A computational method to predict genetically encoded rare amino acids in proteins.

In several natural settings, the standard genetic code is expanded to incorporate two additional amino acids with distinct functionality, selenocysteine and pyrrolysine. These rare amino acids can be overlooked inadvertently, however, as they arise by recoding at certain stop codons. We report a method for such recoding prediction from genomic data, using read-through similarity evaluation. A survey across a set of microbial genomes identifies almost all the known cases as well as a number of novel candidate proteins.