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

用于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：
10603646
负责人：
John Laurence Kulp III
金额：
$ 101.65万
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/10603646
关键词：
2019-nCoV Achievement Address Affinity Algorithms Back 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 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 Vision Visualization Water Work base clinical candidate cloud platform computational chemistry cost data tools design drug discovery enzyme pathway experience improved inhibitor innovation open source 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.

意义：药物选择性结合是药物发现和优化的基本目标