Yana Bromberg1,2, R. Prabakaran1,*, Anowarul Kabir3,* and Amarda Shehu3 1Department of Biology, Emory University, Atlanta 30322, Georgia, USA 2Department of Computer Science, Emory University, Atlanta 30322, Georgia, USA 3Department of Computer Science, George Mason University, Fairfax 22030, Virginia, USA Correspondence: yana.brombergemory.edu

↵* These authors contributed equally to this work.

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein “sentences”? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.