BindingDB: An Open Knowledgebase of Protein-Small Molecule Interactions

BindingDB：蛋白质-小分子相互作用的开放知识库

• 批准号: 10331669
• 负责人: MICHAEL K. GILSON
• 金额: $ 54.14万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10331669
• 关键词: Area; Artificial Intelligence; Benchmarking; Binding; Binding Proteins; Biological; Biology; Biomedical Research; Cell physiology; Certification; Chemicals; Chemistry; Communities; Computer software; Data; Data Analyses; Data Set; Disease; Documentation; Drug Design; Educational process of instructing; Educational workshop; Electronic Mail; FAIR principles; Future Teacher; Government; Human; Information Resources; Knowledge; Legal patent; Ligands; Link; Machine Learning; Measurement; Measures; Methods; Molecular Weight; Natural Language Processing; Organism; Pathway Analysis; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Physicians; Practice Management; Proteins; Publications; Publishing; Readability; Research; Research Personnel; Resources; Science; Scientist; Software Design; Students; Testing; Time; Training; Twitter; Universities; Update; Vocabulary; Work; computational chemistry; data management; data preservation; data resource; data reuse; drug discovery; drug repurposing; experience; high school; improved; interoperability; journal article; knowledgebase; open source; outreach; preservation; protein structure function; repository; small molecule; teacher; teaching machine; technology development; tool; trend; trustworthiness; usability; web site

Small, organic molecules that bind specific proteins represent one of the most effective ways that physicians have to treat diseases and that researchers can use to probe living systems. Such small molecules, also known as ligands, can act in many ways, such as by blocking a protein from working, by activating a protein, or by causing the protein to be broken down by normal cellular processes. In fact, most medications are ligands, and researchers in universities, government labs, and pharmaceutical companies, are constantly at work seeking new ones as drugs and biological probes. These ongoing efforts generate a continuous flow of information about what small molecules bind what proteins, and how tightly. This information is useful not only within the specific project that generated it, but also for many other applications, such as helping researchers identify probe molecules to help with their research, serving as benchmarks for computational chemists creating software designed to predict ligand-protein binding, and training and testing machine-learning tools for drug design. However, scientists generating this information typically release it in scientific articles or patents, where it cannot easily be found or accessed by other researchers. The core purpose of this project is to further develop the BindingDB Knowledgebase, dramatically expanding the availability of protein-ligand binding information and connecting this information to other areas of knowledge in order to make it as broadly useful as possible. This will be accomplished by using a combination of automated and human methods to carry out fast, accurate extraction of large volumes of data from scientific articles and patents. These data will be rendered in machine readable format, linked with related data, such as information on protein structure and function, and made publicly available in open source format via the searchable BindingDB website, which also allows data to be downloaded in quantity for offline use. The information in BindingDB will be managed according to high community standards for findability, accessibility, interoperability, and reusability (FAIR), and the project will achieve the high CoreTrustSeal standards and certification for reliability and long-term preservation. In addition, steps will be taken to maximize usability and integration of this information, such as by making it available as a public dataset in emerging cloud resources and creating links from on- line journal articles and patents to the data extracted from them in BindingDB.

结合特定蛋白质的有机小分子是最有效的方法之一 医生可以用它来治疗疾病，研究人员可以用它来探测生命系统。 这种小分子，也称为配体，可以通过多种方式发挥作用，例如通过阻断 通过激活蛋白质或导致蛋白质被分解，蛋白质无法发挥作用 正常的细胞过程。事实上，大多数药物都是配体，研究人员 大学、政府实验室和制药公司一直在努力寻找 新的药物和生物探针。这些持续的努力产生了源源不断的 有关哪些小分子与哪些蛋白质结合以及结合程度的信息。 此信息不仅在生成它的特定项目中有用，而且对于许多 其他应用，例如帮助研究人员识别探针分子以帮助他们 研究，作为计算化学家创建软件的基准 预测配体-蛋白质结合，以及训练和测试用于药物设计的机器学习工具。 然而，生成这些信息的科学家通常会在科学文章或专利中发布它， 其他研究人员无法轻易找到或访问它。 该项目的核心目的是进一步开发BindingDB知识库， 极大地扩展了蛋白质-配体结合信息的可用性并将其连接起来 将信息传递给其他知识领域，以使其尽可能广泛地发挥作用。这 将通过使用自动化和人工方法的结合来完成 从科学文章和专利中快速、准确地提取大量数据。这些数据 将以机器可读的格式呈现，并与相关数据链接，例如有关信息 蛋白质结构和功能，并通过开源格式公开提供 可搜索的BindingDB网站，还可以离线批量下载数据 使用。 BindingDB中的信息将按照高社区标准进行管理 可查找性、可访问性、互操作性和可重用性（公平），并且该项目将实现 高 CoreTrustSeal 标准和认证，确保可靠性和长期保存。在 此外，将采取措施最大限度地提高这些信息的可用性和集成度，例如通过 使其成为新兴云资源中的公共数据集，并从本地创建链接 将期刊文章和专利与从 BindingDB 中提取的数据进行关联。