SUCCESSFUL EXAMPLES OF THE APPLICATION OF NOVEL ITERATIVE TRAINABLE ALGORITHMS TO GUIDE RATIONAL MUTATION STRATEGIES FOR ENZYME ENGINEERING: FROM PREDICTION TO LAB TESTING TO ALGORITHM RETRAINING

摘要

Both natural mutations occurring in a homologous enzyme family and mutations engineered in a given protein can have a tremendous impact in the activity and binding behavior of the enzyme towards substrates or other molecules. Binding and catalytic properties can be modified by rationally mutating selected amino acids in a protein. For instance, new specificity properties can be engineered into existing enzymes, which can be applied to the rational design of mutations to alter its catalysis. Although this approach has been largely used, the modifications introduced in the target protein have not been exempt of deleterious effects on protein function, binding or physicochemical properties. Much finer tuned modifications should be designed in order to alter the desired catalytic or binding properties of a protein and simultaneously not affecting other protein properties or functions. These engineered mutations usually require a thorough knowledge of the relevant structure-function relationships in the protein molecule. If no precise structure-function information is available for a protein, the amount of possible amino acid mutations to be tested precludes a direct search. Furthermore, in many cases a directed evolution strategy cannot be successfully used to achieve the desired results due to the unavailability of suitable screening tests.