King Edward, Aitchison Erick, Li Han, Luo Ray
Department of Molecular Biology and Biochemistry, University of California, Irvine, CA, United States.
Department of Chemical and Biomolecular Engineering, University of California, Irvine, CA, United States.
Front Mol Biosci. 2021 Aug 11;8:712085. doi: 10.3389/fmolb.2021.712085. eCollection 2021.
The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.
基于结构的药物设计中的重大挑战是实现结合自由能的准确预测。分子动力学(MD)模拟能够对结合过程至关重要的构象变化进行建模,从而计算出参与结合亲和力估算的热力学量。随着计算能力和预测准确性的最新进展,基于MD的虚拟筛选已从理论尝试领域发展到药物开发中的实际应用。包括分子力学泊松玻尔兹曼表面积(MM-PBSA)、线性相互作用能(LIE)和炼金术方法在内的方法已被广泛应用于药物发现和先导优化的分子识别建模。在这里,我们回顾了这些方法的不同方法论、提高模拟效率和可靠性的进展、阻碍预测性能的剩余挑战以及在医学和生物化学领域问题中的应用。