Acellera Labs, C/Doctor Trueta 183, 08005 Barcelona, Spain.
CADD, UCB BioPharma, 1420 Braine-l'Alleud, Belgium.
J Chem Inf Model. 2020 Apr 27;60(4):2314-2324. doi: 10.1021/acs.jcim.9b01209. Epub 2020 Apr 1.
Cryptic pockets are protein cavities that remain hidden in resolved apo structures and generally require the presence of a co-crystallized ligand to become visible. Finding new cryptic pockets is crucial for structure-based drug discovery to identify new ways of modulating protein activity and thus expand the druggable space. We present here a new method and associated web application leveraging mixed-solvent molecular dynamics (MD) simulations using benzene as a hydrophobic probe to detect cryptic pockets. Our all-atom MD-based workflow was systematically tested on 18 different systems and 5 additional kinases and represents the largest validation study of this kind. CrypticScout identifies benzene probe binding hotspots on a protein surface by mapping probe occupancy, residence time, and the benzene occupancy reweighed by the residence time. The method is presented to the scientific community in a web application available via www.playmolecule.org using a distributed computing infrastructure to perform the simulations.
隐匿口袋是指在已解析的无配体蛋白结构中仍然隐藏的蛋白腔,通常需要与共结晶配体结合才能显现。发现新的隐匿口袋对于基于结构的药物发现至关重要,它可以帮助确定新的调节蛋白活性的方法,从而扩大可成药的空间。我们在这里提出了一种新的方法,并通过使用苯作为疏水探针的混合溶剂分子动力学 (MD) 模拟,开发了一个相关的网络应用程序,以检测隐匿口袋。我们的全原子 MD 工作流程在 18 个不同的系统和 5 个额外的激酶上进行了系统测试,这是此类验证研究中规模最大的一次。CrypticScout 通过映射探针占有率、停留时间以及基于停留时间重新加权的苯占有率,在蛋白质表面上识别出苯探针结合的热点。该方法以网络应用程序的形式呈现给科学界,可通过 www.playmolecule.org 使用分布式计算基础设施来执行模拟。