Computational design and interpretation of single-RNA translation experiments

• Published in: PLoS computational biology 2019-10, Vol.15 (10), p.e1007425-e1007425
• Main Authors: Aguilera, Luis U, Raymond, William, Fox, Zachary R, May, Michael, Djokic, Elliot, Morisaki, Tatsuya, Stasevich, Timothy J, Munsky, Brian
• Format: Article
• Language: English
• Subjects: Actin; Amino acid sequence; Assaying; Biochemistry; Bioengineering; Biology and Life Sciences; Biomedical engineering; Codons; Computational Biology - methods; Computer applications; Computer Simulation; Elongation; Engineering; Fluorescence; Fluorescence microscopy; Fluorescence Recovery After Photobleaching; Fluorescence spectroscopy; Gene expression; Genes; Genomes; Humans; Kinetics; Microscopy; Microscopy, Fluorescence; Molecular biology; Muscle proteins; Nucleotide sequence; Open source software; Photobleaching; Physics; Protein Biosynthesis; Proteins; Proteins - metabolism; Research and Analysis Methods; Ribonucleic acid; Ribosomes - metabolism; RNA; RNA - metabolism; RNA - physiology; RNA, Messenger - metabolism; Software; Source code; Spectrometry, Fluorescence; Spectroscopy; Stochastic models; Stochasticity; Transfer RNA; Translation; Translation (Genetics); Translations; tRNA
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1007425

Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, β-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.