DIRseq as a method for predicting drug-interacting residues of intrinsically disordered proteins from sequences.

IF 6.4 1区 生物学 Q1 BIOLOGY
eLife Pub Date : 2025-10-07 DOI:10.7554/eLife.107470
Matt MacAinsh, Sanbo Qin, Huan-Xiang Zhou
{"title":"DIRseq as a method for predicting drug-interacting residues of intrinsically disordered proteins from sequences.","authors":"Matt MacAinsh, Sanbo Qin, Huan-Xiang Zhou","doi":"10.7554/eLife.107470","DOIUrl":null,"url":null,"abstract":"<p><p>Intrinsically disordered proteins (IDPs) are now well-recognized as drug targets. Identifying drug-interacting residues is valuable for both optimizing compounds and elucidating the mechanism of action. Currently, NMR chemical shift perturbation and all-atom molecular dynamics (MD) simulations are the primary tools for this purpose. Here, we present DIRseq, a fast method for predicting drug-interacting residues from the amino-acid sequence. All residues contribute to the propensity of a particular residue to be drug-interacting; the contributing factor of each residue has an amplitude that is determined by its amino-acid type and attenuates with increasing sequence distance from the particular residue. DIRseq predictions match well with drug-interacting residues identified by NMR chemical shift perturbation and other methods, including residues L<sub>22</sub>WK<sub>24</sub> and Q<sub>52</sub>WFT<sub>55</sub> in the tumor suppressor protein p53. These successes augur well for deciphering the sequence code for IDP-drug binding. DIRseq is available as a web server at https://zhougroup-uic.github.io/DIRseq/ and has many applications, such as virtual screening against IDPs and designing IDP fragments for in-depth NMR and MD studies.</p>","PeriodicalId":11640,"journal":{"name":"eLife","volume":"14 ","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12503486/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"eLife","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.7554/eLife.107470","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Intrinsically disordered proteins (IDPs) are now well-recognized as drug targets. Identifying drug-interacting residues is valuable for both optimizing compounds and elucidating the mechanism of action. Currently, NMR chemical shift perturbation and all-atom molecular dynamics (MD) simulations are the primary tools for this purpose. Here, we present DIRseq, a fast method for predicting drug-interacting residues from the amino-acid sequence. All residues contribute to the propensity of a particular residue to be drug-interacting; the contributing factor of each residue has an amplitude that is determined by its amino-acid type and attenuates with increasing sequence distance from the particular residue. DIRseq predictions match well with drug-interacting residues identified by NMR chemical shift perturbation and other methods, including residues L22WK24 and Q52WFT55 in the tumor suppressor protein p53. These successes augur well for deciphering the sequence code for IDP-drug binding. DIRseq is available as a web server at https://zhougroup-uic.github.io/DIRseq/ and has many applications, such as virtual screening against IDPs and designing IDP fragments for in-depth NMR and MD studies.

Abstract Image

Abstract Image

Abstract Image

DIRseq作为一种从序列中预测内在无序蛋白的药物相互作用残基的方法。
内在无序蛋白(IDPs)是目前公认的药物靶点。鉴定药物相互作用残基对优化化合物和阐明作用机制具有重要意义。目前,核磁共振化学位移微扰和全原子分子动力学(MD)模拟是实现这一目的的主要工具。在这里,我们提出了DIRseq,一种从氨基酸序列预测药物相互作用残基的快速方法。所有的残基都有助于特定残基与药物相互作用的倾向;每个残基的贡献因子的振幅由其氨基酸类型决定,并随着与特定残基的序列距离的增加而衰减。DIRseq预测结果与核磁共振化学位移扰动等方法鉴定的药物相互作用残基吻合良好,包括肿瘤抑制蛋白p53中的L22WK24和Q52WFT55残基。这些成功预示着idp药物结合序列密码的破解。DIRseq作为web服务器可在https://zhougroup-uic.github.io/DIRseq/上获得,并且有许多应用程序,例如针对IDP的虚拟筛选和为深入的NMR和MD研究设计IDP片段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
eLife
eLife BIOLOGY-
CiteScore
12.90
自引率
3.90%
发文量
3122
审稿时长
17 weeks
期刊介绍: eLife is a distinguished, not-for-profit, peer-reviewed open access scientific journal that specializes in the fields of biomedical and life sciences. eLife is known for its selective publication process, which includes a variety of article types such as: Research Articles: Detailed reports of original research findings. Short Reports: Concise presentations of significant findings that do not warrant a full-length research article. Tools and Resources: Descriptions of new tools, technologies, or resources that facilitate scientific research. Research Advances: Brief reports on significant scientific advancements that have immediate implications for the field. Scientific Correspondence: Short communications that comment on or provide additional information related to published articles. Review Articles: Comprehensive overviews of a specific topic or field within the life sciences.
×
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