3f0q

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Staphylococcus aureus dihydrofolate reductase complexed with NADPH and 2,4-Diamino-5-[3-(3-methoxy-5-(2,6-dimethylphenyl)phenyl)but-1-ynyl]-6-methylpyrimidineStaphylococcus aureus dihydrofolate reductase complexed with NADPH and 2,4-Diamino-5-[3-(3-methoxy-5-(2,6-dimethylphenyl)phenyl)but-1-ynyl]-6-methylpyrimidine

Structural highlights

3f0q is a 1 chain structure with sequence from Staab. Full crystallographic information is available from OCA. For a guided tour on the structure components use FirstGlance.
Ligands:,
Gene:dfrB (STAAB)
Activity:Dihydrofolate reductase, with EC number 1.5.1.3
Resources:FirstGlance, OCA, PDBe, RCSB, PDBsum, ProSAT

Function

[Q2YY41_STAAB] Key enzyme in folate metabolism. Catalyzes an essential reaction for de novo glycine and purine synthesis, and for DNA precursor synthesis (By similarity).[PIRNR:PIRNR000194]

Evolutionary Conservation

Check, as determined by ConSurfDB. You may read the explanation of the method and the full data available from ConSurf.

Publication Abstract from PubMed

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( *), 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.

Predicting resistance mutations using protein design algorithms.,Frey KM, Georgiev I, Donald BR, Anderson AC Proc Natl Acad Sci U S A. 2010 Jul 19. PMID:20643959[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

See Also

References

  1. Frey KM, Georgiev I, Donald BR, Anderson AC. Predicting resistance mutations using protein design algorithms. Proc Natl Acad Sci U S A. 2010 Jul 19. PMID:20643959

3f0q, resolution 2.08Å

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