|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-molecular-docking-with-autodock-vina/
课程评论：没有评论
**Coursera课程：分子对接（Molecular Docking）** 本课程深入探讨了分子对接这一强大的计算化学技术，旨在预测生物大分子（受体）与小分子（配体）之间非共价相互作用的结合模式和结合亲和力。课程从受体和小分子的初始结构（可以是实验解析、分子动力学模拟或同源建模获得的）出发，介绍如何预测其结合时的构象。 **课程重点内容：** * **分子对接的原理与应用：** 详细解释了分子对接在药物发现中的重要性，尤其是在虚拟筛选中寻找潜在的药物先导化合物。课程还将探讨在缺乏实验结构数据时，如何利用分子对接预测已知结合物的结合构象。 * **核心理论：** 深入剖析了分子对接的两个关键组成部分： * **采样算法（Sampling Algorithms）：** 介绍如何有效地搜索配体和小分子之间的可能结合位点和构象。 * **评分函数（Scoring Functions）：** 讲解如何评估和预测不同结合模式的结合强度（亲和力）。 * **软件与性能比较：** 回顾了多种现有的分子对接软件，并讨论了它们之间的差异、性能表现以及在药物发现中的应用实例。 * **柔性受体对接的挑战与解决方案：** 特别关注在受体结构中考虑（尤其是骨架）柔性的难题，并介绍了一种名为“局部移动蒙特卡洛”（Local Move Monte Carlo, LMMC）的新型方法，该方法被提出作为解决柔性受体对接问题的潜在方案。 * **AutoDock Vina介绍：** 重点介绍了AutoDock Vina这一新的分子对接和虚拟筛选程序。课程将展示AutoDock Vina相比于之前的AutoDock 4在速度上（约提高两个数量级）和预测精度上的显著提升。此外，还将探讨其如何在多核机器上利用多线程实现进一步的速度优化，以及其自动网格图计算和结果聚类等用户友好的特性。 本课程为学习者提供了理解和应用分子对接技术的坚实基础，为药物发现和相关领域的研究提供了重要的理论和实践指导。
Molecular docking is a computational procedure that attempts to predict non-covalent binding of macromolecules or, more frequently, of a macromolecule (receptor) and a small molecule (ligand) efficiently, starting with their unbound structures, structures obtained from MD simulations, or homology modelling, etc. The goal is to predict the bound conformations and the binding affinity.The prediction of binding of small molecules to proteins is of particular practical importance because it is used to screen virtual libraries of drug-like molecules in order to obtain leads for further drug development. Docking can also be used to try to predict the bound conformation of known binders, when the experimental holo structures are unavailable.1One is interested in maximising the accuracy of these predictions while minimising the computer time they take, since the computational resources spent on docking are considerable.Molecular docking has become an increasingly important tool for drug discovery. In this review, we present a brief introduction of the available molecular docking methods, and their development and applications in drug discovery. The relevant basic theories, including sampling algorithms and scoring functions, are summarised. The differences in and performance of available docking software are also discussed. Flexible receptor molecular docking approaches, especially those including backbone flexibility in receptors, are a challenge for available docking methods. A recently developed Local Move Monte Carlo (LMMC) based approach is introduced as a potential solution to flexible receptor docking problems. Three application examples of molecular docking approaches for drug discovery are provided.Autodock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared to the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multi-core machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.
