{"title":"DeepKa Protein pKa Database: Identifying pH Dependence in the Protein Data Bank","authors":"Baorong Yang, Xiangxiang Lu, Zhitao Cai, Jiawen Sun, Yandong Huang","doi":"10.1021/acs.jcim.6c01937","DOIUrl":null,"url":null,"abstract":"<p>pH plays a central role in many biological events. To\r\nexplore the\r\nunderlying molecular mechanisms, it is of importance to determine\r\np<em>K</em><sub>a</sub> values of ionizable residues in proteins.\r\nIn this work, DeepKa, a deep learning-based protein p<em>K</em><sub>a</sub> predictor, was employed to create the p<em>K</em><sub>a</sub> database DeepKa DB (http://computbiophys.com/DeepKa/database), which contains p<em>K</em><sub>a</sub> data of over 30\r\nmillion residues in about 200 thousand soluble proteins. The database\r\nhas been integrated into the existing DeepKa web server, from which\r\nusers can obtain the data of interest freely. In addition to the data\r\npresentation, four representative case studies were set up to demonstrate\r\nhow p<em>K</em><sub>a</sub>’s extracted from the database\r\ncould be applied to the investigation of pH-dependent processes in\r\nproteins.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12852–12861"},"PeriodicalIF":6.4000,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jcim.6c01937","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/9/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
pH plays a central role in many biological events. To
explore the
underlying molecular mechanisms, it is of importance to determine
pKa values of ionizable residues in proteins.
In this work, DeepKa, a deep learning-based protein pKa predictor, was employed to create the pKa database DeepKa DB (http://computbiophys.com/DeepKa/database), which contains pKa data of over 30
million residues in about 200 thousand soluble proteins. The database
has been integrated into the existing DeepKa web server, from which
users can obtain the data of interest freely. In addition to the data
presentation, four representative case studies were set up to demonstrate
how pKa’s extracted from the database
could be applied to the investigation of pH-dependent processes in
proteins.
pH在许多生物事件中起着核心作用。为了探索潜在的分子机制，确定蛋白质中可电离残基的pKa值是很重要的。在这项工作中，DeepKa，一个基于深度学习的蛋白质pKa预测器，被用来创建pKa数据库DeepKa DB (http://computbiophys.com/DeepKa/database)，其中包含约20万种可溶性蛋白质中超过3000万个残基的pKa数据。该数据库已集成到现有的DeepKa网络服务器中，用户可以从中自由获取感兴趣的数据。除了数据展示之外，还建立了四个具有代表性的案例研究，以演示如何从数据库中提取pKa可以应用于蛋白质中ph依赖性过程的研究。
期刊介绍：
The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery.
Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field.
As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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