TandemAI, New York, New York 10036, United States.
Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine, and Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, United States.
J Chem Inf Model. 2024 Sep 23;64(18):7046-7055. doi: 10.1021/acs.jcim.4c00464. Epub 2024 Sep 3.
Accurate predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.
准确预测小分子与蛋白质的结合强度，例如相对结合自由能 (RBFE) 计算所提供的预测，可大大提高药物发现过程中 hit-to-lead 和先导优化阶段的效率。然而，这些计算的成功在很大程度上依赖于它们的精度。在这里，我们展示了最近开发的一种基于变分的增强采样 (ACES) 方法可以一致地提高对大量不同蛋白质和小分子配体的 RBFE 计算的精度。在一组 10 个蛋白质靶标和 213 个小分子中，将 ACES 加入到传统的 RBFE 计算中，平均滞后降低了 35%以上（0.3-0.4 kcal/mol），平均重复扩散降低了 25%以上（0.2-0.3 kcal/mol），同时保持了相似或更高的准确性。我们详细展示了 ACES 如何通过增强重要的缓慢转变配体自由度的采样来改善几个代表性 RBFE 计算的收敛性。
