Redesigning the PheA domain of gramicidin synthetase leads to a new understanding of the enzyme's mechanism and selectivity.

The PheA domain of gramicidin synthetase A, a non-ribosomal peptide synthetase, selectively binds phenylalanine along with ATP and Mg2+ and catalyzes the formation of an aminoacyl adenylate. In this study, we have used a novel protein redesign algorithm, K*, to predict mutations in PheA that should exhibit improved binding for tyrosine. Interestingly, the introduction of two predicted mutations to PheA did not significantly improve KD, as measured by equilibrium fluorescence quenching. However, the mutations improved the specificity of the enzyme for tyrosine (as measured by kcat/KM), primarily driven by a 56-fold improvement in KM, although the improvement did not make tyrosine the preferred substrate over phenylalanine. Using stopped-flow fluorometry, we examined binding of different amino acid substrates to the wild-type and mutant enzymes in the pre-steady state in order to understand the improvement in KM. Through these investigations, it became evident that substrate binding to the wild-type enzyme is more complex than previously described. These experiments show that the wild-type enzyme binds phenylalanine in a kinetically selective manner; no other amino acids tested appeared to bind the enzyme in the early time frame examined (500 ms). Furthermore, experiments with PheA, phenylalanine, and ATP reveal a two-step binding process, suggesting that the PheA-ATP-phenylalanine complex may undergo a conformational change toward a catalytically relevant intermediate on the pathway to adenylation; experiments with PheA, phenylalanine, and other nucleotides exhibit only a one-step binding process. The improvement in KM for the mutant enzyme toward tyrosine, as predicted by K*, may indicate that redesigning the side-chain binding pocket allows the substrate backbone to adopt productive conformations for catalysis but that further improvements may be afforded by modeling an enzyme:ATP:substrate complex, which is capable of undergoing conformational change.

A novel ensemble-based scoring and search algorithm for protein redesign and its application to modify the substrate specificity of the gramicidin synthetase a phenylalanine adenylation enzyme.

Realization of novel molecular function requires the ability to alter molecular complex formation. Enzymatic function can be altered by changing enzyme-substrate interactions via modification of an enzyme's active site. A redesigned enzyme may either perform a novel reaction on its native substrates or its native reaction on novel substrates. A number of computational approaches have been developed to address the combinatorial nature of the protein redesign problem. These approaches typically search for the global minimum energy conformation among an exponential number of protein conformations. We present a novel algorithm for protein redesign, which combines a statistical mechanics-derived ensemble-based approach to computing the binding constant with the speed and completeness of a branch-and-bound pruning algorithm. In addition, we developed an efficient deterministic approximation algorithm, capable of approximating our scoring function to arbitrary precision. In practice, the approximation algorithm decreases the execution time of the mutation search by a factor of ten. To test our method, we examined the Phe-specific adenylation domain of the nonribosomal peptide synthetase gramicidin synthetase A (GrsA-PheA). Ensemble scoring, using a rotameric approximation to the partition functions of the bound and unbound states for GrsA-PheA, is first used to predict binding of the wildtype protein and a previously described mutant (selective for leucine), and second, to switch the enzyme specificity toward leucine, using two novel active site sequences computationally predicted by searching through the space of possible active site mutations. The top scoring in silico mutants were created in the wetlab and dissociation/binding constants were determined by fluorescence quenching. These tested mutations exhibit the desired change in specificity from Phe to Leu. Our ensemble-based algorithm, which flexibly models both protein and ligand using rotamer-based partition functions, has application in enzyme redesign, the prediction of protein-ligand binding, and computer-aided drug design.