Efficient synthon-based modular screening of Giga-to-Terra-scale virtual libraries

基于合成子的高效模块化筛选千兆级到太级虚拟文库

基本信息

批准号：
10504984
负责人：
VSEVOLOD KATRITCH
金额：
$ 41.25万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-26 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10504984
关键词：
Adoption Affinity Algorithms Benchmarking Binding CNR1 gene CNR2 gene Chemicals Cloud Computing Code Collaborations Communities Computer Analysis Cyclic AMP-Dependent Protein Kinases Development Docking Drug Discovery Groups Ensure G-Protein-Coupled Receptors Geometry Goals Growth Institution Lead Letters Libraries Ligands Linux Lipid Binding Machine Learning Nature Nucleotides Orphan Pharmaceutical Preparations Phosphotransferases Positioning Attribute Property Proteins Pythons ROCK1 gene Reaction Research Resolution Seeds Structure Technology Testing Time Validation Work analog base cannabinoid receptor cannabinoid receptor antagonist chemical synthesis clinically relevant cluster computing combinatorial computational platform computing resources cost cost effective drug candidate drug discovery drug quality improved in silico new technology novel novel strategies open source portability prospective rapid detection rapid growth receptor scaffold scale up screening therapeutic target virtual virtual library virtual screening

项目摘要

ABSTRACT The goal of our proposal is to develop a scalable platform for structure-based virtual screening of Giga- and Tera- scale drug-like compound libraries, enabling streamlined discovery of high-quality drug candidates. Availability of protein target structures and Giga-scale REAL Space libraries of virtual compounds (>10 billion) position docking-based virtual screening as a key paradigm for drug discovery. However, the computational cost of Giga- scale screening becomes a major bottleneck limiting further growth of the screening libraries. Recently, we have introduced a highly scalable synthon-based technology, V-SYNTHES, which performs hierarchical structure- based screening of REadily AvaiLable for synthesis (REAL) libraries (Sadybekov et al, Nature accepted). By iteratively screening synthon-scaffold combinations, the V-SYNTHES approach makes possible rapid detection of the best-scoring compounds in the Giga-scale chemical space while performing docking of only a small fraction (~2 million) of the library. First tests of V-SYNTHES demonstrated strong enrichment in computational benchmarks and significantly improved experimental hit rates on cannabinoid receptor CB2 and ROCK1 kinase targets, while requiring 100 times less computational resources than standard virtual screenings. Building upon these preliminary results, our proposal aims to: (1) Further develop a fully automated V- SYNTHES algorithm, optimize its parameters and expand it to Tera-scale REAL libraries. (2) Apply and experimentally validate the V-SYNTHES approach on a set of therapeutic targets of different classes, which includes such challenging targets as nucleotide and lipid binding pockets, allosteric pockets, and orphan receptors (3) Establish portability of the algorithm to an open-source docking platform to further facilitate V- SYNTHES adoption in academic labs. The open-source algorithm will be distributed as a workflow for Linux clusters and computing clouds. Successful completion of this project will establish V-SYNTHES as a robust computational platform for structure-based ligand discovery in most classes of therapeutic targets, scaleable for rapidly growing REAL modular libraries. Most importantly, it will help to make fast virtual screening of the Giga-to-Tera-scale libraries broadly accessible for the whole research community with reasonable computational resources.

抽象的我们提案的目标是开发一个可扩展的平台，用于基于结构的千兆和兆兆级虚拟筛选扩展类药化合物库，从而简化高质量候选药物的发现。可用性蛋白质目标结构和虚拟化合物（>100亿）位置的千兆级真实空间库基于对接的虚拟筛选作为药物发现的关键范例。然而，千兆级的计算成本规模筛选成为限制筛选文库进一步增长的主要瓶颈。最近，我们有推出了一种高度可扩展的基于合成子的技术，V-SYNTHES，它执行分层结构- 基于 REadily AvaiLable 用于合成 (REAL) 文库的筛选（Sadybekov 等人，Nature 接受）。通过迭代筛选合成子-支架组合，V-SYNTHES 方法使快速合成成为可能检测千兆级化学空间中得分最高的化合物，同时仅对图书馆的一小部分（约 200 万）。 V-SYNTHES 的首次测试表明，在计算基准并显着提高大麻素受体 CB2 和的实验命中率 ROCK1 激酶目标，同时所需的计算资源比标准虚拟筛选少 100 倍。基于这些初步结果，我们的建议旨在： (1) 进一步开发全自动 V- SYNTHES算法，优化其参数并将其扩展为Tera规模的REAL库。 (2) 申请并对一组不同类别的治疗靶点进行实验验证 V-SYNTHES 方法，包括诸如核苷酸和脂质结合口袋、变构口袋和孤儿等具有挑战性的目标（3）建立算法到开源对接平台的可移植性，进一步方便V- SYNTHES 在学术实验室中的采用。开源算法将作为 Linux 的工作流程进行分发集群和计算云。该项目的成功完成将使 V-SYNTHES 成为一个强大的用于大多数类别治疗靶标中基于结构的配体发现的计算平台，可扩展快速增长的 REAL 模块化库。最重要的是，它将有助于快速虚拟筛选整个研究界可以通过合理的计算广泛访问千兆至万亿级的库资源。