Unveiling Protein Stability, Design, and Recognition Mechanisms: Insights from Probabilistic Views and AI

Researchers propose a new approach called BayesDesign for protein sequence design that maximizes the Boltzmann probability objective function \(p(\text {structure}|\text {seq})\) without relying on gradient descent or MCMC optimization techniques. The study mathematically formalizes protein design objectives for stability and conformational specificity and shows how they relate to the Boltzmann probability objective. The BayesDesign algorithm is evaluated on two model systems, NanoLuciferase enzyme and the WW beta sheet motif, and the designed sequences show increased stability and conformational specificity compared to the native sequences. The approach offers a faster and more reliable way to design proteins with desired properties.