Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Abstract

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure−function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Prochiral Selectivity in Enzymatic Polyethylene Terephthalate Depolymerization Revealed by Computational Modeling

“…The energy barriers for another effective PET hydrolase, ThermoPETase, were also estimated using the four correlations. The enzyme demonstrated lower energy barriers for the rate-limiting step (16.6 ± 1.5 kcal/mol) compared to the wild-type Is PETase (23.8 ± 1.3 kcal/mol) (Figure S10), consistent with the experimental results. , We anticipate that the established correlation could be adopted as a scoring function by high-throughput computational enzyme engineering platforms in designing PETase with enhanced depolymerization efficiency. − …”

EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution

“…In our future studies, we aim to address these challenges and further evolve EnzyKR into a generalizable model. 31 …”