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: 10701896
• 负责人: John Laurence Kulp III
• 金额: $ 96.65万
• 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: 2020-07-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10701896
• 关键词: 2019-nCoV; Acceleration; Achievement; Address; Affinity; Algorithms; Benchmarking; Big Data; Binding; Binding Sites; Biological Sciences; Biotechnology; Capital; Case Study; Chemicals; Chemistry; Clinical Trials; Cloud Computing; Cloud Service; Collection; Coniferophyta; Consumption; DHFR gene; Data; Data Set; Databases; Doctor of Philosophy; Drug Design; Drug Targeting; Evaluation; Family; Family member; Feedback; Free Energy; Funding; Generations; Geometry; Goals; Grant; Hand; Homology Modeling; Image; Individual; Internet; Learning; Libraries; Maps; Medicine; Methodology; Modification; Mutation; PTGS1 gene; Patients; Pattern; Peptide Hydrolases; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Phosphotransferases; Phylogenetic Analysis; Protein Family; Protein Fragment; Proteins; Publications; Publishing; Research Personnel; Resources; Running; SIRT1 gene; Sales; Sampling; Scientist; Services; Sirtuins; Small Business Innovation Research Grant; Structure; Techniques; Technology; Therapeutic; Time; Toxic effect; Trees; United States National Institutes of Health; Validation; Visualization; Water; Work; candidate identification; clinical candidate; cloud platform; commercialization; computational chemistry; cost; design; drug discovery; empowerment; enzyme pathway; experience; improved; inhibitor; innovation; insight; open source; p38 Mitogen Activated Protein Kinase; pre-clinical; priority pathogen; programs; protein data bank; protein protein interaction; protein structure; prototype; rational design; repository; scale up; simulation; success; therapeutic protein; therapeutic target; tool; web app; web interface; web services

Significance: The goal of selective drug binding is a fundamental objective in the discovery and optimization of a compound on a trajectory toward helping patients and becoming an approved medication. To aid in this goal, our proposal aims to commercialize an innovative tool that addresses target selectivity in a rational de- sign methodology. The prototype tool leverages our large database of chemical fragment binding maps on therapeutically relevant proteins. These include over 100,000 maps covering over 600 drug targets including those on the NIH priority pathogen list, all SARS-CoV-2 structures, and almost 100 structures from the top life science venture capital firms. Searching spatial and energetic binding patterns of fragments gives valuable in- sights into designing selective or pan-selectivity in drugs. Conifer Point’s main product, BMaps, is supported by NIH SBIR grants and will be commercially released in 2022. The product has the largest repository of fragment binding data, affordable/accurate water molecule maps, and is integrated with other standard chemistry tools. To extract the information from the big data of fragment maps, a web service—backed by cloud computing—provides the data in a rational drug design appli- cation. Our prototype selectivity tool, BMaps-select, now allows users to identify candidate compounds by vis- ualizing how and why compounds interact with multiple target proteins, and by exploring suggested compound modifications derived from chemical fragment binding maps across multiple target proteins. The result is higher affinity and more selective compounds that specifically exploit the details of binding sites of a particular protein or protein family. BMaps and BMaps-select are low-cost, easy-to-learn, and available everywhere via the Web. Innovation: To date, no tools are available for the rational design of selectivity across 100s of proteins using fragment maps. Final compound evaluation can be done on individual proteins, but this is time consuming and inefficient. Our solution, BMaps-select, offers the potential for users to design across 100s of proteins within seconds and evaluate compounds across the same hundreds of proteins in minutes with easy-to-use tools. Approach: Our approach follows a similar trajectory to our prior work. First, we will pre-compute a large set of fragment maps (>1 million maps) for important therapeutic protein families. Build a web interface that can lev- erage the data and allow for selectivity design across hundreds of proteins. Lastly, we will validate the data and tools using open source and proprietary datasets, including a unique kinome-wide dataset of >600 inhibitors. Overall Impact: Drug selectivity is an important and fundamental obstacle in the progression of preclinical leads. BMaps-select offers the opportunity to be a first-in-class innovation to help accelerate preclinical drug discovery and to reduce toxicities due to off-target interactions, thus improving success rates of clinical trials.

意义：选择性药物结合的目标是发现和优化的基本目标。 一种化合物正在朝着帮助患者并成为批准药物的方向发展。为了帮助 目标，我们的建议旨在商业化的创新工具，解决目标的选择性，在一个合理的设计， 标志方法。该原型工具利用了我们的化学片段结合图的大型数据库， 治疗相关蛋白质。其中包括超过100，000张地图，涵盖600多个药物目标，包括 那些在NIH优先病原体名单上的，所有SARS-CoV-2结构，以及来自顶级生命的近100个结构， 科学风险投资公司。寻找碎片的空间和能量结合模式提供了有价值的- 着眼于设计药物的选择性或泛选择性。 Conifer Point的主要产品BMaps由NIH SBIR资助赠款并将于2015年商业发布。 2022.该产品拥有最大的片段结合数据库，价格实惠/准确的水分子 地图，并与其他标准化学工具集成。从大数据中提取信息， fragment maps是一种由云计算支持的网络服务，它提供了合理药物设计应用程序中的数据， 阳离子我们的原型选择性工具BMaps-select现在允许用户通过维斯识别候选化合物， 评估化合物与多种靶蛋白相互作用的方式和原因，并通过探索建议的化合物， 来自多个靶蛋白的化学片段结合图谱的修饰。结果更高 亲和性和更有选择性的化合物，专门利用特定蛋白质结合位点的细节 蛋白质家族BMaps和BMaps-select成本低，易于学习，并且可以通过Web在任何地方使用。 创新：到目前为止，还没有工具可用于合理设计100多种蛋白质的选择性， 片段映射。最终的化合物评估可以在单个蛋白质上进行，但这是耗时的， 效率低下。我们的解决方案，BMaps-select，为用户提供了设计100多个蛋白质的潜力， 只需几秒钟，并使用易于使用的工具在几分钟内评估相同数百种蛋白质的化合物。 方法：我们的方法遵循与我们之前工作相似的轨迹。首先，我们将预先计算一个大集合， 片段图谱（> 100万个图谱），用于重要的治疗性蛋白质家族。创建一个Web界面，可以... 擦除数据并允许在数百种蛋白质中进行选择性设计。最后，我们将验证数据， 使用开源和专有数据集的工具，包括超过600种抑制剂的独特激酶组数据集。 总体影响：药物选择性是临床前研究进展的重要和根本障碍 线索. BMaps-select提供了成为一流创新的机会，以帮助加速临床前药物 发现和减少由于脱靶相互作用引起的毒性，从而提高临床试验的成功率。