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Computational structure-based redesign of enzyme activity.

ABSTRACT: We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top-ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for computational enzyme redesign that are experimentally shown to result in significant additional improvements in the target substrate specificity. The mutant with the highest activity for a noncognate substrate exhibits 1/6 of the wild-type enzyme/wild-type substrate activity, further confirming the feasibility of our computational approach. Our results suggest that structure-based protein design can identify active mutants different from those selected by evolution.

SUBMITTER: Chen CY

PROVIDER: S-EPMC2645347 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Computational structure-based redesign of enzyme activity.

Chen Cheng-Yu CY Georgiev Ivelin I Anderson Amy C AC Donald Bruce R BR

Proceedings of the National Academy of Sciences of the United States of America 20090219 10

We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top-ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for ...[more]

PMID: 19228942

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Project description:MotivationDisruption of protein-protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein-Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely.ResultsTwo existing methods for predicting mutational effects on binding, FoldX and INT5, were demonstrated to be quite precise in selecting disruptive mutations from the SKEMPI and AB-Bind databases of experimentally determined changes in binding free energy. DisruPPI was implemented to use an INT5-based disruption score integrated with an AMBER-based stability assessment and was applied to disrupt protein interactions in a set of different targets representing diverse applications. In retrospective evaluation with three different case studies, comparison of DisruPPI-designed variants to published experimental data showed that DisruPPI was able to identify more diverse interaction-disrupting and stability-preserving variants more efficiently and effectively than previous approaches. In prospective application to an interaction between enhanced green fluorescent protein (EGFP) and a nanobody, DisruPPI was used to design five EGFP variants, all of which were shown to have significantly reduced nanobody binding while maintaining function and thermostability. This demonstrates that DisruPPI may be readily utilized for effective removal of known epitopes of therapeutically relevant proteins.Availability and implementationDisruPPI is implemented in the EpiSweep package, freely available under an academic use license.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:As the spread of antibiotic resistant bacteria steadily increases, there is an urgent need for new antibacterial agents. Because fatty acid synthesis is only used for membrane biogenesis in bacteria, the enzymes in this pathway are attractive targets for antibacterial agent development. Acetyl-CoA carboxylase catalyzes the committed and regulated step in fatty acid synthesis. In bacteria, the enzyme is composed of three distinct protein components: biotin carboxylase, biotin carboxyl carrier protein, and carboxyltransferase. Fragment-based screening revealed that amino-oxazole inhibits biotin carboxylase activity and also exhibits antibacterial activity against Gram-negative organisms. In this report, we redesigned previously identified lead inhibitors to expand the spectrum of bacteria sensitive to the amino-oxazole derivatives by including Gram-positive species. Using 9,411 small organic building blocks, we constructed a diverse combinatorial library of 1.2×10⁸ amino-oxazole derivatives. A subset of 9×10⁶ of these compounds were subjected to structure-based virtual screening against seven biotin carboxylase isoforms using similarity-based docking by eSimDock. Potentially broad-spectrum antibiotic candidates were selected based on the consensus ranking by several scoring functions including non-linear statistical models implemented in eSimDock and traditional molecular mechanics force fields. The analysis of binding poses of the top-ranked compounds docked to biotin carboxylase isoforms suggests that: (1) binding of the amino-oxazole anchor is stabilized by a network of hydrogen bonds to residues 201, 202 and 204; (2) halogenated aromatic moieties attached to the amino-oxazole scaffold enhance interactions with a hydrophobic pocket formed by residues 157, 169, 171 and 203; and (3) larger substituents reach deeper into the binding pocket to form additional hydrogen bonds with the side chains of residues 209 and 233. These structural insights into drug-biotin carboxylase interactions will be tested experimentally in in vitro and in vivo systems to increase the potency of amino-oxazole inhibitors towards both Gram-negative as well as Gram-positive species.

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Computational structure-based redesign of enzyme activity.

Publications

Computational structure-based redesign of enzyme activity.

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