Binding MOAD: A Database of Protein-Ligand Information

摘要：本提案是根据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技术传播”，旨在 支持信息科学软件和数据库的持续开发。在这里，我们概述了 开发我们的资源，绑定MOAD（所有数据库之母，发音为“模式”，作为一个双关语， 配体的结合模式）。MOAD是最大的高质量蛋白质配体复合物集合之一 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，所以重要的一点是，所有的蛋白质-配体复合物 包含PDB中所有生物体的MOAD，使MOAD适用于人类健康的所有领域， 生物医学研究MOAD中的复合物是为了纠正错误， 从辅因子和晶体添加剂的相关配体，并注释具有结合亲和力的复合物 数据可用时。精心策划的数据对于严谨、可重复的科学至关重要。此外，MOAD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是一个丰富的数据集，对科学界产生了重大影响。数据库和网站 （www.BindingMOAD.org）在科学文献中被引用了数百次。网站接收 每年约有25，000人次访问。MOAD的点击率为510次/周，低于BindingDB或ZINC的流量，但高于 比Shoichet的SEA、DOCK Blaster或DUD增强（DUDE）实用程序的流量更大。资源与在线 工具被广泛的科学学科所使用：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来检查 分子识别模式，阐明酶的机制，开发基于结构的方法 研究，预测毒理学，并开发新的蛋白质折叠程序，包括辅因子和配体。 我们的长期目标是为计算生物学提供工具，以满足用户多样化的科学需求， 帮助发现新的关系，并从大型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，这一提议的新影响是将化学和生物学联系起来。 以新的方式揭示潜在的多药理学网络。我们的假设是 配体可能结合到相同的结合位点，相反，类似的结合位点可能结合配体。 相同的小分子。为了使类似配体和口袋之间的联系更容易为用户，我们 我建议使用“化学相似树”来显示新的配体-靶点配对， 意义我们还将为MOAD创建多药理学维基页面。潜在的配体-靶标配对将 我们将向我们的用户群开放，我们将促进众包他们的各种生物医学专业知识。

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.