Loss-functions matter, on optimizing score functions for the estimation of protein models accuracy

Motivation: Methods for protein structure prediction (PSP) generate multiple alternative structural models (aka decoys). Thus, supervised learning methods for the evaluation and ranking of these models are crucial elements of PSP. Supervised learning involves optimization of loss functions, but their influence on performance is typically overlooked. Here we put the loss functions in the spotlight, and study their effect on prediction performance.
Results: Here we report the performances of three variants of MESHI-score, a supervised learning method for the estimation of model accuracy (EMA). Each variant was trained with a different loss function and showed better performance in different aspects of the EMA problem. Most importantly, better discrimination between models of the same target, is gained by target centered loss functions.