![]() PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. ![]() There is currently no automated software to predict the occurrence of these programmed ribosomal frameshifts (PRF), and they are currently only identified by manual curation. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. More importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. ![]() The alternate frame has different codons, so different amino acids are added to the peptide chain. One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence and shifting the ribosome into one of the other two alternate reading frames. ![]()
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