THERAZYMES, PID2024-155349NB-I00

Enzymes are remarkable catalysts, capable of achieving exceptional rate accelerations by lowering reaction activation barriers. This allows reactions to proceed at lower temperatures and pressures while maintaining specificity and selectivity under mild biological conditions. Inspired by these unique features, scientists have explored redesigning natural enzymes, reconstructing ancestral variants, and designing entirely new enzymes to expand their reaction and substrate scope or optimize performance under non-natural conditions. Such advancements in enzyme engineering and design have significantly broadened the applications of biocatalysis, both in academia and industry, ranging from chemical synthesis to therapeutic uses.
Natural proteins used as therapeutics often exhibit suboptimal properties that require enhancement to achieve desired functionality to treat disease. In this project, physics and data-based approaches will be combined to target the rational design of efficient enzymes of therapeutic value. Deep Learning (DL) techniques will be used to reconstruct ancestral sequences, which together with manually selected natural scaffolds will be used as starting templates for the rational engineering process. The rational design approach developed will be based on careful analysis of the conformational heterogeneity of DL-generated ancestral and natural enzymes by means of our recently developed template-based AlphaFold2 computational
framework, which will provide multiple conformational states for evaluating their catalytic potential towards natural and desired substrates. Analysis of the intertwined conformational networks by Shortest Path Map evaluation combined with Multiple Sequence Alignment and large language models will be used to generate new sequences. The protocol will be applied to rationally develop efficient ganciclovir-dependent kinases for their application in Suicide Cancer Gene Therapy, and to design glutaminase-free asparaginases for Acute Lymphoblastic Leukemia treatment. The experimental assessment of the most promising designs will reveal the potential of this new approach for the rational design of therapeutically-relevant enzymes.