Perez Juan J, Perez Roman A, Perez Alberto
Department of Chemical Engineering, Universitat Politecnica de Catalunya, Barcelona, Spain.
Bioengineering Institute of Technology, Universitat Internacional de Catalunya, Sant Cugat, Spain.
Front Mol Biosci. 2021 May 20;8:681617. doi: 10.3389/fmolb.2021.681617. eCollection 2021.
Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.
蛋白质-蛋白质相互作用（PPI）介导大量重要的调控途径。对其进行调控是发现新型治疗药物的重要策略。然而，PPI结合表面的特性使得基于结构的药物发现方法极具挑战性。在文献中用于解决该问题的各种方法中，线性肽已被证明是发现PPI破坏剂的合适方法。不幸的是，线性肽较差的药代动力学性质使其无法直接用作药物。然而，它们可用作设计抗酶类似物的模型，包括环肽、肽模拟物或拟肽。小分子能够结合的靶标范围较窄，但基于虚拟对接的筛选技术强大且经过充分测试，这为用于破坏PPI的计算工具增添了助力。我们综述了用于理解和调控PPI的计算方法，并在一些涉及细胞生长、凋亡和细胞间通讯等生理过程的案例研究中突出了其应用。
