Phenotypic mutations contribute to protein diversity and shape protein evolution

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Proteins Need Extra Attention: Improving the Predictive Power of Protein Language Models on Mutational Datasets with Hint Tokens

In this computational study, we introduce "hint token learning," a novel machine learning approach designed to enhance protein language modeling. This method effectively addresses the unique challenges of protein mutational datasets, characterized by highly similar inputs that may differ by only a single token. Our research highlights the superiority of hint token learning over traditional fine-tuning methods through three distinct case studies. We first developed a highly accurate free energy of folding model using the largest protein stability dataset to date. Then, we applied hint token learning to predict a biophysical attribute, the brightness of green fluorescent protein mutants. In our third case, hint token learning was utilized to assess the impact of mutations on RecA bioactivity. These diverse applications collectively demonstrate the potential of hint token learning for improving protein language modeling across general and specific mutational datasets. To facilitate broader use, we have integrated our protein language models into the HuggingFace ecosystem for downstream, mutational fine-tuning tasks.

A novel ELP1 mutation impairs the function of the Elongator complex and causes a severe neurodevelopmental phenotype

BACKGROUND

Neurodevelopmental disorders (NDDs) are heterogeneous, debilitating conditions that include motor and cognitive disability and social deficits. The genetic factors underlying the complex phenotype of NDDs remain to be elucidated. Accumulating evidence suggest that the Elongator complex plays a role in NDDs, given that patient-derived mutations in its ELP2, ELP3, ELP4 and ELP6 subunits have been associated with these disorders. Pathogenic variants in its largest subunit ELP1 have been previously found in familial dysautonomia and medulloblastoma, with no link to NDDs affecting primarily the central nervous system.

METHODS

Clinical investigation included patient history and physical, neurological and magnetic resonance imaging (MRI) examination. A novel homozygous likely pathogenic ELP1 variant was identified by whole-genome sequencing. Functional studies included in silico analysis of the mutated ELP1 in the context of the holo-complex, production and purification of the ELP1 harbouring the identified mutation and in vitro analyses using microscale thermophoresis for tRNA binding assay and acetyl-CoA hydrolysis assay. Patient fibroblasts were harvested for tRNA modification analysis using HPLC coupled to mass spectrometry.

RESULTS

We report a novel missense mutation in the ELP1 identified in two siblings with intellectual disability and global developmental delay. We show that the mutation perturbs the ability of ELP123 to bind tRNAs and compromises the function of the Elongator in vitro and in human cells.

CONCLUSION

Our study expands the mutational spectrum of ELP1 and its association with different neurodevelopmental conditions and provides a specific target for genetic counselling.