ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions

IF 4.2 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yifei Gao , Runhan Shi , Gufeng Yu , Yuyang Huang , Yang Yang
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引用次数: 0

Abstract

RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indispensable role of RBPs in both health and disease development. With the increase of experimental data, machine-learning methods have been widely used to predict RNA-protein interactions. However, most current methods either train models for individual RBPs or develop multi-task models for a fixed set of multiple RBPs. These approaches are incapable of predicting interactions with previously unseen RBPs. In this study, we present ZeRPI, a zero-shot method for predicting RNA-protein interactions. Based on a graph neural network model, ZeRPI integrates RNA and protein information to generate detailed representations, using a novel loss function based on contrastive learning principles to augment the alignment between interacting pairs in feature space. ZeRPI demonstrates competitive performance in predicting RNA-protein interactions across a wide array of RBPs. Notably, our model exhibits remarkable versatility in accurately predicting interactions for unseen RBPs, demonstrating its capacity to transfer knowledge learned from known RBPs.
ZeRPI:用于rna -蛋白质相互作用零射击预测的图神经网络模型。
rna -蛋白相互作用在多个层面上对生物功能至关重要。RNA结合蛋白(rbp)通过特定的RNA分子相互作用参与多种复杂的生物过程。先前的研究已经揭示了rbp在健康和疾病发展中不可或缺的作用。随着实验数据的增加,机器学习方法已被广泛用于预测rna -蛋白相互作用。然而,目前大多数方法要么针对单个rbp训练模型,要么针对一组固定的多个rbp开发多任务模型。这些方法无法预测与以前未见过的rbp的相互作用。在这项研究中,我们提出了ZeRPI,一种预测rna -蛋白质相互作用的零射击方法。ZeRPI基于图神经网络模型,整合RNA和蛋白质信息生成详细表示,使用基于对比学习原理的新型损失函数来增强特征空间中相互作用对之间的对齐。ZeRPI在预测rna -蛋白质相互作用方面具有竞争力。值得注意的是,我们的模型在准确预测未知rbp之间的相互作用方面表现出了显著的多功能性,证明了它有能力转移从已知rbp那里学到的知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Methods
Methods 生物-生化研究方法
CiteScore
9.80
自引率
2.10%
发文量
222
审稿时长
11.3 weeks
期刊介绍: Methods focuses on rapidly developing techniques in the experimental biological and medical sciences. Each topical issue, organized by a guest editor who is an expert in the area covered, consists solely of invited quality articles by specialist authors, many of them reviews. Issues are devoted to specific technical approaches with emphasis on clear detailed descriptions of protocols that allow them to be reproduced easily. The background information provided enables researchers to understand the principles underlying the methods; other helpful sections include comparisons of alternative methods giving the advantages and disadvantages of particular methods, guidance on avoiding potential pitfalls, and suggestions for troubleshooting.
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