Biophysical model explains sequence-specific variation in transcriptional error rates

The fidelity of transcription by RNA polymerases is essential for accurate gene expression and cellular health, but its governing mechanisms remain unclear. Recent in vivo studies reveal that gene sequences influence transcriptional error rates, with the downstream base having a significant effect on the error rate of its upstream base. However, the biophysical mechanisms behind this sequence dependence are still unknown. We developed a biophysical model to compute template-specific error rates and analyzed existing data using bioinformatics, integrating known and unknown parameters. Our theoretical predictions align with Monte Carlo simulations, allowing us to refine kinetic parameters and estimate unknown rates. Notably, our model predicts that error rates also depend on the base two positions downstream—a correlation not previously reported. The lowest error rates occur with pyrimidines and the highest with purines, confirmed by our bioinformatics analysis. Investigating the BRCA1 gene, we found that sequence-specific variations reduce the likelihood of severe mutations compared to the situation where no correlations of error rates with downstream base identity exist. These results, therefore, provide evolutionary insights into transcription fidelity mechanisms.