uu.seUppsala University Publications
Change search
Refine search result
1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Cortina, George A.
    et al.
    Univ Virginia, Dept Mol Physiol & Biol Phys, Box 800886, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Box 800886, Charlottesville, VA 22908 USA.
    Hays, Jennifer M.
    Univ Virginia, Dept Mol Physiol & Biol Phys, Box 800886, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Box 800886, Charlottesville, VA 22908 USA.
    Kasson, P. M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab. Univ Virginia, Dept Mol Physiol & Biol Phys, Box 800886, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Box 800886, Charlottesville, VA 22908 USA.
    Conformational Intermediate That Controls KPC-2 Catalysis and Beta-Lactam Drug Resistance2018In: ACS Catalysis, ISSN 2155-5435, E-ISSN 2155-5435, Vol. 8, no 4, p. 2741-2747Article in journal (Refereed)
    Abstract [en]

    The KPC-2 carbapenemase enzyme is responsible for drug resistance in the majority of carbapenem-resistant Gram-negative bacterial infections in the United States. A better understanding of what permits KPC-2 to hydrolyze carbapenem antibiotics and how this might be inhibited is thus of fundamental interest and great practical importance to development of better anti-infectives. By correlating molecular dynamics simulations with experimental enzyme kinetics, we identified conformational changes that control KPC-2's ability to hydrolyze carbapenem antibiotics. Related beta-lactamase enzymes can interconvert between catalytically permissive and catalytically nonpermissive forms of an acylenzyme intermediate critical to drug hydrolysis. identify a similar equilibrium in KPC-2 and analyze the determinants of this conformational change. Because the conformational dynamics of KPC-2 are complex and sensitive to allosteric changes, we develop an information-theoretic approach to identify key determinants of this change. We measure unbiased estimators of the reaction coordinate between catalytically permissive and nonpermissive states, perform information-theoretic feature selection, and, using restrained molecular dynamics simulations, validate the protein conformational changes predicted to control catalytically permissive geometry. We identify two binding pocket residues that control the conformational transitions between catalytically active and inactive forms of KPC-2. Mutations to one of these residues, Trp105, lower the stability of the catalytically permissive state in simulations and have reduced experimental k(cat) values that show a strong linear correlation with the simulated catalytically permissive state lifetimes. This understanding can be leveraged to predict the drug resistance of further KPC-2 mutants and help design inhibitors to combat extreme drug resistance.

  • 2.
    Ferreira, Ricardo J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kasson, P. M.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Dept Biomed Engn & Mol Physiol, Box 800886, Charlottesville, VA 22908 USA;Univ Virginia, Biol Phys, Box 800886, Charlottesville, VA 22908 USA.
    Antibiotic Uptake Across Gram-Negative Outer Membranes: Better Predictions Towards Better Antibiotics2019In: ACS INFECTIOUS DISEASES, ISSN 2373-8227, Vol. 5, no 12, p. 2096-2104Article in journal (Refereed)
    Abstract [en]

    Crossing the Gram-negative bacterial membrane poses a major barrier to antibiotic development, as many small molecules that can biochemically inhibit key bacterial processes are rendered microbiologically ineffective by their poor cellular uptake. The outer membrane is the major permeability barrier for many drug-like molecules, and the chemical properties that enable efficient uptake into mammalian cells fail to predict bacterial uptake. We have developed a computational method for accurate prospective prediction of outer membrane uptake of drug-like molecules, which we combine with a new medium-throughput experimental assay of outer membrane vesicle swelling. Parallel molecular dynamics simulations of compound uptake through Escherichia coli (E. coli) OmpF are used to successfully and quantitatively predict experimental permeabilities measured via either outer membrane swelling or prior liposome-swelling measurements. These simulations are analyzed using an inhomogeneous solubility-diffusion model to yield predictions of permeability. For most polar molecules we test, outer membrane permeability also correlates well with whole-cell uptake. The ability to accurately predict and measure outer membrane uptake of a wide variety of small molecules will enable simpler determination of which molecular scaffolds and which derivatives are most promising prior to extensive chemical synthesis. It will also assist in formulating a more systematic understanding of the chemical determinants of outer membrane permeability.

  • 3.
    Goronzy, I. N.
    et al.
    Stanford Univ, Dept Chem, Stanford, CA 94305 USA..
    Rawle, R. J.
    Univ Virginia, Dept Mol Physiol & Biomed Engn, Box 800886, Charlottesville, VA 22908 USA..
    Boxer, S. G.
    Stanford Univ, Dept Chem, Stanford, CA 94305 USA..
    Kasson, Peter M.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Biology. Univ Virginia, Dept Mol Physiol & Biomed Engn, Box 800886, Charlottesville, VA 22908 USA..
    Cholesterol enhances influenza binding avidity by controlling nanoscale receptor clustering2018In: Chemical Science, ISSN 2041-6520, E-ISSN 2041-6539, Vol. 9, no 8, p. 2340-2347Article in journal (Refereed)
    Abstract [en]

    Influenza virus infects cells by binding to sialylated glycans on the cell surface. While the chemical structure of these glycans determines hemagglutinin-glycan binding affinity, bimolecular affinities are weak, so binding is avidity-dominated and driven by multivalent interactions. Here, we show that membrane spatial organization can control viral binding. Using single-virus fluorescence microscopy, we demonstrate that the sterol composition of the target membrane enhances viral binding avidity in a dose-dependent manner. Binding shows a cooperative dependence on concentration of receptors for influenza virus, as would be expected for a multivalent interaction. Surprisingly, the ability of sterols to promote viral binding is independent of their ability to support liquid-liquid phase separation in model systems. We develop a molecular explanation for this observation via molecular dynamics simulations, where we find that cholesterol promotes small-scale clusters of glycosphingolipid receptors. We propose a model whereby cholesterol orders the monomeric state of glycosphingolipid receptors, reducing the entropic penalty of receptor association and thus favoring multimeric complexes without phase separation. This model explains how cholesterol and other sterols control the spatial organization of membrane receptors for influenza and increase viral binding avidity. A natural consequence of this finding is that local cholesterol concentration in the plasma membrane of cells may alter the binding avidity of influenza virions. Furthermore, our results demonstrate a form of cholesterol-dependent membrane organization that does not involve lipid rafts, suggesting that cholesterol's effect on cell membrane heterogeneity is likely the interplay of several different factors.

  • 4.
    Hays, Jennifer M.
    et al.
    Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA;Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA.
    Cafiso, David S.
    Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA;Univ Virginia, Dept Chem, Mccormick Rd, Charlottesville, VA 22903 USA.
    Kasson, P. M.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22903 USA;Univ Virginia, Dept Mol Physiol & Biophys, Charlottesville, VA 22903 USA.
    Hybrid Refinement of Heterogeneous Conformational Ensembles Using Spectroscopic Data2019In: Journal of Physical Chemistry Letters, ISSN 1948-7185, E-ISSN 1948-7185, Vol. 10, no 12, p. 3410-3414Article in journal (Refereed)
    Abstract [en]

    Multistructured biomolecular systems play crucial roles in a wide variety of cellular processes but have resisted traditional methods of structure determination, which often resolve only a few low-energy states. High-resolution structure determination using experimental methods that yield distributional data remains extremely difficult, especially when the underlying conformational ensembles are quite heterogeneous. We have therefore developed a method to integrate sparse, multimultimodal spectroscopic data to obtain high-resolution estimates of conformational ensembles. We have tested our method by incorporating double electron-electron resonance data on the soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) protein syntaxin-1a into biased molecular dynamics simulations. We find that our method substantially outperforms existing state-of-the-art methods in capturing syntaxins open-closed conformational equilibrium and further yields new conformational states that are consistent with experimental data and may help in understanding syntaxin's function. Our improved methods for refining heterogeneous conformational ensembles from spectroscopic data will greatly accelerate the structural understanding of such systems.

  • 5. Hays, Jennifer M.
    et al.
    Kieber, Marissa K.
    Li, Jason Z.
    Han, Ji In
    Columbus, Linda
    Kasson, Peter M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Refinement of highly flexible protein structures using simulation-guided spectroscopy2018In: Angewandte Chemie International Edition, ISSN 1433-7851, E-ISSN 1521-3773Article in journal (Refereed)
    Abstract [en]

    Highly flexible proteins present a special challenge for structure determination because they are multi-structured yet not disordered, so their conformational ensembles are essential for understanding function. Because spectroscopic measurements of multiple conformational populations often provide sparse data, experiment selection is a limiting factor in conformational refinement. We have developed an approach using molecular simulations and information theory to select which experiments best refine conformational ensembles. We test this approach on three flexible proteins. For proteins where a clear mechanistic hypothesis exists, we systematically identify experiments that test this hypothesis. When available data do not yield such mechanistic hypotheses, we identify experiments that significantly outperform structure-guided approaches in conformational refinement. Our approach offers a particular advantage when refining challenging, underdetermined protein conformational ensembles.

  • 6.
    Irrgang, M Eric
    et al.
    University of Virginia.
    Hays, Jennifer M
    University of Virginia.
    Kasson, Peter M.
    University of Virginia.
    gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations.2018In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811Article in journal (Refereed)
    Abstract [en]

    Summary: Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that 1) allows custom potential functions without modifying the simulation package code, 2) maintains the optimized performance of GROMACS, and 3) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable, and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles.

    Availability: LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi.

    Supplementary information: Supplementary data are available at Bioinformatics online.

  • 7.
    Kasson, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Uppsala, Sweden..
    Simulations and Experiments Show a Mechanistic Role for Influenza Fusion Peptides in Membrane Bending and Fusion Stoichiometry2018In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 114, no 3, p. 605A-605AArticle in journal (Other academic)
  • 8.
    Kasson, Peter
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Mol Physiol & Biomed Engn, Charlottesville, VA USA..
    Cortina, George
    Univ Virginia, Mol Physiol & Biomed Engn, Charlottesville, VA USA..
    Latallo, Malgorzata
    Univ Virginia, Mol Physiol & Biomed Engn, Charlottesville, VA USA..
    Understanding allosteric modulation of beta lactamase function and bacterial drug resistance2017In: Abstract of Papers of the American Chemical Society, ISSN 0065-7727, Vol. 254Article in journal (Other academic)
  • 9.
    Kasson, Peter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab. University of Virginia, Charlottesville, United States.
    Jha, Shantenu
    Rutgers University, Piscataway, United States.
    Adaptive ensemble simulations of biomolecules2018In: Current opinion in structural biology, ISSN 0959-440X, E-ISSN 1879-033X, Vol. 52, p. 87-94Article in journal (Refereed)
    Abstract [en]

    Recent advances in both theory and computational power have created opportunities to simulate biomolecular processes more efficiently using adaptive ensemble simulations. Ensemble simulations are now widely used to compute a number of individual simulation trajectories and analyze statistics across them. Adaptive ensemble simulations offer a further level of sophistication and flexibility by enabling high-level algorithms to control simulations-based on intermediate results. We review some of the adaptive ensemble algorithms and software infrastructure currently in use and outline where the complexities of implementing adaptive simulation have limited algorithmic innovation to date. We describe an adaptive ensemble API to overcome some of these barriers and more flexibly and simply express adaptive simulation algorithms to help realize the power of this type of simulation.

  • 10.
    Latallo, M. J.
    et al.
    Univ Virginia, Dept Mol Physiol..
    Cortina, G. A.
    Department of Molecular Physiology, University of Virginia; Department of Biomedical Engineering, University of Virginia.
    Faham, S.
    Univ Virginia, Dept Mol Physiol..
    Nakamoto, R. K.
    Univ Virginia, Dept Mol Physiol..
    Kasson, P. M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab. Univ Virginia, Department of Molecular Physiology; Department of Biomedical Engineering, University of Virginia,.
    Predicting allosteric mutants that increase activity of a major antibiotic resistance enzyme2017In: Chemical Science, ISSN 2041-6520, E-ISSN 2041-6539, Vol. 8, no 9, p. 6484-6492Article in journal (Refereed)
    Abstract [en]

    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.

  • 11.
    Liao, Qinghua
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Biochemistry.
    Lüking, Malin
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Biochemistry.
    Krueger, Dennis M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab. German Ctr Neurodegenerat Dis, Bioinformat Unit, Dept Epigenet & Syst Med Neurodegenerat Dis, Siebold Str 3A, D-37075 Gottingen, Germany.
    Deindl, Sebastian
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Elf, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kasson, Peter M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kamerlin, Shina Caroline Lynn
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Biochemistry.
    Long Time-Scale Atomistic Simulations of the Structure and Dynamics of Transcription Factor-DNA Recognition2019In: Journal of Physical Chemistry B, ISSN 1520-6106, E-ISSN 1520-5207, Vol. 123, no 17, p. 3576-3590Article in journal (Refereed)
    Abstract [en]

    Recent years have witnessed an explosion of interest in computational studies of DNA binding proteins, including both coarse grained and atomistic simulations of transcription factor-DNA recognition, to understand how these transcription factors recognize their binding sites on the DNA with such exquisite specificity. The present study performs microsecond time scale all-atom simulations of the dimeric form of the lactose repressor (Lad), both in the absence of any DNA and in the presence of both specific and nonspecific complexes, considering three different DNA sequences. We examine, specifically, the conformational differences between specific and nonspecific protein DNA interactions, as well as the behavior of the helix-turn-helix motif of Lad when interacting with the DNA. Our simulations suggest that stable Lad binding occurs primarily to bent A-form DNA, with a loss of Lad conformational entropy and optimization of correlated conformational equilibria across the protein. In addition, binding to the specific operator sequence involves a slightly larger number of stabilizing DNA protein hydrogen bonds (in comparison to nonspecific complexes), which may account for the experimentally observed specificity for this operator. In doing so, our simulations provide a detailed atomistic description of potential structural drivers for LacI selectivity.

  • 12.
    Rawle, Robert J.
    et al.
    Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA;Williams Coll, Dept Chem, Williamstown, MA 01267 USA.
    Giraldo, Ana M. Villamil
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Boxer, Steven G.
    Stanford Univ, Dept Chem, Stanford, CA 94305 USA.
    Kasson, Peter M.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Detecting and Controlling Dye Effects in Single-Virus Fusion Experiments2019In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 117, no 3, p. 445-452Article in journal (Refereed)
    Abstract [en]

    Fluorescent dye-dequenching assays provide a powerful and versatile means to monitor membrane fusion events. They have been used in bulk assays, for measuring single events in live cells, and for detailed analysis of fusion kinetics for liposomal, viral, and cellular fusion processes; however, the dyes used also have the potential to perturb membrane fusion. Here, using single-virus measurements of influenza membrane fusion, we show that fluorescent membrane probes can alter both the efficiency and the kinetics of lipid mixing in a dye- and illumination-dependent manner. R18, a dye that is commonly used to monitor lipid mixing between membranes, is particularly prone to these effects, whereas Texas Red is somewhat less sensitive. R18 further undergoes photoconjugation to viral proteins in an illumination-dependent manner that correlates with its inactivation of viral fusion. These results demonstrate how fluorescent probes can perturb measurements of biological activity and provide both data and a method for determining minimally perturbative measurement conditions.

  • 13.
    Rawle, Robert J.
    et al.
    Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Webster, Elizabeth R.
    Stanford Univ, Dept Chem, Stanford, CA 94305 USA.
    Jelen, Marta
    Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Kasson, Peter M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Boxer, Steven G.
    Stanford Univ, Dept Chem, Stanford, CA 94305 USA.
    pH Dependence of Zika Membrane Fusion Kinetics Reveals an Off-Pathway State2018In: ACS CENTRAL SCIENCE, ISSN 2374-7943, Vol. 4, no 11, p. 1503-1510Article in journal (Refereed)
    Abstract [en]

    The recent spread of Zika virus stimulated extensive research on its structure, pathogenesis, and immunology, but mechanistic study of entry has lagged behind, in part due to the lack of a defined reconstituted system. Here, we report Zika membrane fusion measured using a platform that bypasses these barriers, enabling observation of single-virus fusion kinetics without receptor reconstitution. Surprisingly, target membrane binding and low pH are sufficient to trigger viral hemifusion to liposomes containing only neutral lipids. Second, although the extent of hemifusion strongly depends on pH, hemifusion rates are relatively insensitive to pH. Kinetic analysis shows that an off-pathway state is required to capture this pH-dependence and suggests this may be related to viral inactivation. Our surrogate-receptor approach thus yields new understanding of flaviviral entry mechanisms and should be applicable to many emerging viruses.

  • 14.
    Zawada, Katarzyna E.
    et al.
    Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Okamoto, Kenta
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kasson, Peter M.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular biophysics. Univ Virginia, Dept Mol Physiol & Biol Phys, Charlottesville, VA 22908 USA;Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA.
    Influenza Hemifusion Phenotype Depends on Membrane Context: Differences in Cell-Cell and Virus-Cell Fusion2018In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 430, no 5, p. 594-601Article in journal (Refereed)
    Abstract [en]

    Influenza viral entry into the host cell cytoplasm is accomplished by a process of membrane fusion mediated by the viral hemagglutinin protein. Hem agglutinin acts in a pH-triggered fashion, inserting a short fusion peptide into the host membrane followed by refolding of a coiled-coil structure to draw the viral envelope and host membranes together. Mutations to this fusion peptide provide an important window into viral fusion mechanisms and protein-membrane interactions. Here, we show that a well-described fusion peptide mutant, G1S, has a phenotype that depends strongly on the viral membrane context. The G1S mutant is well known to cause a "hemifusion" phenotype based on experiments in transfected cells, where cells expressing G1S hemagglutinin can undergo lipid mixing in a pH triggered fashion similar to virus but will not support fusion pores. We compare fusion by the G1S hemagglutinin mutant expressed either in cells or in influenza virions and show that this hemifusion phenotype occurs in transfected cells but that native virions are able to support full fusion, albeit at a slower rate and 10-100x reduced infectious titer. We explain this with a quantitative model where the G1S mutant, instead of causing an absolute block of fusion, alters the protein stoichiometry required for fusion. This change slightly slows fusion at high hemagglutinin density, as on the viral surface, but at lower hemagglutinin density produces a hemifusion phenotype. The quantitative model thus reproduces the observed virus-cell and cell-cell fusion phenotypes, yielding a unified explanation where membrane context can control the observed viral fusion phenotype. (C) 2018 Elsevier Ltd. All rights reserved.

1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf