Department of Molecular Biology, Biotechnology and Bioinformatics, Chaudhary Charan Singh, Haryana Agricultural University, Hisar 125004, India.
Tata Institute of Fundamental Research, Center for Interdisciplinary Sciences, Hyderabad 500046, India.
J Phys Chem Lett. 2021 Jan 14;12(1):633-641. doi: 10.1021/acs.jpclett.0c02785. Epub 2020 Dec 31.
Computer simulation approaches in biomolecular recognition processes have come a long way. In this Perspective, we highlight a series of recent success stories in which computer simulations have played a remarkable role in elucidating the atomic resolution mechanism of kinetic processes of protein-ligand binding in a quantitative fashion. In particular, we show that a robust combination of unbiased simulation, harnessed by a high-fidelity computing environment, and Markov state modeling approaches has been instrumental in revealing novel protein-ligand recognition pathways in multiple systems. We also elucidate the role of recent developments in enhanced sampling approaches in providing the much-needed impetus in accelerating simulation of the ligand recognition process. We identify multiple key issues, including force fields and the sampling bottleneck, which are currently preventing the field from achieving quantitative reconstruction of experimental measurements. Finally, we suggest a possible way forward via adoption of multiscale approaches and coarse-grained simulations as next steps toward efficient elucidation of ligand binding kinetics.
计算机模拟方法在生物分子识别过程中已经取得了很大的进展。在本文中,我们强调了一系列最近的成功案例,其中计算机模拟在以定量方式阐明蛋白质-配体结合的动力学过程的原子分辨率机制方面发挥了重要作用。特别是,我们表明,无偏模拟的稳健组合,利用高保真计算环境和马尔可夫状态建模方法,对于揭示多个系统中的新型蛋白质-配体识别途径至关重要。我们还阐明了增强采样方法的最新发展在提供加速配体识别过程模拟所需的动力方面的作用。我们确定了多个关键问题,包括力场和采样瓶颈,这些问题目前阻碍了该领域对实验测量进行定量重建。最后,我们通过采用多尺度方法和粗粒化模拟作为阐明配体结合动力学的下一步,提出了一种可能的前进方向。