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      Modelling proteins’ hidden conformations to predict antibiotic resistance

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          Abstract

          TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design.

          Abstract

          Expression of TEM β-lactamase is a predominant mechanism underlying antibiotic resistance in pathogenic Gram-negative bacteria. Here, the authors use Markov state models to reveal and experimentally confirm hidden conformations that determine TEM substrate specificity.

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          Canonical sampling through velocity-rescaling

          We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. We illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent on the thermostat parameter also with regard to the dynamic properties.
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            Comparative Protein Structure Modeling Using MODELLER.

            Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Curr. Protoc. Bioinform. 47:5.6.1-5.6.32. © 2014 by John Wiley & Sons, Inc. Copyright © 2014 John Wiley & Sons, Inc.
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              Intrinsic dynamics of an enzyme underlies catalysis.

              A unique feature of chemical catalysis mediated by enzymes is that the catalytically reactive atoms are embedded within a folded protein. Although current understanding of enzyme function has been focused on the chemical reactions and static three-dimensional structures, the dynamic nature of proteins has been proposed to have a function in catalysis. The concept of conformational substates has been described; however, the challenge is to unravel the intimate linkage between protein flexibility and enzymatic function. Here we show that the intrinsic plasticity of the protein is a key characteristic of catalysis. The dynamics of the prolyl cis-trans isomerase cyclophilin A (CypA) in its substrate-free state and during catalysis were characterized with NMR relaxation experiments. The characteristic enzyme motions detected during catalysis are already present in the free enzyme with frequencies corresponding to the catalytic turnover rates. This correlation suggests that the protein motions necessary for catalysis are an intrinsic property of the enzyme and may even limit the overall turnover rate. Motion is localized not only to the active site but also to a wider dynamic network. Whereas coupled networks in proteins have been proposed previously, we experimentally measured the collective nature of motions with the use of mutant forms of CypA. We propose that the pre-existence of collective dynamics in enzymes before catalysis is a common feature of biocatalysts and that proteins have evolved under synergistic pressure between structure and dynamics.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                06 October 2016
                2016
                : 7
                Affiliations
                [1 ]Department of Biochemistry & Molecular Biophysics, Washington University School of Medicine , 660 South Euclid Avenue, St Louis, Missouri 63110, USA
                [2 ]Department of Chemistry, Washington University in St Louis, One Brookings Drive , St Louis, Missouri 63130, USA
                [3 ]Department of Biomedical Engineering, and Center for Biological Systems Engineering, Washington University in St Louis , One Brookings Drive, St Louis, Missouri 63130, USA
                Author notes
                Article
                ncomms12965
                10.1038/ncomms12965
                5477488
                27708258
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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