Proteochemometric modeling strengthens the role of Q299 for GABA transporter subtype selectivity

Stefanie Kickinger, Anna Seiler, Daniela Digles, Gerhard F Ecker
{"title":"Proteochemometric modeling strengthens the role of Q299 for GABA transporter subtype selectivity","authors":"Stefanie Kickinger, Anna Seiler, Daniela Digles, Gerhard F Ecker","doi":"10.1101/2024.08.13.607728","DOIUrl":null,"url":null,"abstract":"Proteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g. activity of a compound). The big advantage of PCM compared to conventional Quantitative Structure-Activity Relationship (QSAR) modeling is, that by creating a single model one can not only predict the affinity of a diverse set of compounds to a diverse set of targets, but also extrapolate the specific ligand-protein interactions that might be relevant for activity. In this study, we compiled a dataset of 323 compounds and their bioactivity data regarding the inhibition of the four GABA-transporter (GAT1/BGT1/GAT2/GAT3) subtypes, which are potential new drug targets for treating epilepsy. Proteochemometric modeling using partial least squares and random forest provided models which performed equally well than conventional QSAR models for each individual transporter. However, by analyzing the importance of the protein descriptors used in the PCM models, we identified the amino acid Leu300/Q299/L294/L314/ in GAT1/BGT1/GAT2/GAT3 to be relevant for binding and subtype selectivity.","PeriodicalId":501518,"journal":{"name":"bioRxiv - Pharmacology and Toxicology","volume":"58 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Pharmacology and Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.13.607728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Proteochemometric modeling (PCM) combines ligand information as well as target information in order to predict an output variable of interest (e.g. activity of a compound). The big advantage of PCM compared to conventional Quantitative Structure-Activity Relationship (QSAR) modeling is, that by creating a single model one can not only predict the affinity of a diverse set of compounds to a diverse set of targets, but also extrapolate the specific ligand-protein interactions that might be relevant for activity. In this study, we compiled a dataset of 323 compounds and their bioactivity data regarding the inhibition of the four GABA-transporter (GAT1/BGT1/GAT2/GAT3) subtypes, which are potential new drug targets for treating epilepsy. Proteochemometric modeling using partial least squares and random forest provided models which performed equally well than conventional QSAR models for each individual transporter. However, by analyzing the importance of the protein descriptors used in the PCM models, we identified the amino acid Leu300/Q299/L294/L314/ in GAT1/BGT1/GAT2/GAT3 to be relevant for binding and subtype selectivity.
蛋白化学计量模型强化了 Q299 在 GABA 转运体亚型选择性中的作用
蛋白质化学计量建模(PCM)结合了配体信息和目标信息,以预测相关输出变量(如化合物的活性)。与传统的定量结构-活性关系(QSAR)建模相比,PCM 的最大优势在于通过创建一个单一模型,不仅可以预测不同化合物与不同靶标的亲和力,还可以推断出可能与活性相关的特定配体-蛋白质相互作用。在这项研究中,我们汇编了一个包含 323 种化合物及其生物活性数据的数据集,这些数据涉及对四种 GABA 转运体(GAT1/BGT1/GAT2/GAT3)亚型的抑制,而这四种亚型是治疗癫痫的潜在新药靶点。使用偏最小二乘法和随机森林建立的蛋白质化学计量模型与传统的 QSAR 模型相比,在每个转运体上的表现不相上下。但是,通过分析 PCM 模型中使用的蛋白质描述符的重要性,我们发现 GAT1/BGT1/GAT2/GAT3 中的氨基酸 Leu300/Q299/L294/L314/ 与结合和亚型选择性有关。
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
自引率
0.00%
发文量
0
×
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