Predicting molecular recognition features in protein sequences with MoRFchibi 2.0

Identifying sites within intrinsically disordered regions (IDR) that bind to other proteins remains a significant challenge. Molecular Recognition Features (MoRFs) are a subset of segments in IDR that bind to other proteins, undergoing a disorder-to-order transition upon binding. This paper introduces MoRFchibi 2.0, a specialized prediction tool designed to identify the locations of MoRFs within protein sequences. Our results show that MoRFchibi 2.0 outperforms all existing MoRF and general predictors of protein-binding sites within IDRs, including top-performing models from CAID rounds 1, 2, and 3. Remarkably, MoRFchibi 2.0 surpasses predictors that utilize AlphaFold data and state-of-the-art protein language models, achieving superior ROC and Precision-Recall curves and higher success rates. MoRFchibi 2.0 generates output scores using an ensemble of convolutional neural network logistic regression models, followed by a reverse Bayes Rule to adjust for priors in the training data. These scores reflect MoRF probabilities normalized for the priors in the training data, making them individually interpretable and compatible with other tools utilizing the same scoring framework.