SPIRED-Fitness: an end-to-end framework for the prediction of protein structure and fitness from single sequence

Abstract

AbstractSignificant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction accuracy, showing promise for many downstream prediction tasks. Here, we propose SPIRED, a novel single-sequence-based structure prediction model that exhibits comparable performance to the state-of-the-art methods but with approximately 5-fold acceleration in inference and at least one order of magnitude reduction in training consumption. By integrating SPIRED with downstream neural networks, we compose an end-to-end framework named SPIRED-Fitness for the rapid prediction of both protein structure and fitness from single sequence. SPIRED-Fitness and its derivative SPIRED-Stab achieve state-of-the-art performance in predicting the mutational effects on protein fitness and stability metrics, respectively.