Deep learning augmented protein mapping software to screen large compound libraries

深度学习增强蛋白质图谱软件可筛选大型化合物库

• 批准号: 10382809
• 负责人: Dmitri Beglov
• 金额: $ 17.3万
• 依托单位: ACPHARIS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10382809
• 关键词: 3-Dimensional; Affinity; Algorithms; Binding; Binding Sites; Blinded; Boston; Communities; Complex; Computer software; Consensus; Data; Development; Discrimination; Docking; Fourier Transform; Geometry; Goals; Hot Spot; Libraries; Licensing; Ligands; Methods; Molecular; Molecular Conformation; Molecular Probes; Pharmaceutical Preparations; Positioning Attribute; Proteins; Roentgen Rays; Sampling; Seeds; Shapes; Site; Software Validation; Solvents; Structure; Training; United States National Institutes of Health; Universities; Update; analog; base; computational chemistry; computerized tools; convolutional neural network; deep learning; density; design; drug discovery; experimental study; functional group; improved; innovation; network architecture; novel; pharmacophore; preference; programs; receptor; screening; tool; virtual

Fragment based drug discovery starts with screening libraries of fragment-sized organic molecules for binding to the target protein. The fragments cluster at binding hot spots, the most important regions for drug discovery, and can be extended into larger and higher affinity ligands. The protein-mapping program FTMap is a computational analogue of fragment screening experiments. Acpharis has licensed the docking engine of FTMap and developed the ATLAS software as an updated version of the FTMap program. While ATLAS is a useful tool for identifying binding sites and predicting druggability, with proper development it can provide much more valuable characterization of both the binding site and the preferred fragments. The major goal of this proposal is to develop a software package based on ATLAS that, starting from the structure of a target protein, will be able to reliably screen very large virtual compound libraries for potential hits. To achieve this major goal we propose the following developments. Our first goal is to identify regions on the target protein that have preferences for binding specific functional groups and to identify a set of bound fragments that can be used as seeds for 2D and 3D screening. This will involve four steps. (1) Developing a higher accuracy scoring function to enable discrimination among different functional groups. (2) Obtaining generalized pharmacophore information by iterative mapping, where the initial mapping, indicating preferences for certain functional groups, will be followed by more focused mapping using probes containing similar functional groups; (3) designing basic and extended fragment libraries for the two steps of mapping; and (4) improving the functional characterization of the site by adding binding information from the PDB using a novel pocket similarity algorithm. Once extended pharmacophores are established, we plan to use ensembles of binding fragments as pseudo-compounds to seed a ligand-based shape-matching search method to screen large libraries of compounds based on molecular similarity. The traditional 2D similarity search will be modified to account for the additional 3D information provided by the mapping. This will enable screening larger libraries and will yield more specific results than the existing 2D ligand based tools. Once we have a set of potential ligand hits, we will perform template based ligand placement to produce a variety of possible poses, and to score the refined poses.

基于片段的药物发现始于筛选片段大小的有机化合物库 与靶蛋白结合的分子。碎片聚集在结合热点处， 药物发现最重要的区域，并且可以扩展到更大和更广泛的区域 更高亲和力的配体。蛋白质图谱程序 FTMap 是一个计算 片段筛选实验的类似物。 Acpharis 已获得对接引擎许可 FTMap 并开发了 ATLAS 软件作为 FTMap 的更新版本 程序。虽然 ATLAS 是识别结合位点和预测的有用工具 成药性，通过适当的开发，它可以提供更有价值的 结合位点和首选片段的表征。主要目标是 这个建议是开发一个基于ATLAS的软件包，从 目标蛋白质的结构，将能够可靠地筛选非常大的虚拟化合物 潜在热门的库。为实现这一重大目标，我们提出以下建议 事态发展。我们的第一个目标是识别目标蛋白上具有 结合特定官能团并识别一组结合的偏好 可用作 2D 和 3D 筛选种子的片段。这将涉及四 步骤。 (1) 开发更高精度的评分函数，以实现区分 不同的功能组。 (2) 获取广义药效团信息 迭代映射，其中初始映射，指示对某些功能的偏好 组，随后将使用包含相似的探针进行更集中的映射 功能组； (3) 设计两步的基础片段库和扩展片段库 测绘； (4) 通过添加改进站点的功能特征 使用新颖的口袋相似性算法绑定来自 PDB 的信息。一次 建立了扩展的药效团，我们计划使用结合的集合 片段作为伪化合物来种子基于配体的形状匹配搜索 基于分子相似性筛选大型化合物库的方法。这 传统的 2D 相似性搜索将被修改以考虑额外的 3D 映射提供的信息。这将能够筛选更大的文库，并将 比现有的基于 2D 配体的工具产生更具体的结果。一旦我们有了一套 潜在的配体命中，我们将执行基于模板的配体放置以产生 各种可能的姿势，并对精致的姿势进行评分。