Integrated analysis of individual codon contribution to protein biosynthesis reveals a new approach to improving the basis of rational gene design

Abstract

Gene codon optimization may be impaired by the misinterpretation of frequency and optimality of codons. Although recent studies have revealed the effects of codon usage bias (CUB) on protein biosynthesis, an integrated perspective of the biological role of individual codons remains unknown. Unlike other previous studies, we show, through an integrated framework that attributes of codons such as frequency, optimality and positional dependency should be combined to unveil individual codon contribution for protein biosynthesis. We designed a codon quantification method for assessing CUB as a function of position within genes with a novel constraint: the relativity of position-dependent codon usage shaped by coding sequence length. Thus, we propose a new way of identifying the enrichment, depletion and non-uniform positional distribution of codons in different regions of yeast genes. We clustered codons that shared attributes of frequency and optimality. The cluster of non-optimal codons with rare occurrence displayed two remarkable characteristics: higher codon decoding time than frequent–non-optimal cluster and enrichment at the 5′-end region, where optimal codons with the highest frequency are depleted. Interestingly, frequent codons with non-optimal adaptation to tRNAs are uniformly distributed in the Saccharomyces cerevisiae genes, suggesting their determinant role as a speed regulator in protein elongation.

Protein Abundance Prediction Through Machine Learning Methods

“…This selection is likely associated with the differences observed in both translation initiation rates and translation elongation rates (20,61). The enrichment of certain codons positions of a gene is also determined by natural selection (19), such as the presence of clusters of RareNO codons at the 5'…”

“…First, we analysed the codon positional dependency using the CodG package (19) to assess how different positions in the sequences are under evolutionary selective pressure. We employ a binning scheme, as described by Villada et al (19), where codons in a sequence are divided in a binning scheme relative to the CDS length. Each CDS is divided into 10 parts, which includes information about codon quantity per tenth part.…”

“…Based on this description, codons can be classified as frequent and optimal (FreO), frequent and non-optimal (FreNO), rare and optimal (RareO), and rare and non-optimal (RareNO). The distribution of codons regarding optimality and frequency in a protein-coding sequence is not stochastic, that is, it follows an evolution-selected distribution given their individual contributions to protein biosynthesis (19) As protein abundance is generally conserved across diverse phylogenetic taxa (22), this poses the question of whether absolute protein abundance can be mathematically predicted based on existing data. Based on the association between protein abundance and codon usage bias, it is reasonable to consider that metrics of codon usage bias are a potential source of information that can be applied to predict the abundance of proteins of non-model species or proteins undetected by mass spectrometry.…”

Codon usage bias creates a ramp of hydrogen bonding at the 5′-end in prokaryotic ORFeomes

“…Based on these findings, we hypothesized that selection acts, through position-dependent codon usage bias, against uniform distribution of hydrogen bonds per codon along CDSs. To test this hypothesis, we compiled a reference set with ∼1,200 bacterial and ∼300 archaeal ORFeomes and applied codon shuffling techniques 27,28 to generate ∼300,000 simulated ORFeomes that contain random synonymous mutations.…”

“…The null model to test selection against uniform distribution of codons was built by shuffling synonymous codons within all CDSs in each ORFeome. A total of 200 simulated ORFeomes were built for each one of the 1,496 ORFeomes in the representative dataset from which we obtained the metrics of expected and standard deviation of the number of hydrogen bonds per codon position as described in detail elsewhere 28 .…”