DrugDomain 2.0:横跨整个蛋白质数据库的蛋白质结构域-配体/药物相互作用的综合数据库。

IF 4.1 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-09-12 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.09.018
Kirill E Medvedev, R Dustin Schaeffer, Nick V Grishin
{"title":"DrugDomain 2.0:横跨整个蛋白质数据库的蛋白质结构域-配体/药物相互作用的综合数据库。","authors":"Kirill E Medvedev, R Dustin Schaeffer, Nick V Grishin","doi":"10.1016/j.csbj.2025.09.018","DOIUrl":null,"url":null,"abstract":"<p><p>Proteins carry out essential cellular functions - signaling, metabolism, transport - through the specific interaction of small molecules and drugs within their three-dimensional structural domains. Protein domains are conserved folding units that, when combined, drive evolutionary progress. The Evolutionary Classification Of protein Domains (ECOD) places domains into a hierarchy explicitly built around distant evolutionary relationships, enabling the detection of remote homologs across the proteomes. Yet no single resource has systematically mapped domain-ligand interactions at the structural level. To fill this gap, we introduce DrugDomain v2.0, an updated comprehensive resource, that extends earlier releases by linking evolutionary domain classifications (ECOD) to ligand binding events across the entire Protein Data Bank. We also leverage AI-driven predictions from AlphaFold to extend domain-ligand annotations to human drug targets lacking experimental structures. DrugDomain v2.0 catalogs interactions with over 37,000 PDB ligands and 7560 DrugBank molecules, integrates more than 6000 small-molecule-associated post-translational modifications, and provides context for 14,000 + PTM-modified human protein models featuring docked ligands. The database encompasses 43,023 unique UniProt accessions and 174,545 PDB structures. The DrugDomain data is available online: https://drugdomain.cs.ucf.edu/ and https://github.com/kirmedvedev/DrugDomain.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"4040-4047"},"PeriodicalIF":4.1000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475577/pdf/","citationCount":"0","resultStr":"{\"title\":\"DrugDomain 2.0: Comprehensive database of protein domains-ligands/drugs interactions across the whole Protein Data Bank.\",\"authors\":\"Kirill E Medvedev, R Dustin Schaeffer, Nick V Grishin\",\"doi\":\"10.1016/j.csbj.2025.09.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Proteins carry out essential cellular functions - signaling, metabolism, transport - through the specific interaction of small molecules and drugs within their three-dimensional structural domains. Protein domains are conserved folding units that, when combined, drive evolutionary progress. The Evolutionary Classification Of protein Domains (ECOD) places domains into a hierarchy explicitly built around distant evolutionary relationships, enabling the detection of remote homologs across the proteomes. Yet no single resource has systematically mapped domain-ligand interactions at the structural level. To fill this gap, we introduce DrugDomain v2.0, an updated comprehensive resource, that extends earlier releases by linking evolutionary domain classifications (ECOD) to ligand binding events across the entire Protein Data Bank. We also leverage AI-driven predictions from AlphaFold to extend domain-ligand annotations to human drug targets lacking experimental structures. DrugDomain v2.0 catalogs interactions with over 37,000 PDB ligands and 7560 DrugBank molecules, integrates more than 6000 small-molecule-associated post-translational modifications, and provides context for 14,000 + PTM-modified human protein models featuring docked ligands. The database encompasses 43,023 unique UniProt accessions and 174,545 PDB structures. The DrugDomain data is available online: https://drugdomain.cs.ucf.edu/ and https://github.com/kirmedvedev/DrugDomain.</p>\",\"PeriodicalId\":10715,\"journal\":{\"name\":\"Computational and structural biotechnology journal\",\"volume\":\"27 \",\"pages\":\"4040-4047\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475577/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational and structural biotechnology journal\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.csbj.2025.09.018\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and structural biotechnology journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.csbj.2025.09.018","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

蛋白质通过其三维结构域内的小分子和药物的特定相互作用来执行基本的细胞功能——信号传导、代谢、运输。蛋白质结构域是保守的折叠单元,当它们结合在一起时,推动了进化进程。蛋白质结构域的进化分类(ECOD)将结构域置于明确建立在遥远进化关系周围的层次结构中,从而能够检测跨蛋白质组的远程同源物。然而,没有单一的资源系统地在结构水平上绘制了结构域-配体相互作用。为了填补这一空白,我们推出了DrugDomain v2.0,这是一个更新的综合资源,通过将整个蛋白质数据库中的进化结构域分类(ECOD)与配体结合事件联系起来,扩展了早期版本。我们还利用AlphaFold的人工智能驱动预测,将域配体注释扩展到缺乏实验结构的人类药物靶标。DrugDomain v2.0收录了超过37000种PDB配体和7560种DrugBank分子的相互作用,整合了6000多种小分子相关的翻译后修饰,并提供了14000种以对接配体为特征的ptm修饰的人类蛋白质模型的背景。该数据库包含43,023个唯一的UniProt访问和174,545个PDB结构。DrugDomain的数据可以在https://drugdomain.cs.ucf.edu/和https://github.com/kirmedvedev/DrugDomain上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DrugDomain 2.0: Comprehensive database of protein domains-ligands/drugs interactions across the whole Protein Data Bank.

Proteins carry out essential cellular functions - signaling, metabolism, transport - through the specific interaction of small molecules and drugs within their three-dimensional structural domains. Protein domains are conserved folding units that, when combined, drive evolutionary progress. The Evolutionary Classification Of protein Domains (ECOD) places domains into a hierarchy explicitly built around distant evolutionary relationships, enabling the detection of remote homologs across the proteomes. Yet no single resource has systematically mapped domain-ligand interactions at the structural level. To fill this gap, we introduce DrugDomain v2.0, an updated comprehensive resource, that extends earlier releases by linking evolutionary domain classifications (ECOD) to ligand binding events across the entire Protein Data Bank. We also leverage AI-driven predictions from AlphaFold to extend domain-ligand annotations to human drug targets lacking experimental structures. DrugDomain v2.0 catalogs interactions with over 37,000 PDB ligands and 7560 DrugBank molecules, integrates more than 6000 small-molecule-associated post-translational modifications, and provides context for 14,000 + PTM-modified human protein models featuring docked ligands. The database encompasses 43,023 unique UniProt accessions and 174,545 PDB structures. The DrugDomain data is available online: https://drugdomain.cs.ucf.edu/ and https://github.com/kirmedvedev/DrugDomain.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信