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. 2022 Jul 15;12:12126. doi: 10.1038/s41598-022-15526-7

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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iCodon predicts gene expression based on the codon composition and designs new variants based on synonymous mutations. (A) Diagram of the synonymous reporters, differing only in synonymous mutations with different regulatory effects on mRNA stability. Each mRNA contains the coding sequence of mCherry fluorescent protein followed by a ribosome-skipping sequence (P2A) and a coding region that differs in the proportion of optimal and non-optimal codons but encodes the same peptide (synonymous mutations). (B) Scatter plot showing that fluorescence intensity of synonymous reporters in 293T transfected cells correlates with predicted mRNA stability (r = 0.97, p < 2.2 × 10⁻¹⁶, Pearson correlation test). n = 10 for all tested variants. (C) Diagram depicting the algorithm for codon optimization, iCodon: An initial coding sequence is provided by the user. Evolution: the algorithm generates variant sequences by introducing random synonymous mutations. Prediction: the machine learning model predicts the mRNA stability of each variant based on the codon composition. Selection: the algorithm selects the sequences with the highest or lowest mRNA stability depending on the direction of optimization. Iteration: this process is repeated multiple times producing an optimization path that generates a gradient in mRNA stability level. Illustrations by Mark Miller. (D) A random group of 50 human genes with predicted intermediate mRNA stability was selected and optimized and deoptimized by iCodon. The x-axis is the iteration number, and the y-axis is the predicted mRNA stability. The circles connected by a dash-line show the optimization or deoptimization path for each gene. The histogram on the right is the mRNA stability distribution for endogenous human genes. (E) Violin plot showing predictions of mRNA stability of selected groups of genes compared to all genes in the human transcriptome. The horizontal lines show the lower, middle and upper quartiles of the predicted mRNA stability of all genes. p values and number of genes (n) are indicated.