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 蛋白质与配体的特定相互作用是生物学功能的起源，这对于 活生物体。这些分子识别发生在蛋白质的局部表面区域。迅速增加 在高分辨率蛋白质结构的数量和蛋白质结构预测的令人印象深刻的进步中 预计大多数生物蛋白的完整三维结构信息 很快。我们先前的研究表明，在非同源蛋白质结构中发生相似的结合位点， 从蛋白质配体复合物中预测配体结合位点和配体结构的可行 蛋白质数据库通过比较其结合位点。基于这些，我们的目标是发展高 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 蛋白质组学水平通过利用来自大生物分子的蛋白质 - 配体相互作用的局部结构模式 结构数据并通过检测蛋白质结构之间的保守局部区域。在AIM 1中，我们将发展 g-pli predictor预测配体结合位点，推定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，我们将发展G- Losalr，我们本地结构对齐工具G-Losa和G-LBS-Refiner的粗粒版版本 基于分子动力学模拟的构象采样方法，由衍生的约束电位引导 从结构模板提高结构库搜索中的性能。 G-losalr将提供宽容 蛋白质结构和结构误差的构象变化在结构上预测的蛋白质模型中 一致性和相似性测量。 G-LBS替补器将通过 通过APO结构或通过完善低分辨率蛋白质模型产生全构形成。在AIM3中，我们 将开发G-Promis，这是一种蛋白质组学的配体滥交预测方法。 G-Promis将执行所有 查询结合位点结构的结构比较与蛋白质组中每种蛋白质的整个表面 结构库以识别一组潜在的蛋白质靶标，然后检查近似结合亲和力 在查询配体和靶蛋白之间。这些Web服务和/或独立工具包将是自由的 可用于所有学术用户和非营利机构。拟议的研究将提供可靠的 生物学社区和其他学科的学生和研究人员的一般计算方法， 能够促进有关蛋白质 - 配体相互作用并促进的协同科学研究和教育 药物开发。