Computational mutation prediction to enhance or recover enzymatic activity.

Abstract

Atomistic computational simulations have become a useful tool for biomolecular research. These tools are now becoming sufficiently accurate to predict single point mutations that can enhance or recover enzymatic activity. In this talk I will present the use of computational approaches to perform these predictions, and examples coupled with experimental validation. Two examples of applications of these methods to DNA transaction enzymes will be discussed. The first example involves an enzyme from the APOBEC3 family, which are host restriction factors that inhibit lentiviruses, such as HIV-1. For APOBEC3C (A3C), the chimpanzee and gorilla orthologues are more active than human A3C, and the Old World Monkey A3C from rhesus macaque (rh) is not active against HIV-1. Using a combination of molecular dynamics (MD), direct coupling analysis (DCA) and experimental techniques we have investigated the key residues for antivirala ctivities. Overall, our results determine the basis for why rhA3C is less active than human A3C and establish the amino acid network for dimerization and increased activity. The second example involves the methyltransferase (MTase) from Mycoplasma penetrans, which has recently been shown to synthesize the modified nucleotide 5-carboxymethylcytosine (5cxmC), formed as a trace byproduct of cytidine methylation by M.MpeI with the secondary metabolite carboxy-S-adenosyl-L-methionine (CxSAM) in place of the normal reaction cofactor, SAM. The use of computational simulations to investigate this new selectivity and predict additional mutations to enhance this new function, and experimental confirmation of these predictions will be presented and discussed.