DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

It is shown that DistilProtBert preforms very well on singlet, doublet, and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91, and 0.87, and it is suggested that by examining the small number of false-positive classifications the authors may be able to identify de-novo potential natural-like proteins based on random shuffling of amino acid sequences.