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

小分子片段热点分析可推动整个蛋白质家族选择性分子的半自动设计

基本信息

批准号：
1940170
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1940170
关键词：
Small Molecule Fragment Hotspot Analyses

项目摘要

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.

该项目福尔斯属于化学生物学/生物化学、生物信息学、计算化学研究领域，如EPSRC网站所述。公司/合作者：SGC（Structural Genomics Consortium，Oxford）/XChem，CCDC（剑桥晶体数据中心），Exscientia拟议项目概述：过去十年中，大量生物分子疾病靶点的基因组和结构数据的可用性呈爆炸式增长。合理的药物发现旨在创造针对这些靶点的有效和选择性化合物，目的不仅是开发药物，而且是高选择性探针来研究蛋白质功能。事实证明，这是一项具有挑战性且代价高昂的工作，为实现这一过程的自动化已做出了相当大的努力。目前，自动化方法，如通过X射线晶体学进行片段筛选，输出了大量关于与蛋白质靶点复合的低分子量分子的结构数据。解释这些数据提出了一个复杂的问题，因此需要计算工具，可以将其提炼成化合物的建议。虽然相当大的努力已经投入到开发工具来预测结合亲和力，很少有人知道的方式，其中选择性可以表征和predicted.Fragment热点分析是一种新的和有前途的方法，可以突出特定的相互作用的蛋白质与化合物，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相关目标家族设计化合物时的选择性。在旋转过程中，我研究了将同一蛋白质的X射线结构集合中的联合收割机热点信息组合成热点“集合图”的方法，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，需要将其应用于更广泛的蛋白质和蛋白质家族，进一步的工作将从集合图中聚类和提取重要特征。此外，将研究载脂蛋白、片段结合和配体结合结构的集成图之间的差异。从长远来看，该项目将尝试开发新的方法或途径来对X射线晶体学筛选活动中的碎片进行排序，重点是结合计算和实验方法的信息。