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

意义：选择性药物结合的目标是发现和优化药物的根本目标 一种化合物在帮助患者并成为批准的药物的轨道上。在这方面提供帮助 目标，我们的提议旨在将一种创新的工具商业化，该工具解决了理性去... 签名方法。Prototype工具利用我们的大型化学碎片结合图数据库 与治疗相关的蛋白质。其中包括覆盖600多个毒品目标的100,000多张地图，其中包括 NIH优先病原体名单上的那些，所有SARS-CoV-2结构，以及来自顶级生命的近100个结构 科技风险投资公司。研究碎片的空间和能量结合模式给出了有价值的In- 着眼于设计药物的选择性或泛选择性。 Conifer Point的主要产品BMaps得到了NIH SBIR赠款的支持，并将于 2022年该产品拥有最大的片段结合数据存储库，负担得起/准确的水分子 地图，并与其他标准化学工具集成。从大数据中提取信息 片段地图是一项由云计算支持的网络服务，它在一个合理的药物设计应用程序中提供数据- 阳离子。我们的原型选择性工具BMaps-SELECT现在允许用户通过VIS-SELECT识别候选化合物。 了解化合物如何以及为什么与多个靶蛋白相互作用，并通过探索建议的化合物 来自化学片段结合的修饰映射到多个靶蛋白上。结果是更高的 亲和力和选择性更高的化合物，专门利用特定蛋白质结合位点的细节 或者蛋白质家族。BMaps和BMaps-SELECT是低成本、易于学习的，并且可以通过Web随处获得。 创新：到目前为止，还没有工具可以用来合理设计100种蛋白质的选择性 片断地图。最终的化合物评估可以对单个蛋白质进行，但这是耗时和 效率低下。我们的解决方案BMaps-SELECT为用户提供了跨100种蛋白质进行设计的潜力 使用简单易用的工具，在几秒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的工作类似的轨迹。首先，我们将预先计算一大组 重要治疗蛋白家族的片段图谱(&gt；100万图谱)。构建一个Web界面，它可以- 擦除数据，并允许对数百种蛋白质进行选择性设计。最后，我们将验证数据并 使用开源和专有数据集的工具，包括唯一的基因组范围的&gt；600抑制剂数据集。 总体影响：药物选择性是临床前进展的一个重要和根本障碍 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 并减少因非靶点相互作用而产生的毒性，从而提高临床试验的成功率。

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.