan, Grigoryan Gevorg
Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA.
Department of Computer Science, Dartmouth College, 6211 Sudikoff Lab, Room 113, Hanover, NH, 03755, USA.
Methods Mol Biol. 2017;1561:189-200. doi: 10.1007/978-1-4939-6798-8_11.
Computationally designed peptides targeting protein-protein interaction interfaces are of great interest as reagents for biological research and potential therapeutics. In recent years, it has been shown that detailed structure-based calculations can, in favorable cases, describe relevant determinants of protein-peptide recognition. Yet, despite large increases in available computing power, such accurate modeling of the binding reaction is still largely outside the realm of protein design. The chief limitation is in the large sequence spaces generally involved in protein design problems, such that it is typically infeasible to apply expensive modeling techniques to score each sequence. Toward addressing this issue, we have previously shown that by explicitly evaluating the scores of a relatively small number of sequences, it is possible to synthesize a direct mapping between sequences and scores, such that the entire sequence space can be analyzed extremely rapidly. The associated method, called Cluster Expansion, has been used in a number of studies to design binding affinity and specificity. In this chapter, we provide instructions and guidance for applying this technique in the context of designing protein-peptide interactions to enable the use of more detailed and expensive scoring approaches than is typically possible.
作为生物研究试剂和潜在治疗手段，针对蛋白质-蛋白质相互作用界面进行计算设计的肽备受关注。近年来，研究表明，在有利情况下，基于详细结构的计算能够描述蛋白质-肽识别的相关决定因素。然而，尽管可用计算能力大幅提升，但这种对结合反应的精确建模在很大程度上仍超出蛋白质设计的范畴。主要限制在于蛋白质设计问题通常涉及的巨大序列空间，以至于应用昂贵的建模技术对每个序列进行评分通常是不可行的。为了解决这个问题，我们之前已经表明，通过明确评估相对少量序列的分数，可以合成序列与分数之间的直接映射，从而能够极其快速地分析整个序列空间。相关方法称为聚类扩展，已在多项研究中用于设计结合亲和力和特异性。在本章中，我们提供了在设计蛋白质-肽相互作用的背景下应用该技术的说明和指导，以便能够使用比通常情况更详细、更昂贵的评分方法。
