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Drug resistance resulting from mutations to the target is an unfortunate common phenomenon that limits the lifetime of many of the most successful drugs. In contrast to the investigation of mutations after clinical exposure, it would be powerful to be able to incorporate strategies early in the development process to predict and overcome the effects of possible resistance mutations. Here we present a unique prospective application of an ensemble-based protein design algorithm, [K.sup.*], to predict potential resistance mutations in dihydrofolate reductase from Staphylococcus aureus using positive design to maintain catalytic function and negative design to interfere with binding of a lead inhibitor. Enzyme inhibition assays show that three of the four highly-ranked predicted mutants are active yet display lower affinity (18-, 9-, and 13-fold) for the inhibitor. A crystal structure of the top-ranked mutant enzyme validates the predicted conformations of the mutated residues and the structural basis of the loss of potency. The use of protein design algorithms to predict resistance mutations could be incorporated in a lead design strategy against any target that is susceptible to mutational resistance. negative design algorithm | DHFR | MRSA | [K.sup.*] design algorithm doi/ 10.1073/pnas.1002162107

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 substratas 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. biophysical algorithms | gramicidin S synthetase | nonribosomal peptide synthetase | protein design

The membrane proximal external region (MPER) of HIV-1 glycoprotein (gp) 41 is involved in viral-host cell membrane fusion. It contains short amino acid sequences that are binding sites for the HIV-1 broadly neutralizing antibodies 2F5, 4E10, and 10E8, making these binding sites important targets for HIV-1 vaccine development. We report a high-resolution structure of a designed MPER trimer assembled on a detergent micelle. The NMR solution structure of this trimeric domain, designated gp41-M-MAT, shows that the three MPER peptides each adopt symmetric a-helical conformations exposing the amino acid side chains of the antibody binding sites. The helices are closely associated at their N termini, bend between the 2F5 and 4E10 epitopes, and gradually separate toward the C termini, where they associate with the membrane. The mAbs 2F5 and 4E10 bind gp41-M-MAT with nanomolar affinities, consistent with the substantial exposure of their respective epitopes in the trimer structure. The traditional structure determination of gp41-M-MAT using the Xplor-NIH protocol was validated by independently determining the structure using the DISCO sparse-data protocol, which exploits geometric arrangement algorithms that guarantee to compute all structures and assignments that satisfy the data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.