A Web Service for Fragment-based Selectivity Analysis of Drug Leads

基于片段的先导药物选择性分析的 Web 服务

• 批准号: 9906478
• 负责人: John Laurence Kulp III
• 金额: $ 23.47万
• 依托单位: CONIFER POINT PHARMACEUTICALS, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2022-09-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906478
• 关键词: Address; Adopted; Affinity; Algorithms; Amino Acids; Area; Benchmarking; Binding; Binding Sites; Chemicals; Clinical; Clinical Trials; Cloud Service; DHFR gene; Data; Data Set; Drug Design; Failure; Family member; Free Energy; G-Protein-Coupled Receptors; Goals; Grant; Hand; Industry; Internet; Isoenzymes; JAK2 gene; JAK3 gene; Journals; Location; Maps; Measures; Medicine; Methodology; Mutation; Online Systems; Outcome; PTGS1 gene; PTGS2 gene; Patients; Pattern; Peer Review; Peptide Hydrolases; Pharmaceutical Preparations; Phase; Phosphotransferases; Property; Protein Family; Protein Fragment; Protein Isoforms; Proteins; Publications; Publishing; Research Personnel; Resources; Services; Sirtuins; Small Business Innovation Research Grant; Software Tools; Specificity; Speed; Technology; Testing; Therapeutic; Toxic effect; United States National Institutes of Health; base; clinical candidate; design; drug discovery; enzyme pathway; improved; innovation; lead optimization; novel; programs; prototype; repository; side effect; small molecule; success; tool; web based interface; web page; web services; web site

Abstract Significance: The goal of this proposal is to provide drug researchers with compelling new tools that address off-target selectivity in the rational design of drugs. To do this, we propose to employ our large repository of maps of where chemical fragments tightly bind to 1,000’s of proteins (Boltzmann maps) in searches for differ- ences in spatial binding patterns of fragments across protein families. This will empower medicinal chemists in attacking the off-target toxicity challenge that plagues clinical candidates in drug discovery. Our team is currently supported by an NIH Phase II SBIR grant (2R44GM109549) to computationally produce BMaps on a large scale (1,000’s of fragment maps on 1,000’s of proteins) for fragment-based drug design (see www.boltzmannmaps.com). Building upon this, the need is to build fast tools for the design of selective com- pounds that search these fragment maps in comparing binding patterns across a large number of proteins. Putting this capability in the hands of medicinal chemists across the industry via the Web has the potential to significantly improve those clinical outcomes negatively impacted by off-target toxicities and speed the delivery of new medicines to patients. Innovation: Comparing chemical fragment binding across a large number of diverse isozymes within protein families, based on our unique repository of fragment binding maps, is a new scientific approach to the rational design of selectivity. To efficiently query our large repository for binding patterns requires that we devise a new geometric search algorithm. Aim 1: Develop a novel algorithm for comparing fragment binding patterns using geometric hashing, supple- mented with other parameters such as the relative free energy from the chemical potential ranking and other physiochemical properties of the local binding site. We will provide a Web-based interface for requesting and visualizing search results. Aim 2: Validate the tools developed under Aim 1 in a proof-of-concept project to assess selectivity of a proprie- tary set of 127 compounds across the sirtuin family of proteins confirming experimental data. Overall Impact: In summary, having a body of diverse fragment binding data across isozymes within protein presents a unique opportunity to attack the selectivity problem in drug discovery. By comparing fragment in- teraction patterns across a large number of proteins, never feasible before, an assessment of differential bind- ing between proteins to modulate and proteins to leave unaffected is now more practical. As widely adopted, the service would provide a key tool to reduce toxicities due to off-target interactions, resulting in improved success rates of clinical trials.

摘要 意义：该提案的目标是为药物研究人员提供引人注目的新工具， 药物合理设计中的脱靶选择性。为了做到这一点，我们建议使用我们的大型存储库， 化学碎片与1,000种蛋白质紧密结合的地图（玻尔兹曼地图）， 蛋白质家族中片段的空间结合模式。这将使药物化学家， 攻击在药物发现中困扰临床候选人的脱靶毒性挑战。 我们的团队目前得到NIH第二阶段SBIR资助（2 R44 GM 109549）的支持，以计算方式产生 用于基于片段的药物设计的大规模BMaps（1，000个蛋白质上的1，000个片段图谱）（参见 www.boltzmannmaps.com）。在此基础上，需要建立快速的工具，选择性com的设计， 他们搜索这些片段图，比较大量蛋白质的结合模式。 通过网络将这种能力交给整个行业的药物化学家， 显著改善那些受脱靶毒性负面影响的临床结局，并加快递送 给病人的新药。 创新：比较蛋白质内大量不同同工酶的化学片段结合 家族，基于我们独特的片段结合图谱库，是一种新的科学方法， 选择性设计。为了有效地查询绑定模式的大型存储库，我们需要设计一个新的 几何搜索算法 目标1：开发一种使用几何哈希比较片段结合模式的新型算法，灵活- 与其他参数，如来自化学势排名的相对自由能和其他参数一起进行了计算。 局部结合位点的理化性质。我们将提供一个基于Web的接口， 可视化搜索结果。 目标2：在一个概念验证项目中推广根据目标1开发的工具，以评估一个专有技术的选择性， 一组横跨沉默调节蛋白家族的127种化合物证实了实验数据。 总体影响：总之，在蛋白质内的同工酶中具有多种片段结合数据， 为解决药物发现中的选择性问题提供了一个独特的机会。通过比较碎片- 大量蛋白质之间的相互作用模式，以前从未可行过，对差异结合的评估， 在要调节的蛋白质和不受影响的蛋白质之间进行选择现在更实际。作为广泛采用的， 该服务将提供一个关键工具，以减少由于脱靶相互作用而产生的毒性，从而改善 临床试验的成功率。