DIGEP-Pred 2.0:用于预测药物诱导的细胞信号传导和基因表达变化的网络应用程序。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Sergey M Ivanov, Anastasia V Rudik, Alexey A Lagunin, Dmitry A Filimonov, Vladimir V Poroikov
{"title":"DIGEP-Pred 2.0:用于预测药物诱导的细胞信号传导和基因表达变化的网络应用程序。","authors":"Sergey M Ivanov, Anastasia V Rudik, Alexey A Lagunin, Dmitry A Filimonov, Vladimir V Poroikov","doi":"10.1002/minf.202400032","DOIUrl":null,"url":null,"abstract":"<p><p>The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.</p>","PeriodicalId":18853,"journal":{"name":"Molecular Informatics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes.\",\"authors\":\"Sergey M Ivanov, Anastasia V Rudik, Alexey A Lagunin, Dmitry A Filimonov, Vladimir V Poroikov\",\"doi\":\"10.1002/minf.202400032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.</p>\",\"PeriodicalId\":18853,\"journal\":{\"name\":\"Molecular Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/minf.202400032\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/minf.202400032","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0

摘要

药物诱导基因表达谱(DIGEP)分析被广泛用于评估药物的潜在治疗和不良反应以及药物作用的分子机制。然而,许多现有药物和类药物缺乏相应的实验数据。为了解决这个问题,我们创建了 DIGEP-Pred 2.0 网络应用程序,它可以通过类药物的结构式预测 DIGEP 和潜在的药物靶点。它基于结构-活性关系(SAR)和网络分析的结合使用。SAR 模型是利用 PASS(物质活性谱预测)技术创建的,其数据来自比较毒物基因组学数据库(CTD),连接图(CMap)用于预测 DIGEP,PubChem 和 ChEMBL 用于预测分子作用机制(MoA)。用户只需使用化合物的结构式,就能获得多个细胞系和药物靶点中潜在基因表达变化的信息,这些基因表达变化是导致观察到的 DIGEP 的潜在主调节因子。通过缺一交叉验证计算出的 13377 个基因和 2932 个蛋白质(CTD 数据)的平均预测准确率分别为 86.5%和 94.8%,2170 个 MoAs 的平均预测准确率为 97.9%。此外,还针对 MCF7、PC3 和 HL60 细胞系的 CMap 数据创建了 SAR 模型(平均准确率为 87.5%),并采用了不同的折叠变化对数值阈值:0.5、0.7、1、1.5 和 2。此外,还提供了预测基因富集的通路(KEGG、Reactome)、基因本体论的生物过程和疾病(DisGeNet)数据,以及基于 OmniPath 数据的目标主调节因子估算。DIGEP-Pred 2.0 网络应用程序可在 https://www.way2drug.com/digep-pred 免费获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes.

The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.

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
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
×
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:481959085
Book学术官方微信