Inserm U1242, Oncogenesis, Stress and Signaling (OSS), Université de Rennes 1, Rennes, France.
Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France.
Methods Mol Biol. 2024;2714:33-83. doi: 10.1007/978-1-0716-3441-7_3.
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).
理性药物设计对于新药物的出现至关重要，尤其是当目标蛋白质或核酸的结构已知时。为此，高通量虚拟配体筛选活动旨在发现计算上的新结合分子或片段，以调节与疾病过程相关的特定生物分子相互作用或生物活性。基于结构的虚拟配体筛选过程主要依赖于对接方法，该方法允许预测分子与生物靶标结构的结合，具有正确的构象和最佳可能的亲和力。对接方法本身并不足够，因为它存在几个关键的局限性（缺乏完整的蛋白质灵活性信息、没有溶剂化和离子效应、评分函数较差以及分子亲和力估计不可靠）。在计算机技术和药物发现的接口处，分子动力学（MD）允许在对接方案之前或之后引入蛋白质灵活性，在存在水、离子甚至类似膜的环境下，对蛋白质-药物复合物的结构进行细化，更精确地描述生物复合物的时间演变，并使用更准确的结合能计算对这些复合物进行排序。在本章中，我们将描述最新的 MD，它在虚拟配体筛选（VS）过程中充当支持工具。毫无疑问，现在使用对接与 MD 相结合是基于结构的药物发现协议中的一种有吸引力的方法。它已经通过文献中的许多例子证明了它的效率，并且是一种非常有效的方法，可以大大减少所需的湿实验数量（Tarcsay 等人，J Chem Inf Model 53：2990-2999，2013；Barakat 等人，PLoS One 7：e51329，2012；De Vivo 等人，J Med Chem 59：4035-4061，2016；Durrant，McCammon，BMC Biol 9：71-79，2011；Galeazzi，Curr Comput Aided Drug Des 5：225-240，2009；Hospital 等人，Adv Appl Bioinforma Chem 8：37-47，2015；Jiang 等人，Molecules 20：12769-12786，2015；Kundu 等人，J Mol Graph Model 61：160-174，2015；Mirza 等人，J Mol Graph Model 66：99-107，2016；Moroy 等人，Future Med Chem 7：2317-2331，2015；Naresh 等人，J Mol Graph Model 61：272-280，2015；Nichols 等人，J Chem Inf Model 51：1439-1446，2011；Nichols 等人，Methods Mol Biol 819：93-103，2012；Okimoto 等人，PLoS Comput Biol 5：e1000528，2009；Rodriguez-Bussey 等人，Biopolymers 105：35-42，2016；Sliwoski 等人，Pharmacol Rev 66：334-395，2014）。
