Modulation of HIV protease flexibility by the T80N mutation

Zhou, Hao; Li, Shangyang; Badger, John; Nalivaika, E.A.; Cai, Yufeng; Foulkes-Murzycki, Jennifer E.; Schiffer, Celia A.; Makowski, Lee

doi:10.1002/prot.24737

Cited by 6 publications

(9 citation statements)

References 34 publications

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“…(B)]. The conclusion (similar to that observed in HIV protease) is that dynamics are responsible for the majority of intensity change observed on IHP binding—and that the molecule is more flexible/dynamic when IHP binds.…”

Section: Resultsmentioning

confidence: 99%

Modulation of hemoglobin dynamics by an allosteric effector

et al. 2017

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Hemoglobin (Hb) is an extensively studied paradigm of proteins that alter their function in response to allosteric effectors. Models of its action have been used as prototypes for structure‐function relationships in many proteins, and models for the molecular basis of its function have been deeply studied and extensively argued. Recent reports suggest that dynamics may play an important role in its function. Relatively little is known about the slow, correlated motions of hemoglobin subunits in various structural states because experimental and computational strategies for their characterization are challenging. Allosteric effectors such as inositol hexaphosphate (IHP) bind to both deoxy‐Hb and HbCO, albeit at different sites, leading to a lowered oxygen affinity. The manner in which these effectors impact oxygen binding is unclear and may involve changes in structure, dynamics or both. Here we use neutron spin echo measurements accompanied by wide‐angle X‐ray scattering to show that binding of IHP to HbCO results in an increase in the rate of coordinated motions of Hb subunits relative to one another with little if any change in large scale structure. This increase of large‐scale dynamics seems to be coupled with a decrease in the average magnitude of higher frequency modes of individual residues. These observations indicate that enhanced dynamic motions contribute to the functional changes induced by IHP and suggest that they may be responsible for the lowered oxygen affinity triggered by these effectors.

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Section: Resultsmentioning

confidence: 99%

Modulation of hemoglobin dynamics by an allosteric effector

et al. 2017

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“…Sigma‐r plots can also be compared with X‐ray solution scattering data by using the method “Vector‐length convolution,” which convoluting the pair correlation function p ( r ) from a calculated rigid scattering pattern by XS and a Gaussian distribution with the half‐width σ = c × r e to predict a more flexible structural ensemble, where r is the vector length of the protein. Sigma‐r plot from X‐ray solution scattering data can be carried out by plotting

σ

from the best prediction as a function of r , which give us a connection between MD simulation data and experimental observed data.…”

Section: Discussionmentioning

confidence: 99%

Visualizing global properties of a molecular dynamics trajectory

Zhou

Makowski

2015

Proteins

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Molecular dynamics (MD) trajectories are very large data sets that contain substantial information about the dynamic behavior of a protein. Condensing these data into a form that can provide intuitively useful understanding of the molecular behavior during the trajectory is a substantial challenge that has received relatively little attention. Here, we introduce the sigma-r plot, a plot of the standard deviation of intermolecular distances as a function of that distance. This representation of global dynamics contains within a single, one-dimensional plot, the average range of motion between pairs of atoms within a macromolecule. Comparison of sigma-r plots calculated from 10 ns trajectories of proteins representing the four major SCOP fold classes indicates diversity of dynamic behaviors which are recognizably different among the four classes. Differences in domain structure and molecular weight also produce recognizable features in sigma-r plots, reflective of differences in global dynamics. Plots generated from trajectories with progressively increasing simulation time reflect the increased sampling of the structural ensemble as a function of time. Single amino acid replacements can give rise to changes in global dynamics detectable through comparison of sigma-r plots. Dynamic behavior of substructures can be monitored by careful choice of interatomic vectors included in the calculation. These examples provide demonstrations of the utility of the sigma-r plot to provide a simple measure of the global dynamics of a macromolecule.

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“…Comparison of sigma‐r plots from WAXS patterns and MD simulations from HIV protease indicated that the protein in solution was undergoing structural fluctuations with length scales much greater than those indicated by the simulations. This observation suggested either that the MD simulations did not reflect a full structural ensemble or that the solution conditions supported far greater structural fluctuations than reflected in the MD trajectories.…”

Section: Introductionmentioning

confidence: 91%

“…Although containing no dynamical information, per se, WAXS data reflect the structural heterogeneity intrinsic to the entire conformational ensemble in solution. Comparison of WAXS from proteins in solution with that predicted for rigid proteins often identifies discrepancies that can be attributed to structural fluctuations and can provide information about the spatial scale and nature of those fluctuations in a manner not possible with SAXS. The Debye formula should, in principle, provide a simple basis for the calculation of solution scattering from atomic coordinates, but accounting for solvent molecules in the hydration layer and the volume of solvent excluded by the protein complicates the calculation.…”

Section: Introductionmentioning

confidence: 99%

“…However, solution scattering data is highly sensitive to structural fluctuations, and the scattering from a flexible protein in solution can be very different from that predicted assuming it is rigid . Many approaches to this problem have been discussed, such as producing a representative ensemble of protein structures, predicting the scattering from each member of the ensemble and modeling the observed scattering as a linear combination of these scattering patterns; or predicting the SAXS profile by taking account of the thermal motions on the molecular component of the solution scattering of a single state of the macromolecule .…”

Section: Introductionmentioning

confidence: 99%

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Predicting X‐ray solution scattering from flexible macromolecules

et al. 2018

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Wide-angle X-ray solution scattering (WAXS) patterns contain substantial information about the structure and dynamics of a protein. Solution scattering from a rigid protein can be predicted from atomic coordinate sets to within experimental error. However, structural fluctuations of proteins in solution can lead to significant changes in the observed intensities. The magnitude and form of these changes contain information about the nature and spatial extent of structural fluctuations in the protein. Molecular dynamics (MD) simulations based on a crystal structure and selected force field generate models for protein internal motions, and here we demonstrate that they can be used to predict the impact of structural fluctuations on solution scattering data. In cases where the observed and calculated intensities correspond, we can conclude that the X-ray scattering provides direct experimental validation of the structural and MD results. In cases where calculated and observed intensities are at odds, the inconsistencies can be used to determine the origins of these discrepancies. They may be because of overestimates or underestimates of structural fluctuations in MD simulations, under-sampling of the structural ensemble in the simulations, errors in the structural model, or a mismatch between the experimental conditions and the parameters used in carrying out the MD simulation.

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Modulation of HIV protease flexibility by the T80N mutation

Cited by 6 publications

References 34 publications

Modulation of hemoglobin dynamics by an allosteric effector

Modulation of hemoglobin dynamics by an allosteric effector

Visualizing global properties of a molecular dynamics trajectory

Predicting X‐ray solution scattering from flexible macromolecules

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