Enhancing the reverse transcriptase function in Taq polymerase via AI-driven multiparametric rational design.

Introduction: Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5'- 3' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties. Methods: The experimental procedure was conducted in several stages: 1) On the basis of a foundational paper, we selected 18 candidate mutations known to affect RTase activity across six sites. These candidates, along with the wild type, were assessed in the wet lab for multiple properties to establish an initial training dataset. 2) Using embeddings of Taq polymerase variants generated by a protein language model, we trained a Ridge regression model to predict multiple enzyme properties. This model guided the selection of 14 new candidates for experimental validation, expanding the dataset for further refinement. 3) To better manage risk by assessing confidence intervals on predictions, we transitioned to Gaussian process regression and trained this model on an expanded dataset comprising 33 data points. 4) With this enhanced model, we conducted an Results and Discussion: This iterative, data-driven strategy ultimately led to the identification of 18 enzyme variants that exhibited markedly improved RTase activity while maintaining a favorable balance of other key properties. These enhancements were generally accompanied by lower K