Efficient modelling and validation of cryptic protein binding sites for drug discovery

超过75%的与疾病相关的蛋白质不能被传统的化学生物学方法轻易地靶向。需要新的方法来扩大分子医学的范围。隐结合口袋，即在折叠蛋白质中短暂形成的口袋，但在未配体载脂蛋白的晶体结构中不明显，为靶向蛋白质提供了绝佳的机会，否则被认为是“不可药物”的，因此在学术界和制药行业引起了相当大的兴趣。不幸的是，它们不仅是出了名的难以识别，而且它们形成的分子机制也仍然存在争议。这个合作项目的目的是解决知识差距，并开发一个基于原子分子模拟的高效计算平台，以系统地检测生物制药目标中的可药物隐藏口袋。该平台将建立在我们在开发和应用增强采样模拟算法以进行分子识别方面的成功经验之上，并将在包含隐藏位点的经过验证的药物靶标上进行广泛测试。计算结果将通过与工业伙伴（UCB）合作的实验组合在新的目标上进一步验证。

Over 75% of disease-involved proteins cannot be readily targeted by conventional chemical biology approaches. New approaches are needed to increase the scope of molecular medicine. Cryptic binding pockets, i.e. pockets that transiently form in a folded protein, but are not apparent in the crystal structure of the unliganded apo-form, offer outstanding opportunities to target proteins otherwise deemed 'undruggable' and are thus of considerable interest in academia and the pharmaceutical industry. Unfortunately, not only they are notoriously difficult to identify, but also the molecular mechanism by which they form is still debated. The aim of this collaborative project is to address the knowledge gaps and develop an efficient computational platform based on atomistic molecular simulations to systematically detect druggable cryptic pockets in targets of biopharmaceutical interest. The platform will build on our successful experience in developing and applying enhanced-sampling simulation algorithms to molecular recognition, and will be extensively tested on validated drug targets harbouring cryptic sites. The computational results will be further validated on novel targets by a combination of experiments in collaboration with an industrial partner (UCB).