Binding MOAD: A Database of Protein-Ligand Information

结合 MOAD：蛋白质配体信息数据库

• 批准号: 9367088
• 负责人: HEATHER A CARLSON
• 金额: $ 34.98万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9367088
• 关键词: Affinity; Area; Atlases; Back; Big Data; Binding; Binding Proteins; Binding Sites; Bioinformatics; Biological; Biomedical Computing; Biomedical Research; Biophysics; Chemicals; Chemistry; Collection; Communities; Complex; Computational Biology; Data; Data Science; Data Set; Databases; Development; Discipline; Distal; Docking; Drug Design; Ensure; Event; Foundations; Goals; Gold; Growth; Health; Human; Individual; Informatics; Institutes; Intuition; Ligand Binding; Ligands; Link; Literature; Mathematics; Methods; Mothers; Organism; Pattern; Pharmaceutical Chemistry; Population; Protein Databases; Proteins; Reproducibility; Resource Informatics; Resources; Science; Scientist; Security Measures; Sequence Alignment; Site; Solid; Structure; Technology; Time; Toxicology; Trees; United States National Institutes of Health; Visit; Visual; base; biomedical informatics; cheminformatics; cofactor; computer science; computerized tools; crowdsourcing; data integrity; design; drug discovery; electron density; enzyme mechanism; improved; interest; interoperability; method development; molecular recognition; novel; novel therapeutics; operation; predictive tools; protein folding; receptor; response; small molecule; software development; statistics; structural biology; text searching; tool; web site; wiki

ABSTRACT: This proposal is submitted in response to PA 14-156 “Extended Development, Hardening, and Dissemination of Technologies in Biomedical Computing, Informatics, and Big Data Science” which aims to support continued development of software and databases for informatics science. Here, we outline the development of our resource, Binding MOAD (Mother of All Databases, pronounced ``mode'' as a pun on binding modes for ligands). MOAD is one of the largest collections of high-quality, protein-ligand complexes available from the scientific literature and the Protein Data Bank (PDB). PA 14-156 notes that projects should be of interests to most NIH Institutes and Centers, so it is an important point that all protein-ligand complexes from all organisms in the PDB are included, making MOAD applicable to all areas of human health and biomedical research. The complexes in MOAD are curated to correct errors, to differentiate biologically relevant ligands from cofactors and crystallographic additives, and to annotate complexes with binding affinity data when available. Curated data is essential for rigorous, reproducible science. Furthermore, MOAD's HiQ subset is the gold standard for docking calculations, and it sets a solid foundation for method development. MOAD is a rich dataset with significant impact on the scientific community. The database and website (www.BindingMOAD.org) have been cited hundreds of times in the scientific literature. The website receives ~25,000 visits each year. MOAD's rate of 510 hits/wk is less than the traffic at BindingDB or ZINC, but more than the traffic to Shoichet's SEA, DOCK Blaster, or DUD enhanced (DUDE) utilities. The resource and on-line tools are used by a wide range of scientific disciplines: bioinformatics, structural biology, biophysics, protein science, medicinal chemistry, theoretical chemistry, and computer science. Scientists use MOAD to examine patterns of molecular recognition, elucidate enzyme mechanisms, develop methods for structure-based studies, predict toxicology, and develop new protein-folding routines that incorporate cofactors and ligands. Our long-term goal is to provide tools for computational biology that meet users' diverse scientific needs, help uncover new relationships, and inspire new hypotheses from large datasets. Guided by structural biology and cheminformatics we can filter the PDB's Big Data into intuitive patterns of ligand and receptor similarity. Beyond the intrinsic value of the data itself, the novel impact of this proposal is the linking of chemical and biological data in novel ways to reveal potential polypharmacology networks. Our hypothesis is that similar ligands are likely to bind to the same binding sites, and conversely, similar binding sites are likely to bind the same small molecules. To make the links between similar ligands and pockets more accessible to the user, we propose using “chemical similarity trees” to display new ligand-target pairings with potential biological significance. We will also create polypharmacology wiki pages for MOAD. Potential ligand-target pairings will be available to our user base, and we will facilitate crowd-sourcing their diverse biomedical expertise.

摘要：本提案是为了响应 PA 14-156“扩展开发、强化和 生物医学计算、信息学和大数据科学技术的传播”，旨在 支持信息学软件和数据库的持续开发在这里，我们概述了。 我们的资源 Binding MOAD（所有数据库之母，发音为“mode”）的双关语 MOAD 是最大的高质量蛋白质-配体复合物集合之一。 可从科学文献和蛋白质数据库 (PDB) 获取，指出项目应 大多数 NIH 研究所和中心都感兴趣，因此重要的一点是所有蛋白质-配体复合物 PDB 中的所有生物体都包含在内，使得 MOAD 适用于人类健康的所有领域 MOAD 中的复合物被编译以纠正错误，以进行生物学区分。 来自辅因子和晶体学添加剂的相关配体，并用结合亲和力注释复合物 可用的数据对于严谨、可重复的科学来说至关重要。 子集是对接计算的黄金标准，为方法开发奠定了坚实的基础。 MOAD 是一个丰富的数据集，对科学界具有重大影响的数据库和网站。 (www.BindingMOAD.org) 已在科学文献中被引用数百次。 MOAD 每年约 25,000 次访问，每周 510 次点击，低于 BindingDB 或 ZINC 的流量，但更多。 与 Shoichet 的 SEA、DOCK Blaster 或 DUD 增强型 (DUDE) 实用程序的流量相比。 工具被广泛的科学学科使用：生物信息学、结构生物学、生物物理学、蛋白质 科学、药物化学、理论化学和计算机科学科学家使用 MOAD 进行检查。 分子识别模式，阐明酶机制，开发基于结构的方法 研究、预测毒理学并开发包含辅因子和配体的新蛋白质折叠程序。 我们的长期目标是为计算生物学提供工具，满足用户多样化的科学需求， 在结构生物学的指导下，帮助发现新的关系，并从大型数据集中激发新的假设。 通过化学信息学，我们可以将 PDB 的大数据过滤为配体和受体相似性的直观模式。 除了数据本身的内在价值之外，该提案的新颖影响是将化学和 我们的假设是，以新颖的方式揭示潜在的多药理学网络的生物数据。 配体可能结合相同的结合位点，相反，相似的结合位点可能结合 为了使用户更容易连接相似的配体和口袋之间的连接，我们 建议使用“化学相似性树”来显示具有潜在生物活性的新配体-靶标配对 我们还将为 MOAD 创建潜在的配体-靶标配对。 可供我们的用户群使用，我们将促进众包他们多样化的生物医学专业知识。