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Significance Substrate specificity landscape of a protease enzyme is the set of all substrate sequences that are recognized/cut (and, as importantly, not recognized/cut) by the enzyme. Accurate and rapid elucidation of these landscapes for any given protease is key for the design of novel targeted proteases to prevent unwarranted off-target cleavage, and provides insight into the functional robustness of naturally occurring proteases. We developed a structure-guided approach for predicting protease substrate specificity landscapes, in which data from experiments in yeast and molecular simulations are combined using machine learning. Using this approach, we comprehensively map the sequence−energetics−function landscape of the hepatitis C virus NS3/4A protease and its drug-resistant variants.

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations