Departments of Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, Materials Science and Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California Irvine, Irvine, California, USA.
Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai, Zhejiang, China.
Expert Opin Drug Discov. 2023 Mar;18(3):315-333. doi: 10.1080/17460441.2023.2171396. Epub 2023 Feb 23.
Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.
In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.
Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.
蛋白质-蛋白质相互作用（PPIs）是设计新型小分子抑制剂的有趣靶点。由于其平坦的表面、无序性和缺乏凹槽，这些靶点被认为是不可成药的，但它们在各种感染和神经退行性疾病中的作用使它们成为潜在的治疗靶点。
最近计算生物学的进展促使研究人员重新考虑在药物发现中使用 PPIs。
在这篇综述中，我们介绍了用于识别 PPI 界面的计算方法，并深入概述了各种成功应用于注释 PPIs 的计算方法。我们还讨论了几个成功的案例研究，这些研究使用计算工具来理解 PPIs 的调节及其在各种生理过程中的关键作用。
由于蛋白质的固有灵活性，计算方法面临挑战，这使得它们昂贵，并导致使用刚性模型。由于 PPI 的柔性和平坦界面，这个问题变得更加严重。计算方法，如分子动力学（MD）模拟和机器学习，可以将化学结构数据整合到生化中，并可用于目标识别和调节。这些计算方法在理解 PPIs 的结构、设计 PPI 调节剂、发现新的药物靶点和预测治疗结果方面发挥了至关重要的作用。
