Small Molecule Fragment Hotspot Analyses to Drive Semi-Automated Design of Selective Molecules across a Protein Family

该项目属于EPSRC网站上概述的化学生物学/生物化学、生物信息学、计算化学研究领域。公司/合作者：SGC(结构基因组学联盟，牛津大学)/XChem，CCDC(剑桥晶体数据中心)，ExScience建议项目概述：过去十年，大量生物分子疾病靶标的基因组和结构数据的可用性出现了爆炸性增长。合理的药物发现旨在创造针对这些靶点的有效和选择性的化合物，目的不仅是开发药物，而且还开发高选择性的探针来研究蛋白质功能。事实证明，这是一项具有挑战性和代价高昂的工作，已经作出了相当大的努力来使这一过程自动化。目前，X-射线结晶学片段筛选等自动化方法产生了大量与蛋白质靶标形成复合体的低分子分子的结构数据。解释这些数据是一个复杂的问题，因此需要能够将其提炼成复合精化建议的计算工具。虽然人们已经投入了大量的精力来开发预测结合亲和力的工具，但对选择性的表征和预测方法知之甚少。片段热点分析是一种新的有前途的方法，可以突出蛋白质与化合物之间的特定相互作用，以驱动其结合。在轮换项目工作的基础上，DPhil项目最初将专注于使用片段热点分析来描述和寻找针对一系列相关目标设计化合物时的选择性。在旋转过程中，我寻找了一些方法，将相同蛋白质的X射线结构集合中的热点信息组合成热点“集合图”，然后减去两种不同蛋白质的集合图，以突出两种蛋白质结合口袋的差异。为了扩展这项工作，它需要应用于更多种类的蛋白质和蛋白质家族，进一步的工作将从集合地图中聚集和提取重要特征。此外，还将研究脱辅基、片段结合和配体结合结构的系综映射之间的差异。从长远来看，该项目将尝试开发新的方法或途径来对X射线晶体筛选活动中的碎片进行排序，重点是结合来自计算方法和实验方法的信息。

This project falls within the Chemical biology/biological chemistry, biological informatics, computational chemistry research areas, as outlined on the EPSRC website.Companies/collaborators: SGC (Structural Genomics Consortium, Oxford)/XChem, CCDC (Cambridge Crystallographic Data Centre), ExscientiaSummary of the proposed project:The past decade has seen an explosion in the availability of genomic and structural data for a great number of biomolecular disease targets. Rational drug discovery aims to create potent and selective compounds against these targets, with the aim of developing not only drugs, but also highly selective probes to investigate protein function. This has proven to be a challenging and expensive endeavour, and considerable efforts have been made to automate this process. Currently, automated methods such as fragment screening by X-ray crystallography output a wealth of structural data on low molecular weight molecules in complex with protein targets. Interpreting this data presents a complex problem, so computational tools that can distill it into suggestions for compound elaboration are needed. While considerable effort has been put into developing tools to predict binding affinity, less is known about the ways in which selectivity can be characterised and predicted.Fragment hotspot analysis is a new and promising method that can highlight the specific interactions a protein makes with a compound to drive its binding. Building on work from the rotation project, the DPhil project will initially focus on using fragment hotspot analysis to describe and look for selectivity when designing compounds against a family of related targets. During the rotation, I looked at ways to combine hotspot information across an ensemble of X-ray structures of the same protein into a hotspot "ensemble map", then subtracted the ensemble maps for two different proteins to highlight differences in the binding pockets of the two proteins. To extend this work, it needs to be applied to a wider variety of proteins and protein families, and further work will cluster and extract important features from the ensemble maps. In addition, differences between ensemble maps for apo- , fragment-bound and ligand-bound structures will be investigated. In the longer term, the project will attempt to develop novel methods or approaches to ranking fragment hits from X-ray crystallography screening campaigns, focussing on combining information from both computational and experimental methods.