Determining Atomistic SAXS Models of Tri-Ubiquitin Chains from Bayesian Analysis of Accelerated Molecular Dynamics Simulations

Bowerman, Samuel; Rana, Ambar S. J. B.; Rice, Amy; Pham, Grace H.; Strieter, Eric R.; Wereszczynski, Jeff

doi:10.1021/acs.jctc.7b00059

Cited by 20 publications

(19 citation statements)

References 93 publications

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“…As mentioned in the introduction, there are several ways to combine MD simulations with SAS data (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), but we here used the Bayesian/Maximum Entropy (BME) method (3) and the above-calculated SAXS and SANS intensities to reweight the trajectories. For details of BME see (3) as well as code and examples online https://github.com/KULL-Centre/BME.…”

Section: Combining MD Simulations and Sas Data By Bayesian Reweightingmentioning

confidence: 99%

“…In that case, however, simulations and experiments may be used synergistically to generate and refine the description of flexible molecules. Thus, as described by us and others, SAXS and molecular simulations can be combined to determine a structural ensemble that represents the system, and is compatible with the information in the force field and the experimental constraints from SAXS (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 96%

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Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Larsen

Wang²,

Bottaro

et al. 2019

Preprint

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AbstractMany proteins contain multiple folded domains separated by flexible linkers, and the ability to describe the structure and conformational heterogeneity of such flexible systems pushes the limits of structural biology. Using the three-domain protein TIA-1 as an example, we here combine coarse-grained molecular dynamics simulations with previously measured small-angle scattering data to study the conformation of TIA-1 in solution. We show that while the coarse-grained potential (Martini) in itself leads to too compact conformations, increasing the strength of protein-water interactions results in ensembles that are in very good agreement with experiments. We show how these ensembles can be refined further using a Bayesian/Maximum Entropy approach, and examine the robustness to errors in the energy function. In particular we find that as long as the initial simulation is relatively good, reweighting against experiments is very robust. We also study the relative information in X-ray and neutron scattering experiments and find that refining against the SAXS experiments leads to improvement in the SANS data. Our results suggest a general strategy for studying the conformation of multi-domain proteins in solution that combines coarse-grained simulations with small-angle X-ray scattering data that are generally most easy to obtain. These results may in turn be used to design further small-angle neutron scattering experiments that exploit contrast variation through 1H/2H isotope substitutions.

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Section: Combining MD Simulations and Sas Data By Bayesian Reweightingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Larsen

Wang²,

Bottaro

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Simulations have been used to predict chemical shifts and directly compare these results to nuclear magnetic resonance (NMR) experiments [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Simulations have also been used to recapitulate small angle X-ray scattering (SAXS) data [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Temporal information can be gleaned in NMR or SAXS by a diverse variety of means, including peak broadening in NMR, or by performing SAXS in timeresolved modes [38].…”

Section: Introductionmentioning

confidence: 99%

Quantitative comparison between sub-millisecond time resolution single-molecule FRET measurements and 10-second molecular simulations of a biosensor protein

et al. 2020

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Molecular Dynamics (MD) simulations seek to provide atomic-level insights into conformationally dynamic biological systems at experimentally relevant time resolutions, such as those afforded by single-molecule fluorescence measurements. However, limitations in the time scales of MD simulations and the time resolution of single-molecule measurements have challenged efforts to obtain overlapping temporal regimes required for close quantitative comparisons. Achieving such overlap has the potential to provide novel theories, hypotheses, and interpretations that can inform idealized experimental designs that maximize the detection of the desired reaction coordinate. Here, we report MD simulations at time scales overlapping with in vitro single-molecule Förster (fluorescence) resonance energy transfer (smFRET) measurements of the amino acid binding protein LIV-BPSS at sub-millisecond resolution. Computationally efficient all-atom structure-based simulations, calibrated against explicit solvent simulations, were employed for sampling multiple cycles of LIV-BPSS clamshell-like conformational changes on the time scale of seconds, examining the relationship between these events and those observed by smFRET. The MD simulations agree with the smFRET measurements and provide valuable information on local dynamics of fluorophores at their sites of attachment on LIV-BPSS and the correlations between fluorophore motions and large-scale conformational changes between LIV-BPSS domains. We further utilize the MD simulations to inform the interpretation of smFRET data, including Förster radius (R0) and fluorophore orientation factor (κ2) determinations. The approach we describe can be readily extended to distinct biochemical systems, allowing for the interpretation of any FRET system conjugated to protein or ribonucleoprotein complexes, including those with more conformational processes, as well as those implementing multi-color smFRET.

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“…The BEES algorithm is designed to find the theoretical solution ensemble that uses the fewest number of populations to accurately describes the experimental data. This algorithm is briefly presented here ( Fig 1), but further details can be found in the supplemental text and elsewhere 22 . In short, experimental data are gathered and post-processed prior to using the BEES module.…”

Section: Bees Algorithmmentioning

confidence: 99%

“…This approach, which is an extension of the BSS-SAXS technique 13 , compares solution ensembles of a variety of sub-ensembles from a combination of potential scattering states. Originally, we used this method to fit ensembles of covalently linked ubiquitin trimers, and we observed that the algorithm could produce ensemble models that robustly resisted overfitting 22 .…”

Section: Introductionmentioning

confidence: 99%

BEES: Bayesian Ensemble Estimation from SAS

Bowerman

Curtis

Clayton

et al. 2018

Preprint

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1AbstractMany biomolecular complexes exist in a flexible ensemble of states in solution which are necessary to perform their biological function. Small angle scattering (SAS) measurements are a popular method for characterizing these flexible molecules due to their relative ease of use and ability to simultaneously probe the full ensemble of states. However, SAS data is typically low-dimensional and difficult to interpret without the assistance of additional structural models. In theory, experimental SAS curves can be reconstituted from a linear combination of theoretical models, although this procedure carries significant risk of overfitting the inherently low-dimensional SAS data. Previously, we developed a Bayesian-based method for fitting ensembles of model structures to experimental SAS data that rigorously avoids overfitting. However, we have found that these methods can be difficult to incorporate into typical SAS modeling workflows, especially for users that are not experts in computational modeling. To this end, we present the “Bayesian Ensemble Estimation from SAS” (BEES) program. Two forks of BEES are available, the primary one existing as module for the SASSIE webserver and a developmental version that is a standalone python program. BEES allows users to exhaustively sample ensemble models constructed from a library of theoretical states and to interactively analyze and compare each model’s performance. The fitting routine also allows for secondary data sets to be supplied, thereby simultaneously fitting models to both SAS data as well as orthogonal information. The flexible ensemble of K63-linked ubiquitin trimers is presented as an example of BEES’ capabilities.

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Determining Atomistic SAXS Models of Tri-Ubiquitin Chains from Bayesian Analysis of Accelerated Molecular Dynamics Simulations

Cited by 20 publications

References 93 publications

Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution

Quantitative comparison between sub-millisecond time resolution single-molecule FRET measurements and 10-second molecular simulations of a biosensor protein

BEES: Bayesian Ensemble Estimation from SAS

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