Barcelona Supercomputing Center (BSC), Jordi Girona 29, E-08034, Barcelona, Spain.
ICREA, Passeig Lluís Companys 23, E-08010, Barcelona, Spain.
Sci Rep. 2017 Aug 16;7(1):8466. doi: 10.1038/s41598-017-08445-5.
Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.
用原子模拟来模拟蛋白质 - 配体结合的动态特性是计算生物物理学中的主要挑战之一，这对药物设计过程有重要影响。尽管在过去的几年中，硬件和软件的进步极大地改进了分子模拟的应用，但我们仍然缺乏对复杂系统中结合机制的快速、准确的从头描述，这种方法仅适用于最新的技术，需要数小时或数天的繁重计算。这种延迟是蛋白质动力学建模在制药行业中更广泛应用的主要限制因素之一。在这里，我们提出了一项改变游戏规则的技术，通过将自适应强化学习程序与现代多核计算资源中的蒙特卡罗采样相结合，为快速可靠的蛋白质动力学模拟开辟了道路。我们在绘制蛋白质 - 配体能量景观方面表现出了显著的性能，能够在不到半小时的时间内重现完整的结合机制，或者在不到 5 分钟的时间内重现活性位点诱导适应。我们通过研究包括核激素受体和 GPCR 在内的各种复杂靶标来说明我们的方法，展示了在筛选和先导优化研究中使用新的自适应技术的潜力。
