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文库