Limitations of Current Machine-Learning Models in Predicting Enzymatic Functions for Uncharacterized Proteins

Abstract

Thirty to seventy percent of proteins in any given genome have no assigned function and have been labeled as the protein “unknownme”. This large knowledge gap prevents the biological community from fully leveraging the plethora of genomic data that is now available. Machine-learning approaches are showing some promise in propagating functional knowledge from experimentally characterized proteins to the correct set of isofunctional orthologs. However, they largely fail to predict enzymatic functions unseen in the training set, as shown by dissecting the predictions made for 450 enzymes of unknown function from the model bacteriaEscherichia coliusing the DeepECTransformer platform. Lessons from these failures can help the community develop machine-learning methods that assist domain experts in making testable functional predictions for more members of the uncharacterized proteome.