Predicting the efficiency of UAG translational stop signal through studies of physicochemical properties of its composite mono- and dinucleotides
In this study, we explored the problem of predicting the UAG stop-codon read-through efficiency. The reported nucleotide sequences were first converted into physicochemical property vectors before being presented to a machine learning algorithm. Two sets of physicochemical properties were applied: one for mononucleosides (in terms of steric bulk, hydrophobicity and electronics) and another for dinucleotides. To the best of our knowledge, this is the first report of how dinucleotides are converted into principle components derived from NMR chemical shift data. A few efficiency prediction models were then derived and a comparison between mononucleoside and dinucleotide-based models was shown. In the derived models, the coefficients of these property based predictors lend themselves to bio-physical interpretations, an advantage which is demonstrated in this study via a prediction model based on the steric bulk factor. Although it is quite simple, the steric bulk factor model explained well the effect of sequence variations surrounding the amber stop codon and the tRNA bearing UCCU anticodon. We further proposed new alternatives at position ?1 and +4 of a UAG stop codon sequence to enhance the readthrough efficiency. This research may contribute to a better understanding of the readthrough mechanisms and may also help to study the normal translation termination process.
physicochemical properties amber stop codon artificial intelligence computational biology computer modelling