Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC)-based method (NCMC + MD) perform in predicting binding modes for a set of 12 fragment-like molecules, which bind to soluble epoxide hydrolase. We evaluate each method’s effectiveness in identifying the dominant binding mode and finding additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures. We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 μs each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions between binding modes in our study. Following this, we build Markov State Models to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD, we have better success in sampling the crystal structure while obtaining the correct populations.药物研发早期阶段的核心工作之一，是探明分子与靶蛋白的结合方式。通过解析分子结合的位点与作用机制，化学家可据此构建优化思路，以提升分子的结合亲和力。本项回顾性研究中，我们对比了分子对接、分子动力学（MD）模拟以及基于非平衡候选蒙特卡洛（non-equilibrium candidate Monte Carlo）的方法（NCMC+MD）等计算手段，在12种结合可溶性环氧化物水解酶（soluble epoxide hydrolase）的类片段分子集合中预测结合模式的表现。我们评估了每种方法在识别优势结合模式以及发现额外结合模式（若存在）方面的有效性。随后，我们将预测得到的结合模式与实验获取的X射线晶体结构进行比对。我们将这12种小分子逐一对接至脱辅基蛋白晶体结构中，随后分别开展时长可达1微秒的模拟。相较于类药分子，类片段分子的相互作用更少且更弱，因此其不同结合模式间的能垒更低，更易发生结合模式的快速转换。据此我们预期，本研究中会观察到结合模式间更频繁的转换。在此基础上，我们构建了马尔可夫状态模型（Markov State Models），以界定配体的稳定结合模式。我们探究了传统MD模拟或混合NCMC+MD模拟方法，能否在微秒时间尺度下充分采样配体的结合模式及其间的转换过程。研究结果表明，即便针对小型类片段分子，传统微秒级MD模拟仍无法采样到所有晶体学结合模式；而采用NCMC+MD方法时，我们不仅能更高效地采样到晶体结构，还能获得准确的结合模式占比。