蛋白质在许多生物过程中起着关键作用，其与其他蛋白质的相互作用是不可或缺的。异常的蛋白质-蛋白质相互作用（PPIs）已与多种疾病，包括癌症，相关联，因此靶向PPIs对药物开发具有潜力。然而，由于PPIs具有动态和瞬态特性，实验证实其特殊性是具有挑战性的。作为实验技术的补充，已经开发了多种计算机分子对接（MD）方法，用于预测蛋白质-蛋白质复合物及其动力学的结构，但仍需要进一步改进几个问题。在这里，我们报道了一种改进的MD方法，即三软件对接（3SD），通过结合三种流行的蛋白质-肽对接软件（CABS-dock，HPEPDOCK和HADDOCK），以保证对大多数目标的恒定质量。我们在已知的蛋白质-肽相互作用（PpIs）中验证了3SD的性能。我们还通过应用改进的3SD策略，在具有内在无序区（IDRs）的蛋白质中增强了MD性能，即在去除无规卷曲IDR（3SD-RR）后的三软件对接，与可比较的晶体PpI结构相匹配。最后，我们将3SD-RR应用于AlphaFold2预测的受体，得到了对PpI位姿的高质量预测，与实验数据具有高相关性，不受IDRs的存在或受体结构的可用性的影响。我们的研究通过计算对接提供了一种改进的解决方案，以应对研究PPIs的挑战，并有望为PPIs靶向药物发现做出贡献。重要性陈述：蛋白质-蛋白质相互作用（PPIs）是生命不可或缺的，异常的PPIs与疾病如癌症相关。由于其动态和瞬态特性，研究蛋白质-肽相互作用（PpIs）具有挑战性。在这里，我们开发了改进的对接方法（3SD和3SD-RR），用于预测PpIs的位姿，确保在大多数目标中保持恒定质量，并解决了内在的无序区域（IDRs）和人工智能预测的结构等问题。我们的研究通过计算对接提供了一种改进的解决方案，以应对研究PpIs的挑战，并有望为PPIs靶向药物发现做出贡献。版权所有©2023，由Elsevier B.V.出版。

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. SIGNIFICANCE STATEMENT: Protein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.Copyright © 2023. Published by Elsevier B.V.