SolXplain: An Explainable Sequence-Based Protein Solubility Predictor

Abstract

MotivationProtein solubility is a property associated with protein expression and is a critical determinant of the manufacturability of therapeutic proteins. It is thus imperative to design accurate in-silico sequence-based solubility predictors.\n\nMethodsIn this study, we propose SolXplain, an extreme gradient boosting machine based protein solubility predictor which achieves state-of-the-art performance using physio-chemical, sequence and novel structure derived features from protein sequences. Moreover, SolXplain has a unique attribute that it can provide explanation for the predicted class label for each test protein based on its corresponding feature values using SHapley Additive exPlanations (SHAP) method.\n\nResultsBased on an independent test set, SolXplain outperformed other sequence-based methods by at least 2% in accuracy and 2% in Matthews correlation coefficient, with an overall accuracy of 78% and Matthews correlation coefficient of 0.56. Additionally, for fractions of exposed residues (FER) at various residual solvent accessibility (RSA) cutoffs, we observed higher fractions to associate positively with protein solubility, and tripeptide stretches that contain one isoleucine and one or more histidines, to associate negatively with solubility. The improved prediction accuracy of SolXplain enables it to predict protein solubility with greater consistency and screen for sequences with enhanced manufacturability.