Alonso Hernán, Bliznyuk Andrey A, Gready Jill E
Computational Proteomics Group, John Curtin School of Medical Research, The Australian National University, Canberra ACT 0200, Australia.
Med Res Rev. 2006 Sep;26(5):531-68. doi: 10.1002/med.20067.
A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.
需要一种合理的方法来最大化发现新药的机会，并利用基因组学和蛋白质组学计划以及现在通过组合化学可轻易获得的大量小分子化合物库中出现的潜在新药靶点的机会。尽管早期历史不稳定，但计算机辅助药物设计技术现在可以有效地降低成本并加速药物发现。这一令人满意的结果源于更准确可靠算法的开发、应用这些算法时更精心规划的策略的使用，以及计算机能力的大幅提升，从而能够进行具有必要可靠性的研究。我们的综述重点关注应用和方案，主要强调对近期研究的批判性分析，这些研究将对接计算和分子动力学（MD）模拟相结合，将小分子对接至蛋白质受体。我们突出成功案例以展示目前可能实现的成果，但也指出缺点和未来方向。综述的结构旨在引导读者从更简单的方法过渡到计算量更大的方法。因此，虽然廉价且快速的对接算法可用于扫描大型化合物库并缩小其规模，但当只剩下少数选定的配体候选物时，可以应用更准确但昂贵的MD模拟。MD模拟可用于：在对接前蛋白质受体的制备过程中，优化其结构并考虑蛋白质的灵活性；对接复合物的优化，以纳入溶剂效应并考虑诱导契合；计算结合自由能，以提供潜在配体的准确排名；以及在最新进展中，在对接过程本身中找到结合位点并事先正确对接配体。
