Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5

IF 4.6 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2024-06-27 DOI:10.1088/2632-2153/ad5784
Atsarina Larasati Anindya, Torbjörn Nur Olsson, Maja Jensen, Maria-Jose Garcia-Bonete, Sally P Wheatley, Maria I Bokarewa, Stefano A Mezzasalma and Gergely Katona
{"title":"Deciphering peptide-protein interactions via composition-based prediction: a case study with survivin/BIRC5","authors":"Atsarina Larasati Anindya, Torbjörn Nur Olsson, Maja Jensen, Maria-Jose Garcia-Bonete, Sally P Wheatley, Maria I Bokarewa, Stefano A Mezzasalma and Gergely Katona","doi":"10.1088/2632-2153/ad5784","DOIUrl":null,"url":null,"abstract":"In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleev’s periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only confirms the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.","PeriodicalId":33757,"journal":{"name":"Machine Learning Science and Technology","volume":"236 1","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning Science and Technology","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/2632-2153/ad5784","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

In the realm of atomic physics and chemistry, composition emerges as the most powerful means of describing matter. Mendeleev’s periodic table and chemical formulas, while not entirely free from ambiguities, provide robust approximations for comprehending the properties of atoms, chemicals, and their collective behaviours, which stem from the dynamic interplay of their constituents. Our study illustrates that protein-protein interactions follow a similar paradigm, wherein the composition of peptides plays a pivotal role in predicting their interactions with the protein survivin, using an elegantly simple model. An analysis of these predictions within the context of the human proteome not only confirms the known cellular locations of survivin and its interaction partners, but also introduces novel insights into biological functionality. It becomes evident that electrostatic- and primary structure-based descriptions fall short in predictive power, leading us to speculate that protein interactions are orchestrated by the collective dynamics of functional groups.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过基于成分的预测解密肽与蛋白质之间的相互作用:Survivin/BIRC5 案例研究
在原子物理学和化学领域,组成是描述物质的最有力手段。门捷列夫的元素周期表和化学公式虽然并非完全没有歧义,但却为理解原子、化学物质的特性及其集体行为提供了可靠的近似值,而这些特性和集体行为都源于其组成成分的动态相互作用。我们的研究表明,蛋白质与蛋白质之间的相互作用也遵循类似的范式,其中肽的组成在预测肽与蛋白质 survivin 的相互作用中发挥着关键作用,我们使用了一个优雅而简单的模型。在人类蛋白质组的背景下对这些预测进行分析,不仅证实了存活素及其相互作用伙伴在细胞中的已知位置,还对生物功能提出了新的见解。基于静电和一级结构的描述显然不具备预测能力,因此我们推测蛋白质的相互作用是由功能基团的集体动力学协调的。
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 去求助
来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
期刊最新文献
Generative diffusion model surrogates for mechanistic agent-based biological models. Mamba time series forecasting with uncertainty quantification. Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures. 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery. Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1