GroScore: Accurate Scoring of Protein–Protein Binding Poses Using Explicit-Solvent Free-Energy Calculations

Protein–protein docking algorithms promise a potential
relief
for the mismatch between the number of experimentally determined complex
structures and the number of relevant protein interactions in an organism.
To distinguish correctly from wrongly generated poses, it is necessary
to score complexes according to their structural similarity to the
real complex, which is usually done by computing interaction energies
of some sort. Here, we explore the potential of free-energy calculations
with statistical-mechanical foundation in the context of molecular
dynamics (MD) simulations with explicit solvent to score a large number
of complex poses. We introduce an adaptive sampling scheme which ensures
that most sampling time is spent on the most promising poses. Our
approach is illustrated by scoring of all targets in the CAPRI Score_set,
a scoring benchmark set, and three additional CAPRI targets, together
consisting of more than 22 000 poses. Our scoring scheme shows
a performance that is competitive with the most successful approaches
that were previously reported. All necessary scripts to run the automated
scoring pipeline are available in the Supporting Information for this
paper.

蛋白质-蛋白质对接算法有望缓解实验解析的复合物结构数量与生物体中相关蛋白质相互作用数量之间的失衡问题。为区分正确与错误生成的对接构象，需依据复合物与真实复合物的结构相似性对其进行打分，该过程通常通过计算各类相互作用能量完成。本研究探索了基于统计力学基础的自由能计算方法，结合显式溶剂分子动力学（MD）模拟，对大量复合物对接构象进行打分的潜力。我们提出了一种自适应采样方案，可确保绝大多数采样时间被分配至最具潜力的对接构象上。本方法通过对CAPRI打分基准集（CAPRI Score_set）中的全部靶标以及另外3个CAPRI靶标进行打分得以验证，这些靶标总计包含超过22000个对接构象。本打分方案的性能可与此前报道的最优秀同类方法相媲美。运行自动化打分流程所需的全部脚本均可在本文的支持信息中获取。