| Summary: | Understanding how enzymes achieve their tremendous catalytic power is a major question in biochemistry. Greater understanding is also needed for enzyme engineering applications. To date, some active site residues have been identified for hundreds of enzymes and these known residues are generally those that come in direct contact with the substrate. Dynamic conformational changes during catalysis, in addition to electrostatic interactions, allow for coupling between distal
residues and the canonical active site residues of an enzyme. In many cases, enzyme efficiency and specificity depend on residues not in direct contact with the substrate, termed distal residues. This work focuses on two enzymes with computationally predicted extended active sites: Escherichia coli ornithine transcarbamoylase (OTC) and Escherichia coli Glycinamide ribonucleotide transformylase (GART). OTC plays a central role in amino acid metabolism and has been reported to undergo an
induced-fit conformational change upon binding of the first substrate, carbamoyl phosphate (CP), followed by binding of the second substrate, ornithine (ORN). Glycinamide ribonucleotide transformylase (GART) catalyzes the reaction of 10-formyltetrahydrofolate (FTHF) and β-glycinamide ribonucleotide (GAR) to turn over formyl-glycinamide ribonucleotide (FGAR) and tetrahydrofolate (TF) in the de novo purine biosynthesis pathway. Although this enzyme has been greatly studied, most focus has
been on catalytic residues in direct contact with the substrate molecules. Interestingly, GART undergoes a pH-dependent conformational change during catalysis. Therefore, there are likely to be non-obvious distal residues that modulate this conformational change and are important for catalysis, but these distal residues are yet to be identified. To better understand how distal residues may participate in the catalytic mechanism of OTC and GART, computational predictions were used to
design a number of variants in the distal residues of these proteins to probe their impact on the catalytic efficiency of GART. POOL (Partial Order Optimum Likelihood), a machine learning method developed at Northeastern University, utilizes electrostatic and geometric information to predict important catalytic residues based on the 3D structure of a protein. Catalytic residues are ranked in order of functional importance based on the electrostatic properties of that protein. POOL also
has additional input features, including sequence-based scores derived from evolutionary history and protein surface topology to predict residues important for catalytic activity. The roles of these distal residues in OTC and GART activity were tested by constructing site-directed mutations at predicted positions, followed by steady-state kinetics assays, thermal stability, substrate binding and small x-ray scattering studies with the variants. In addition to direct effects on catalytic
activity of OTC and GART variants, effects on overall protein stability and substrate binding were observed that reveal the intricacies of how these residues contribute to catalysis.
|
|---|
