Paper Info
Title
Riboexp: an interpretable reinforcement learning framework for ribosome density modeling
Abstract
Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis.
Year
DOI
Venue
2021
10.1093/bib/bbaa412
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
reinforcement learning, ribosome profiling, translation elongation
Journal
22
Issue
ISSN
Citations 
5
1467-5463
0
PageRank 
References 
Authors
0.34
11
9
Name
Order
Citations
PageRank
Hailin Hu143.17
Xianggen Liu232.07
An Xiao300.34
YangYang Li400.34
Chengdong Zhang500.34
Tao Jiang6143.25
Dan Zhao700.68
Sen Song829922.35
Jianyang Zeng913516.82