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

IF 6.3 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":null,"pages":null},"PeriodicalIF":6.3000,"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分钟内获得全文求助全文

来源期刊

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

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.