Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University,Grahamstown 6140, South Africa.
Graduate Program in Computational and Data Sciences, Keck Center for Science and Engineering, SchmidCollege of Science and Technology, Chapman University, One University Drive, Orange, California 92866,USA.
Int J Mol Sci. 2020 Jan 28;21(3):847. doi: 10.3390/ijms21030847.
Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and design of drug combinations with improved therapeutic indices and a broad range of activities.
理解蛋白质变构调节复杂性的分子机制在药物发现中引起了相当大的关注，因为变构调节剂具有提供针对蛋白质靶标的理想选择性、最小化毒性和其他副作用的益处和多功能性。使用动态和以网络为中心的观点预测配体-蛋白质相互作用和结合的新型计算方法的激增，导致对变构机制有了新的认识，并促进了基于计算机的变构药物发现。虽然不存在用于实验和变构药物/位点发现的绝对方法，但目前的方法仍在不断改进。因此，将成熟的方法进行批判性分析和整合到具有实验反馈的稳健、可重复和可定制的计算管道中，可以使变构药物发现更高效、更可靠。在本文中，我们回顾了变构药物发现的计算方法，并讨论了如何利用这些工具来开发用于鉴定变构位点和调节剂的共识工作流程，以及一些在病原体耐药性和精准医学中的应用。人们逐渐认识到，变构调节剂可以利用独特的调节机制，并可以提供针对蛋白质活性的靶向调节，这为探索生物过程和设计具有改善的治疗指数和广泛活性的药物组合开辟了机会。
