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

基本信息

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
项目类别：
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

项目摘要

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.

含义：选择性药物结合的目标是一个基本目标和优化轨迹上的轨迹，以帮助患者并进行验证。目标，我们的提议旨在全面商业化的工具网络解决目标的选择性。符号方法。在治疗上相关的蛋白质。 NIH优先病原体列表，所有SARS-COV-2结构以及最高寿命的近100个结构科学风险投资公司。视力在药物中设计选择性或泛选择性。 Conifer Point的主要产品BMAPS由NIH SBIR赠款支持，并将在商业上发行 2022年。该产品具有最大的碎片结合数据存储库，负担得起/准确的水分子地图，并与其他标准化学工具集成片段地图，一种由云计算支持的Web服务 - 在合理设计中提供数据阳离子。化合物如何以及为什么与多种tarteins相互作用，并通过探索建议的化合物相互作用从多个tartein的化学片段结合图中得出的修饰。亲和力和更多选择性的组合，专门利用特定蛋白质的绑定细节或蛋白质家族。创新：迄今片段图。 FFECT。秒并使用易于使用的工具在几分钟内评估了数百种蛋白质蛋白质的化合物。方法：我们的方法遵循与我们先前的工作相似的轨迹。重要的治疗蛋白家族的片段图（> 100万张图）。 tata，允许数百种蛋白质的选择性设计。使用开源和专有数据集的工具，包括> 600个抑制剂的独特范围内数据集。总体影响：毒品选择性是Proclinical进展的重要且根本的障碍铅。发现并减少因脱靶相互作用而导致的毒性，从而提高了临床试验的成功率。