21 research outputs found

    A Score of the Ability of a Three-Dimensional Protein Model to Retrieve Its Own Sequence as a Quantitative Measure of Its Quality and Appropriateness

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    BACKGROUND: Despite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel. PRINCIPAL FINDINGS: The approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449-460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function. CONCLUSION: Because the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone

    The communication between the zinc and nickel sites in dimeric HypA: Metal recognition and pH sensing.

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    Helicobacter pylori, a pathogen that colonizes the human stomach, requires the nickel-containing metalloenzymes urease and NiFe-hydrogenase to survive this low pH environment. The maturation of both enzymes depends on the metallochaperone, HypA. HypA contains two metal sites, an intrinsic zinc site and a low-affinity nickel binding site. X-ray absorption spectroscopy (XAS) shows that the structure of the intrinsic zinc site of HypA is dynamic, and able to sense both nickel loading and pH changes. At pH 6.3, an internal pH that occurs during acid shock, the zinc site undergoes unprecedented ligand substitutions to convert from a Zn(Cys)(4) site to a Zn(His)(2)(Cys)(2) site. NMR spectroscopy shows that binding of Ni(II) to HypA results in paramagnetic broadening of resonances near the N-terminus. NOEs between the β-CH(2) protons of Zn cysteinyl ligands are consistent with a strand-swapped HypA dimer. Addition of nickel causes resonances from zinc binding motif and other regions to double, indicating more than one conformation can exist in solution. Although the structure of the high-spin, 5–6 coordinate Ni(II) site is relatively unaffected by pH, the nickel binding stoichiometry is decreased from one per monomer to one per dimer at pH = 6.3. Mutation of any cysteine residue in the zinc binding motif results in a zinc site structure similar to that found for holo-WT-HypA at low pH and is unperturbed by the addition of nickel. Mutation of the histidines that flank the CXXC motifs results in a zinc site structure that is similar to holo-WT-HypA at neutral pH (Zn(Cys)(4)) and is no longer responsive to nickel binding or pH changes. Using an in vitro urease activity assay, it is shown that the recombinant protein is sufficient for recovery of urease activity in cell lysate from a HypA deletion mutant, and that mutations in the zinc-binding motif result in a decrease in recovered urease activity. The results are interpreted in terms of a model wherein HypA controls the flow of nickel traffic in the cell in response to nickel availability and pH

    Protein recognition in ferredoxin-P450 electron transfer in the class I CYP199A2 system from Rhodopseudomonas palustris

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    CYP199A2 from Rhodopseudomonas palustris CGA009 is a heme monooxygenase that catalyzes the oxidation of para-substituted benzoic acids. CYP199A2 activity is reconstituted by a class I electron transfer chain consisting of the associated [2Fe-2S] ferredoxin palustrisredoxin (Pux) and a flavoprotein palustrisredoxin reductase (PuR). Another [2Fe-2S] ferredoxin, palustrisredoxin B (PuxB; RPA3956) has been identified in the genome. PuxB shares sequence identity and motifs with vertebrate-type ferredoxins involved in Fe-S cluster assembly but also 50% identity with Pux and it mediates electron transfer from PuR to CYP199A2, albeit with lower steady-state turnover activity: 99 nmol (nmol P450)(-1)min(-1) for 4-methoxybenzoic acid oxidation compared with 1,438 nmol (nmol P450)(-1 )min(-1) for Pux. This difference mainly arises from weak CYP199A2-PuxB binding (K (m) 34.3 vs. 0.45 microM for Pux) rather than slow electron transfer (k (cat) 19.1 vs. 37.9 s(-1) for Pux). Comparison of the 2.0-A-resolution crystal structure of the PuxB A105R mutant with other vertebrate-type, P450-associated ferredoxins revealed similar protein folds but also significant differences in some loop regions. Therefore, PuxB offers a platform for studying ferredoxin-P450 recognition in class I P450 systems. Substitution of PuxB residues at key locations with those in Pux shows that Ala42, Cys43, and Ala44 in the [2Fe-2S] cluster binding loop and Met66 are important in electron transfer from PuxB to CYP199A2, whereas Phe73 and the C-terminal Ala105 were involved in both protein binding and electron transfer.Stephen G. Bell, Feng Xu, Eachan O. D. Johnson, Ian M. Forward, Mark Bartlam, Zihe Rao, Luet-Lok Won
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