Looking beyond natural sequences

MIT researchers developed PottsMPNN, a machine-learning framework that models the sequence-energy landscape of proteins better than existing methods. The model improves sequence generation and mutation stability prediction, enabling design of structurally feasible proteins with sequences unlike native ones.