Development of Computational Toolset for Structural Systems Pharmacology

结构系统药理学计算工具集的开发

• 批准号: 9419966
• 负责人: Wonpil Im
• 金额: $ 35.5万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9419966
• 关键词: Affinity; Binding; Binding Sites; Biological Process; Biology; Chemicals; Communities; Complex; Computer Simulation; Computing Methodologies; Data; Development; Dimensions; Discipline; Drug Design; Education; Experimental Models; Fostering; G-substrate; Goals; Grain; Institution; Libraries; Ligand Binding; Ligands; Measurement; Measures; Methods; Modeling; Molecular; Molecular Conformation; Organism; Pathway interactions; Pattern; Performance; Pharmacology; Positioning Attribute; Prevention; Protein Family; Protein Region; Proteins; Proteome; Proteomics; Research; Research Personnel; Resolution; Sampling; Set protein; Structure; Students; Surface; System; Variant; base; computer infrastructure; computerized tools; data warehouse; design; drug development; drug discovery; experience; human disease; improved; interest; molecular dynamics; molecular recognition; open source; predictive modeling; protein function; protein structure; protein structure prediction; restraint; tool; virtual; web services

PROJECT SUMMARY/ABSTRACT The specific interactions of proteins with their ligands are an origin of biological functions that are essential for living organisms. These molecular recognitions occur in local surface regions of proteins. With a rapid increase in the number of high-resolution protein structures and impressive advances in protein structure prediction, complete three-dimensional structural information of most organismal proteins is expected to be available soon. Our previous study indicates that similar binding sites occur in non-homologous protein structures, making it feasible to predict ligand binding sites and ligand structures from protein-ligand complex structures in the Protein Data Bank by comparing their binding sites. Based on these, our goal is to develop a high- performance computational toolset for structure-based protein-ligand interaction studies and drug discovery at the proteomic level by utilizing the local structural patterns of protein-ligand interactions from big biomolecular structure data and by detecting conserved local regions between protein structures. In AIM 1, we will develop G-PLI-Predictor to predict ligand binding sites, putative ligand structures, and protein functions for hard targets and to design new ligands using a chemical fragment template-based approach. In AIM 2, we will develop G- LoSALR, a coarse-grained version of our local structure alignment tool G-LoSA, and G-LBS-Refiner, a molecular dynamics simulation-based conformation sampling method guided by restraint potentials derived from structure templates to improve performance in structure library search. G-LoSALR will provide tolerance to conformational variations in protein structures and structural errors in predicted protein models upon structure alignment and similarity measurement. G-LBS-Refiner will provide more reliable binding site conformations by generating holo-conformations from an apo-structure or by refining low-resolution protein models. In AIM3, we will develop G-Promis, a proteomic-scale ligand promiscuity prediction method. G-Promis will perform all structure comparisons of a query binding site structure with the whole surfaces of each protein in the proteome structure library to identify a set of potential protein targets and then examine approximate binding affinities between a query ligand and the target proteins. These web services and/or standalone toolkits will be freely available to all academic users and not-for-profit institutions. The proposed research will provide reliable and general computational methods to students and researchers in the biology community and other disciplines, enabling to foster synergistic scientific research and education on protein-ligand interactions and facilitating drug development.

项目摘要/摘要 蛋白质与其配体的特定相互作用是生物功能的起源，这些功能对 活着的有机体。这些分子识别发生在蛋白质的局部表面区。以快速的增长 在高分辨率蛋白质结构的数量和蛋白质结构预测方面的令人印象深刻的进展方面， 大多数生物蛋白质的完整三维结构信息有望可用 很快。我们之前的研究表明，类似的结合位点存在于非同源蛋白质结构中， 使从蛋白质-配体复合体结构中预测配体结合部位和配体结构成为可能 通过比较它们的结合位点，建立蛋白质数据库。在此基础上，我们的目标是开发一种高性能的 基于结构的蛋白质-配体相互作用研究和药物发现的性能计算工具集，请访问 利用生物大分子蛋白质-配体相互作用的局部结构模式在蛋白质组水平上的应用 通过检测蛋白质结构之间的保守局部区域。在目标1中，我们将开发 G-PLI预测硬靶的配体结合部位、可能的配体结构和蛋白质功能 并使用基于化学片段模板的方法设计新的配体。在AIM 2中，我们将开发G- LoSALR是我们的本地结构对齐工具G-LOSA的粗粒度版本，而G-LBS-Refiner是一个 基于分子动力学模拟的约束势引导构象采样方法 从结构模板中提高结构库搜索的性能。G-LoSALR将对 蛋白质结构的构象变化与结构预测蛋白质模型的结构误差 比对和相似性度量。G-LBS-Refiner通过以下方式提供更可靠的结合位点构象 从脱辅基结构或通过改进低分辨率蛋白质模型来生成全息构象。在AIM3中，我们 将开发G-PROMIS，一种蛋白质组尺度的配体混杂预测方法。G-Promis将执行所有 查询结合位点结构与蛋白质组中每种蛋白质整个表面的结构比较 结构文库，以确定一组潜在的蛋白质靶点，然后检查近似结合亲和力 在查询配体和目标蛋白之间。这些Web服务和/或独立工具包将免费 适用于所有学术用户和非营利性机构。拟议的研究将提供可靠和 为生物界和其他学科的学生和研究人员提供通用计算方法， 促进关于蛋白质-配体相互作用的协同科学研究和教育，并促进 药物开发。