85
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      The Role of Distant Mutations and Allosteric Regulation on LovD Active Site Dynamics

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Natural enzymes have evolved to perform their cellular functions under complex selective pressures, which often require their catalytic activities to be regulated by other proteins. We contrasted a natural enzyme, LovD, which acts on a protein-bound (LovF) acyl substrate, with a laboratory-generated variant that was transformed by directed evolution to accept instead a small free acyl thioester, and no longer requires the acyl carrier protein. The resulting 29-mutant variant is 1000-fold more efficient in the synthesis of the drug simvastatin than the wild-type LovD. This is the first non-patent report of the enzyme currently used for the manufacture of simvastatin, as well as the intermediate evolved variants. Crystal structures and microsecond molecular dynamics simulations revealed the mechanism by which the laboratory-generated mutations free LovD from dependence on protein-protein interactions. Mutations dramatically altered conformational dynamics of the catalytic residues, obviating the need for allosteric modulation by the acyl carrier LovF.

          Related collections

          Most cited references49

          • Record: found
          • Abstract: found
          • Article: not found

          How fast-folding proteins fold.

          An outstanding challenge in the field of molecular biology has been to understand the process by which proteins fold into their characteristic three-dimensional structures. Here, we report the results of atomic-level molecular dynamics simulations, over periods ranging between 100 μs and 1 ms, that reveal a set of common principles underlying the folding of 12 structurally diverse proteins. In simulations conducted with a single physics-based energy function, the proteins, representing all three major structural classes, spontaneously and repeatedly fold to their experimentally determined native structures. Early in the folding process, the protein backbone adopts a nativelike topology while certain secondary structure elements and a small number of nonlocal contacts form. In most cases, folding follows a single dominant route in which elements of the native structure appear in an order highly correlated with their propensity to form in the unfolded state.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Refinement of severely incomplete structures with maximum likelihood in BUSTER-TNT.

            BUSTER-TNT is a maximum-likelihood macromolecular refinement package. BUSTER assembles the structural model, scales observed and calculated structure-factor amplitudes and computes the model likelihood, whilst TNT handles the stereochemistry and NCS restraints/constraints and shifts the atomic coordinates, B factors and occupancies. In real space, in addition to the traditional atomic and bulk-solvent models, BUSTER models the parts of the structure for which an atomic model is not yet available ('missing structure') as low-resolution probability distributions for the random positions of the missing atoms. In reciprocal space, the BUSTER structure-factor distribution in the complex plane is a two-dimensional Gaussian centred around the structure factor calculated from the atomic, bulk-solvent and missing-structure models. The errors associated with these three structural components are added to compute the overall spread of the Gaussian. When the atomic model is very incomplete, modelling of the missing structure and the consistency of the BUSTER statistical model help structure building and completion because (i) the accuracy of the overall scale factors is increased, (ii) the bias affecting atomic model refinement is reduced by accounting for some of the scattering from the missing structure, (iii) the addition of a spatial definition to the source of incompleteness improves on traditional Luzzati and sigmaA-based error models and (iv) the program can perform selective density modification in the regions of unbuilt structure alone.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              ClusPro: a fully automated algorithm for protein-protein docking.

              ClusPro (http://nrc.bu.edu/cluster) represents the first fully automated, web-based program for the computational docking of protein structures. Users may upload the coordinate files of two protein structures through ClusPro's web interface, or enter the PDB codes of the respective structures, which ClusPro will then download from the PDB server (http://www.rcsb.org/pdb/). The docking algorithms evaluate billions of putative complexes, retaining a preset number with favorable surface complementarities. A filtering method is then applied to this set of structures, selecting those with good electrostatic and desolvation free energies for further clustering. The program output is a short list of putative complexes ranked according to their clustering properties, which is automatically sent back to the user via email.
                Bookmark

                Author and article information

                Journal
                101231976
                32624
                Nat Chem Biol
                Nat. Chem. Biol.
                Nature chemical biology
                1552-4450
                1552-4469
                25 April 2014
                13 April 2014
                June 2014
                01 December 2014
                : 10
                : 6
                : 431-436
                Affiliations
                [1 ]Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095-1569
                [2 ]Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA 90095
                [3 ]Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095
                [4 ]Codexis, Inc., 200 Penobscot Drive, Redwood City, California 94063
                Author notes
                [* ]Correspondence and requests for materials should be addressed to K.N.H., T.O.Y., Y.T. and G.H. houk@ 123456chem.ucla.edu , yitang@ 123456ucla.edu , yeates@ 123456mbi.ucla.edu , gjalt.huisman@ 123456codexis.com
                [5]

                These authors contributed equally to this work.

                Article
                NIHMS575619
                10.1038/nchembio.1503
                4028369
                24727900
                5c651651-25ae-4dff-82cf-28cc8fae3f49
                History
                Categories
                Article

                Biochemistry
                Biochemistry

                Comments

                Comment on this article