Binding MOAD: A Database of Protein-Ligand Information

摘要：本提案是为响应PA 14-156《扩展开发、硬化和 生物医学计算、信息学和大数据科学中的技术传播“，旨在 支持继续开发用于信息学科学的软件和数据库。在这里，我们概述一下 开发我们的资源，BINDING MOAD(所有数据库之母，发音为‘MODE’，意思是双关语 配体的结合模式)。MOAD是最大的高质量蛋白质-配体复合体集合之一 可从科学文献和蛋白质数据库(PDB)获得。PA 14-156指出，项目应 大多数NIH研究所和中心都感兴趣，所以所有蛋白质-配体复合体是一个重要的点 包括来自PDB中所有生物体的数据，使MOAD适用于人类健康的所有领域和 生物医学研究。MOAD中的复合体经过整理以纠正错误，从生物学上区分 来自辅因子和晶体添加剂的相关配体，并对具有结合亲和力的配合物进行注释 数据可用时。精选数据对于严谨、可重现的科学来说是必不可少的。此外，国防部的HiQ 子集是对接计算的黄金标准，它为方法开发奠定了坚实的基础。 MOAD是一个对科学界有重大影响的丰富数据集。数据库和网站 (www.BindingMOAD.org)在科学文献中被引用了数百次。该网站收到 每年约25,000人次。MoAD的点击率为510次/周，低于BindingDB或ZINE的流量，但更高 比Shoichet‘s Sea、Dock Blaster或DUD Enhanced(DUD)公用事业的交通流量更大。资源与在线 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的蛋白质折叠程序。 我们的长期目标是为计算生物学提供满足用户不同科学需求的工具， 帮助发现新的关系，并从大数据集中激发新的假设。以结构生物学为指导 和化学信息学，我们可以将PDB的大数据过滤成配体和受体相似性的直观模式。 除了数据本身的内在价值，这一提议的新影响是将化学和 以新的方式提供生物数据，以揭示潜在的多元药理学网络。我们的假设是类似的 配体可能结合到相同的结合部位，相反，相似的结合部位可能结合 同样的小分子。为了使相似的配体和口袋之间的链接更容易被用户访问，我们 提出用“化学相似树”来显示具有潜在生物学意义的新的配体-靶配对 意义。我们还将为MOAD创建多元药理学维基页面。潜在的配体-目标配对将 对我们的用户群可用，我们将促进他们多样化的生物医学专业知识的众包。

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.