Physics Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8571, Ibaraki Japan.
Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro 152-8550, Tokyo Japan.
J Chem Inf Model. 2023 Dec 25;63(24):7768-7777. doi: 10.1021/acs.jcim.3c01677. Epub 2023 Dec 12.
Peptides have attracted much attention recently owing to their well-balanced properties as drugs against protein-protein interaction (PPI) surfaces. Molecular simulation-based predictions of binding sites and amino acid residues with high affinity to PPI surfaces are expected to accelerate the design of peptide drugs. Mixed-solvent molecular dynamics (MSMD), which adds probe molecules or fragments of functional groups as solutes to the hydration model, detects the binding hotspots and cryptic sites induced by small molecules. The detection results vary depending on the type of probe molecule; thus, they provide important information for drug design. For rational peptide drug design using MSMD, we proposed MSMD with amino acid residue probes, named amino acid probe-based MSMD (AAp-MSMD), to detect hotspots and identify favorable amino acid types on protein surfaces to which peptide drugs bind. We assessed our method in terms of hotspot detection at the amino acid probe level and binding free energy prediction with amino acid probes at the PPI site for the complex structure that formed the PPI. In hotspot detection, the max-spatial probability distribution map (max-PMAP) obtained from AAp-MSMD detected the PPI site, to which each type of amino acid can bind favorably. In the binding free energy prediction using amino acid probes, ΔGFE obtained from AAp-MSMD roughly estimated the experimental binding affinities from the structure-activity relationship. AAp-MSMD, with amino acid probes, provides estimated binding sites and favorable amino acid types at the PPI site of a target protein.
由于其作为针对蛋白质-蛋白质相互作用（PPI）表面的药物的良好平衡特性，肽类最近引起了广泛关注。基于分子模拟的结合位点和与 PPI 表面具有高亲和力的氨基酸残基的预测有望加速肽类药物的设计。混合溶剂分子动力学（MSMD）将探针分子或功能基团的片段作为溶质添加到水合模型中，可检测小分子诱导的结合热点和隐匿位点。检测结果取决于探针分子的类型；因此，它们为药物设计提供了重要信息。为了使用 MSMD 进行合理的肽类药物设计，我们提出了基于氨基酸残基探针的 MSMD（AAp-MSMD），以检测热点并识别与肽类药物结合的蛋白质表面上有利的氨基酸类型。我们从氨基酸探针水平的热点检测和在 PPI 位点使用氨基酸探针的结合自由能预测两个方面评估了我们的方法，这两个方面针对形成 PPI 的复合物结构。在热点检测中，AAp-MSMD 获得的最大空间概率分布图（max-PMAP）检测到每种氨基酸都可以有利结合的 PPI 位点。在使用氨基酸探针进行结合自由能预测中，AAp-MSMD 中的 ΔGFE 从结构-活性关系大致估计了实验结合亲和力。AAp-MSMD 提供了目标蛋白质 PPI 位点的估计结合位点和有利的氨基酸类型。
