Read-Across the Targetome - An integrated structure- and ligand-based workbench for computational design of novel tool compounds

Read-Across the Targetome - 基于结构和配体的集成工作台，用于新型工具化合物的计算设计

• 批准号: 391684253
• 负责人: Professorin Dr. Andrea Volkamer
• 金额: --
• 依托单位: Institut für Physiologie (CCM)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/391684253?language=en
• 关键词: Read; Across; Targetome; integrated; structure

How to probe and validate a potential pathway or target remains one of the key questions in basic research in life sciences. Often these investigations lack suitable chemical tool compounds for the elucidation of the function of a specific protein. Therefore, large consortia, like the Structural Genomics Consortium, have formed to generate tool compounds for the validation of biological targets via classical chemical synthesis and extensive protein structure determination efforts. While these consortia will continue with their focused experimental approaches, the read-across the targetome project will offer a computational solution for the generation of a comprehensive set of tool compounds for novel targets. The project hypothesis is based on the similarity principle (similar pockets bind similar compounds) with the ultimate goal of using protein pocket similarity to extrapolate compound information from one target to another. With this concept, the investigator seeks to answer the central question: Can binding site similarity be used to propose tool compounds for novel targets? Based on this new paradigm for tool compound identification, a holistic workbench will be developed. With currently over 127,000 macromolecular structures in the protein data bank (PDB) and over 1.68 million tested compounds in ChEMBL, a wealth of structural and binding data is freely available, which will be systematically explored here. First, protein structures will be collected from the PDB. For all structures per protein, potential pockets will be identified and clustered to ensemble pockets. Next, a novel efficient structure-based binding site comparison algorithm will be developed to find the most similar pockets considering protein flexibility in terms of ensembles. Ligand and binding data will be extracted from ChEMBL, filtered and assigned to the respective pockets. Using the novel algorithm, ligands known to bind to the detected neighboring pockets can be elucidated. These compounds can be selected as chemical probes for functional annotations or as novel focused compound libraries for virtual screening. The whole procedure will be made available as a web-service to combine the data and methods in one place, as well as to allow simplified access to the methodology for researchers from different life science disciplines.The combined usage of available structural and chemical information, as well as knowledge transfer from known compounds to novel similar binding pockets, will help to speed-up biological and pharmaceutical research in the area of pathway and target validation.

如何探索和验证潜在的途径或靶点是生命科学基础研究的关键问题之一。通常这些研究缺乏合适的化学工具化合物来阐明特定蛋白质的功能。因此，已经形成了大型联盟，如结构基因组学联盟，以通过经典的化学合成和广泛的蛋白质结构测定工作来产生用于验证生物靶标的工具化合物。虽然这些联盟将继续其专注的实验方法，但目标组项目将提供一种计算解决方案，用于为新目标生成一套全面的工具化合物。该项目假设基于相似性原理（相似的口袋结合相似的化合物），最终目标是使用蛋白质口袋相似性将化合物信息从一个靶标外推到另一个靶标。有了这个概念，研究人员试图回答的中心问题：结合位点的相似性可以用来提出新的目标工具化合物？基于这种新的模式，工具化合物识别，一个整体的工作台将被开发。目前，蛋白质数据库（PDB）中有超过127，000种大分子结构，ChEMBL中有超过168万种测试化合物，可以免费获得丰富的结构和结合数据，本文将系统地探讨这些数据。首先，将从PDB收集蛋白质结构。对于每种蛋白质的所有结构，将识别潜在的口袋并聚类为集合口袋。接下来，将开发一种新的有效的基于结构的结合位点比较算法，以找到最相似的口袋，考虑到蛋白质的灵活性，在合奏。将从ChEMBL中提取配体和结合数据，过滤并分配至相应的口袋。使用新的算法，已知的配体结合到检测到的相邻口袋可以被阐明。这些化合物可以被选择作为功能注释的化学探针或作为虚拟筛选的新型聚焦化合物库。整个过程将以网络服务的形式提供，以便将数据和方法联合收割机集中在一个地方，并允许来自不同生命科学学科的研究人员简化访问方法。现有结构和化学信息的综合使用，以及从已知化合物到新型类似结合口袋的知识转移，将有助于加快在途径和靶点验证领域的生物学和制药学研究。