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基于计算的链接多片段筛选发现增殖细胞核抗原抑制剂

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen.

作者信息

Bartolowits Matthew D, Gast Jonathon M, Hasler Ashlee J, Cirrincione Anthony M, O'Connor Rachel J, Mahmoud Amr H, Lill Markus A, Davisson Vincent Jo

机构信息

Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana 47907, United States.

Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Ain Shams University, Cairo 11566, Egypt.

出版信息

ACS Omega. 2019 Sep 6;4(12):15181-15196. doi: 10.1021/acsomega.9b02079. eCollection 2019 Sep 17.

Abstract

Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein-protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA-protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on -alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA-protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein-protein interactions, demonstrating the method's utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

摘要

增殖细胞核抗原(PCNA)是DNA复制和修复途径中的核心因子,在基因组稳定性中起着至关重要的作用。PCNA的功能作用是通过大量的蛋白质-蛋白质相互作用介导的,其中每一种相互作用都在蛋白质组装中传递特定信息。PCNA-蛋白质相互作用界面的灵活性为发现DNA修复途径的功能选择性抑制剂提供了机会。当前基于片段的药物设计方法可能会受到蛋白质界面灵活性的限制。这些因素促使人们采用一种方法来定义能够利用PCNA上先前确定的适合片段结合位点的亚口袋的化合物。部署用于筛选不同亚口袋中多个相连片段结合事件的方法,以改进片段组合的选择。基于β-烷基甘氨酸酰胺的柔性骨架提供了一个支架,用于组合连接多个片段,以构建用于计算机筛选文库,探索蛋白质界面亚口袋的多样性。该方法被应用于发现新的潜在的DNA复制和修复抑制剂,这些抑制剂在多蛋白识别位点靶向PCNA。文库筛选旨在根据片段和位置对配体进行计算过滤,使其比例小于1%,然后合成并测试它们与PCNA的直接结合。分子动力学模拟还揭示了这些新型分子阻断关键PCNA-蛋白质相互作用的独特特征。此外,贝叶斯分类器预测16种新抑制剂中的15种是蛋白质-蛋白质相互作用的调节剂,证明了该方法作为一种有效筛选过滤器的实用性。对PCNA具有相似亲和力的示例配体的细胞活性表明,在耐药环境中,它们与治疗性DNA损伤剂具有独特的选择性协同特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f31b/6751697/7fa9fd297633/ao9b02079_0001.jpg

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