1
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Demirtaş K, Erman B, Haliloğlu T. Dynamic correlations: exact and approximate methods for mutual information. Bioinformatics 2024; 40:btae076. [PMID: 38341647 PMCID: PMC10898342 DOI: 10.1093/bioinformatics/btae076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/17/2024] [Accepted: 02/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions. RESULTS Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths. AVAILABILITY AND IMPLEMENTATION Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.
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Affiliation(s)
- Kemal Demirtaş
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
| | - Burak Erman
- Department of Chemical and Biological Engineering, Koc University, 34450 Istanbul, Turkey
| | - Türkan Haliloğlu
- Department of Chemical Engineering, Bogazici University, 34342 Istanbul, Turkey
- Polymer Research Center, Bogazici University, 34342 Istanbul, Turkey
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2
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Corbella M, Pinto GP, Kamerlin SCL. Loop dynamics and the evolution of enzyme activity. Nat Rev Chem 2023; 7:536-547. [PMID: 37225920 DOI: 10.1038/s41570-023-00495-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2023] [Indexed: 05/26/2023]
Abstract
In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.
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Affiliation(s)
- Marina Corbella
- Department of Chemistry, Uppsala University, Uppsala, Sweden
| | - Gaspar P Pinto
- Department of Chemistry, Uppsala University, Uppsala, Sweden
- Cortex Discovery GmbH, Regensburg, Germany
| | - Shina C L Kamerlin
- Department of Chemistry, Uppsala University, Uppsala, Sweden.
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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3
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Sakhrani VV, Ghosh RK, Hilario E, Weiss KL, Coates L, Mueller LJ. Toho-1 β-lactamase: backbone chemical shift assignments and changes in dynamics upon binding with avibactam. JOURNAL OF BIOMOLECULAR NMR 2021; 75:303-318. [PMID: 34218390 PMCID: PMC9122098 DOI: 10.1007/s10858-021-00375-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/23/2021] [Indexed: 06/13/2023]
Abstract
Backbone chemical shift assignments for the Toho-1 β-lactamase (263 amino acids, 28.9 kDa) are reported based on triple resonance solution-state NMR experiments performed on a uniformly 2H,13C,15N-labeled sample. These assignments allow for subsequent site-specific characterization at the chemical, structural, and dynamical levels. At the chemical level, titration with the non-β-lactam β-lactamase inhibitor avibactam is found to give chemical shift perturbations indicative of tight covalent binding that allow for mapping of the inhibitor binding site. At the structural level, protein secondary structure is predicted based on the backbone chemical shifts and protein residue sequence using TALOS-N and found to agree well with structural characterization from X-ray crystallography. At the dynamical level, model-free analysis of 15N relaxation data at a single field of 16.4 T reveals well-ordered structures for the ligand-free and avibactam-bound enzymes with generalized order parameters of ~ 0.85. Complementary relaxation dispersion experiments indicate that there is an escalation in motions on the millisecond timescale in the vicinity of the active site upon substrate binding. The combination of high rigidity on short timescales and active site flexibility on longer timescales is consistent with hypotheses for achieving both high catalytic efficiency and broad substrate specificity: the induced active site dynamics allows variously sized substrates to be accommodated and increases the probability that the optimal conformation for catalysis will be sampled.
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Affiliation(s)
- Varun V Sakhrani
- Department of Chemistry, University of California Riverside, Riverside, CA, 92521, USA
| | - Rittik K Ghosh
- Department of Biochemistry, University of California Riverside, Riverside, CA, 92521, USA
| | - Eduardo Hilario
- Department of Chemistry, University of California Riverside, Riverside, CA, 92521, USA
| | - Kevin L Weiss
- Neutron Scattering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA
| | - Leighton Coates
- Second Target Station, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA.
| | - Leonard J Mueller
- Department of Chemistry, University of California Riverside, Riverside, CA, 92521, USA.
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4
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Modi T, Risso VA, Martinez-Rodriguez S, Gavira JA, Mebrat MD, Van Horn WD, Sanchez-Ruiz JM, Banu Ozkan S. Hinge-shift mechanism as a protein design principle for the evolution of β-lactamases from substrate promiscuity to specificity. Nat Commun 2021; 12:1852. [PMID: 33767175 PMCID: PMC7994827 DOI: 10.1038/s41467-021-22089-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 02/01/2021] [Indexed: 01/31/2023] Open
Abstract
TEM-1 β-lactamase degrades β-lactam antibiotics with a strong preference for penicillins. Sequence reconstruction studies indicate that it evolved from ancestral enzymes that degraded a variety of β-lactam antibiotics with moderate efficiency. This generalist to specialist conversion involved more than 100 mutational changes, but conserved fold and catalytic residues, suggesting a role for dynamics in enzyme evolution. Here, we develop a conformational dynamics computational approach to rationally mold a protein flexibility profile on the basis of a hinge-shift mechanism. By deliberately weighting and altering the conformational dynamics of a putative Precambrian β-lactamase, we engineer enzyme specificity that mimics the modern TEM-1 β-lactamase with only 21 amino acid replacements. Our conformational dynamics design thus re-enacts the evolutionary process and provides a rational allosteric approach for manipulating function while conserving the enzyme active site.
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Affiliation(s)
- Tushar Modi
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ, USA
| | - Valeria A Risso
- Departamento de Quimica Fisica, Facultad de Ciencias, Universidad de Granada, Granada, Spain
- Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, Granada, Spain
| | - Sergio Martinez-Rodriguez
- Departamento de Quimica Fisica, Facultad de Ciencias, Universidad de Granada, Granada, Spain
- Departamento de Bioquimica, Biologia Molecular III e Inmunologia, Universidad de Granada, Granada, Spain
| | - Jose A Gavira
- Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, Granada, Spain
- Laboratorio de Estudios Cristalograficos, Instituto Andaluz de Ciencias de la Tierra, CSIC, Universidad de Granada, Granada, Armilla, Spain
| | - Mubark D Mebrat
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Wade D Van Horn
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
| | - Jose M Sanchez-Ruiz
- Departamento de Quimica Fisica, Facultad de Ciencias, Universidad de Granada, Granada, Spain.
- Unidad de Excelencia de Quimica Aplicada a Biomedicina y Medioambiente (UEQ), Universidad de Granada, Granada, Spain.
| | - S Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, AZ, USA.
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5
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Westerlund AM, Fleetwood O, Pérez-Conesa S, Delemotte L. Network analysis reveals how lipids and other cofactors influence membrane protein allostery. J Chem Phys 2020; 153:141103. [DOI: 10.1063/5.0020974] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Annie M. Westerlund
- KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Oliver Fleetwood
- KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Sergio Pérez-Conesa
- KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Lucie Delemotte
- KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
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6
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Crean RM, Gardner JM, Kamerlin SCL. Harnessing Conformational Plasticity to Generate Designer Enzymes. J Am Chem Soc 2020; 142:11324-11342. [PMID: 32496764 PMCID: PMC7467679 DOI: 10.1021/jacs.0c04924] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Indexed: 02/08/2023]
Abstract
Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.
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Affiliation(s)
- Rory M. Crean
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Jasmine M. Gardner
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Shina C. L. Kamerlin
- Department of Chemistry -
BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
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7
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Mutations Utilize Dynamic Allostery to Confer Resistance in TEM-1 β-lactamase. Int J Mol Sci 2018; 19:ijms19123808. [PMID: 30501088 PMCID: PMC6321620 DOI: 10.3390/ijms19123808] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/27/2018] [Accepted: 11/27/2018] [Indexed: 12/20/2022] Open
Abstract
β-lactamases are enzymes produced by bacteria to hydrolyze β-lactam antibiotics as a common mechanism of resistance. Evolution in such enzymes has been rendering a wide variety of antibiotics impotent, therefore posing a major threat. Clinical and in vitro studies of evolution in TEM-1 β-lactamase have revealed a large number of single point mutations that are responsible for driving resistance to antibiotics and/or inhibitors. The distal locations of these mutations from the active sites suggest that these allosterically modulate the antibiotic resistance. We investigated the effects of resistance driver mutations on the conformational dynamics of the enzyme to provide insights about the mechanism of their long-distance interactions. Through all-atom molecular dynamics (MD) simulations, we obtained the dynamic flexibility profiles of the variants and compared those with that of the wild type TEM-1. While the mutational sites in the variants did not have any direct van der Waals interactions with the active site position S70 and E166, we observed a change in the flexibility of these sites, which play a very critical role in hydrolysis. Such long distance dynamic interactions were further confirmed by dynamic coupling index (DCI) analysis as the sites involved in resistance driving mutations exhibited high dynamic coupling with the active sites. A more exhaustive dynamic analysis, using a selection pressure for ampicillin and cefotaxime resistance on all possible types of substitutions in the amino acid sequence of TEM-1, further demonstrated the observed mechanism. Mutational positions that play a crucial role for the emergence of resistance to new antibiotics exhibited high dynamic coupling with the active site irrespective of their locations. These dynamically coupled positions were neither particularly rigid nor particularly flexible, making them more evolvable positions. Nature utilizes these sites to modulate the dynamics of the catalytic sites instead of mutating the highly rigid positions around the catalytic site.
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8
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Cortina GA, Kasson PM. Predicting allostery and microbial drug resistance with molecular simulations. Curr Opin Struct Biol 2018; 52:80-86. [PMID: 30243041 PMCID: PMC6296865 DOI: 10.1016/j.sbi.2018.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 08/31/2018] [Accepted: 09/07/2018] [Indexed: 11/30/2022]
Abstract
Beta-lactamase enzymes mediate the most common forms of gram-negative antibiotic resistance affecting clinical treatment. They also constitute an excellent model system for the difficult problem of understanding how allosteric mutations can augment catalytic activity of already-competent enzymes. Multiple allosteric mutations have been identified that alter catalytic activity or drug-resistance spectrum in class A beta lactamases, but predicting these in advance continues to be challenging. Here, we review computational techniques based on structure and/or molecular simulation to predict such mutations. Structure-based techniques have been particularly helpful in developing graph algorithms for analyzing critical residues in beta-lactamase function, while classical molecular simulation has recently shown the ability to prospectively predict allosteric mutations increasing beta-lactamase activity and drug resistance. These will ultimately achieve the greatest power when combined with simulation methods that model reactive chemistry to calculate activation free energies directly.
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Affiliation(s)
- George A Cortina
- Departments of Molecular Physiology and of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States
| | - Peter M Kasson
- Departments of Molecular Physiology and of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States; Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala 75146, Sweden.
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9
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Petrović D, Risso VA, Kamerlin SCL, Sanchez-Ruiz JM. Conformational dynamics and enzyme evolution. J R Soc Interface 2018; 15:20180330. [PMID: 30021929 PMCID: PMC6073641 DOI: 10.1098/rsif.2018.0330] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 06/27/2018] [Indexed: 12/21/2022] Open
Abstract
Enzymes are dynamic entities, and their dynamic properties are clearly linked to their biological function. It follows that dynamics ought to play an essential role in enzyme evolution. Indeed, a link between conformational diversity and the emergence of new enzyme functionalities has been recognized for many years. However, it is only recently that state-of-the-art computational and experimental approaches are revealing the crucial molecular details of this link. Specifically, evolutionary trajectories leading to functional optimization for a given host environment or to the emergence of a new function typically involve enriching catalytically competent conformations and/or the freezing out of non-competent conformations of an enzyme. In some cases, these evolutionary changes are achieved through distant mutations that shift the protein ensemble towards productive conformations. Multifunctional intermediates in evolutionary trajectories are probably multi-conformational, i.e. able to switch between different overall conformations, each competent for a given function. Conformational diversity can assist the emergence of a completely new active site through a single mutation by facilitating transition-state binding. We propose that this mechanism may have played a role in the emergence of enzymes at the primordial, progenote stage, where it was plausibly promoted by high environmental temperatures and the possibility of additional phenotypic mutations.
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Affiliation(s)
- Dušan Petrović
- Department of Chemistry, BMC, Uppsala University, Box 576, 751 23 Uppsala, Sweden
| | - Valeria A Risso
- Departamento de Quimica Fisica, Facultad de Ciencias, University of Granada, 18071 Granada, Spain
| | | | - Jose M Sanchez-Ruiz
- Departamento de Quimica Fisica, Facultad de Ciencias, University of Granada, 18071 Granada, Spain
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10
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Jandova Z, Fast D, Setz M, Pechlaner M, Oostenbrink C. Saturation Mutagenesis by Efficient Free-Energy Calculation. J Chem Theory Comput 2018; 14:894-904. [PMID: 29262673 PMCID: PMC5813279 DOI: 10.1021/acs.jctc.7b01099] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
![]()
Single-point mutations
in proteins can greatly influence protein
stability, binding affinity, protein function or its expression per
se. Here, we present accurate and efficient predictions of the free
energy of mutation of amino acids. We divided the complete mutational
free energy into an uncharging step, which we approximate by a third-power
fitting (TPF) approach, and an annihilation step, which we approximate
using the one-step perturbation (OSP) method. As a diverse set of
test systems, we computed the solvation free energy of all amino acid
side chain analogues and obtained an excellent agreement with thermodynamic
integration (TI) data. Moreover, we calculated mutational free energies
in model tripeptides and established an efficient protocol involving
a single reference state. Again, the approximate methods agreed excellently
with the TI references, with a root-mean-square error of only 3.6
kJ/mol over 17 mutations. Our combined TPF+OSP approach does show
not only a very good agreement but also a 2-fold higher efficiency
than full blown TI calculations.
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Affiliation(s)
- Zuzana Jandova
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Vienna A-1190, Austria
| | - Daniel Fast
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Vienna A-1190, Austria
| | - Martina Setz
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Vienna A-1190, Austria
| | - Maria Pechlaner
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Vienna A-1190, Austria
| | - Chris Oostenbrink
- Institute of Molecular Modeling and Simulation, University of Natural Resources and Life Sciences , Vienna A-1190, Austria
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Latallo MJ, Cortina GA, Faham S, Nakamoto RK, Kasson PM. Predicting allosteric mutants that increase activity of a major antibiotic resistance enzyme. Chem Sci 2017; 8:6484-6492. [PMID: 28989673 PMCID: PMC5628580 DOI: 10.1039/c7sc02676e] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 07/17/2017] [Indexed: 11/25/2022] Open
Abstract
Allosteric mutations increasing kcat in a beta lactamase act by changing conformational ensembles of active-site residues identified by machine learning.
The CTX-M family of beta lactamases mediate broad-spectrum antibiotic resistance and are present in the majority of drug-resistant Gram-negative bacterial infections worldwide. Allosteric mutations that increase catalytic rates of these drug resistance enzymes have been identified in clinical isolates but are challenging to predict prospectively. We have used molecular dynamics simulations to predict allosteric mutants increasing CTX-M9 drug resistance, experimentally testing top mutants using multiple antibiotics. Purified enzymes show an increase in catalytic rate and efficiency, while mutant crystal structures show no detectable changes from wild-type CTX-M9. We hypothesize that increased drug resistance results from changes in the conformational ensemble of an acyl intermediate in hydrolysis. Machine-learning analyses on the three top mutants identify changes to the binding-pocket conformational ensemble by which these allosteric mutations transmit their effect. These findings show how molecular simulation can predict how allosteric mutations alter active-site conformational equilibria to increase catalytic rates and thus resistance against common clinically used antibiotics.
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Affiliation(s)
- M J Latallo
- Department of Molecular Physiology , University of Virginia , Box 800886 , Charlottesville , VA 22908 , USA .
| | - G A Cortina
- Department of Molecular Physiology , University of Virginia , Box 800886 , Charlottesville , VA 22908 , USA . .,Department of Biomedical Engineering , University of Virginia , USA
| | - S Faham
- Department of Molecular Physiology , University of Virginia , Box 800886 , Charlottesville , VA 22908 , USA .
| | - R K Nakamoto
- Department of Molecular Physiology , University of Virginia , Box 800886 , Charlottesville , VA 22908 , USA .
| | - P M Kasson
- Department of Molecular Physiology , University of Virginia , Box 800886 , Charlottesville , VA 22908 , USA . .,Department of Biomedical Engineering , University of Virginia , USA.,Science for Life Laboratory , Department of Cell and Molecular Biology , Uppsala University , Sweden
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