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基于多输入多输出模型的全长核糖体密度预测

Full-length ribosome density prediction by a multi-input and multi-output model.

作者信息

Tian Tingzhong, Li Shuya, Lang Peng, Zhao Dan, Zeng Jianyang

机构信息

Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.

MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.

出版信息

PLoS Comput Biol. 2021 Mar 26;17(3):e1008842. doi: 10.1371/journal.pcbi.1008842. eCollection 2021 Mar.

Abstract

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.

摘要

在原核生物和真核生物中,翻译延伸过程都受到一系列复杂机制的调控。尽管核糖体谱分析技术最近取得了进展,使人们能够以密码子分辨率捕获全基因组范围内沿着转录本的核糖体足迹,但延伸动力学的调控密码仍未被完全理解。现有的大多数从核糖体谱数据建模翻译延伸的计算方法主要关注局部上下文模式,而忽略了延伸过程的连续性以及远距离密码子的核糖体密度之间的关系。据我们所知,尚未在全长编码序列(CDS)水平上对翻译延伸过程进行建模研究。在本文中,我们开发了一种基于深度学习的方法,该方法具有多输入多输出框架,名为RiboMIMO,用于对全长mRNA CDS区域的核糖体密度分布进行建模。通过考虑相邻和远距离密码子之间翻译效率的潜在相关性,并从输入的全长编码序列中提取隐藏特征,RiboMIMO能够大大优于当前最先进的基线方法,并准确预测沿整个mRNA CDS区域的核糖体密度分布。此外,RiboMIMO探索了单个输入密码子对输出核糖体密度预测的贡献,从而有助于揭示影响翻译延伸过程的重要生物学因素。基于我们名为密码子影响得分的可解释指标进行的分析,不仅确定了几种与先前发表的文献一致的模式,而且据我们所知首次揭示了距离核糖体A位点较远的密码子可能也与翻译延伸速率有关。这一关于翻译延伸速度的远程影响的发现可能为蛋白质合成的调控机制提供新的线索。总体而言,这些结果表明RiboMIMO可以为研究全长CDS范围内的翻译延伸调控提供一个有用的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5215/8026034/a026373b2e63/pcbi.1008842.g001.jpg

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