Refinement of<i>α</i>-synuclein ensembles against SAXS data: Comparison of force fields and methods

Ahmed, Mustapha Carab; Skaanning, Line K; Jussupow, Alexander; Newcombe, Estella A.; Kragelund, Birthe B.; Camilloni, Carlo; Langkilde, Annette Eva; Lindorff‐Larsen, Kresten

doi:10.1101/2021.01.15.426794

Cited by 10 publications

(15 citation statements)

References 64 publications

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“…S6 and S7). They also show, in line with previous observations (66,68), that even when starting from somewhat different priors, the posterior distributions tend to be substantially similar. Noteworthy, the results are robust to the choice of and 0 so that very similar results are obtained even when using the global minimum from our analyses of Hst5, Sic1, Tau and TIA-1.…”

Section: Assessing the Influence Of The Prior On The Parameters Searchsupporting

confidence: 89%

“…In this context, there are two related questions that we here address. First, as we have also examined previously (66,68), is the question of how much the distribution of conformations after reweighting depends on the prior that is used. Second, is the question of how much the and 0 parameters depend on the prior.…”

Section: Assessing the Influence Of The Prior On The Parameters Searchmentioning

confidence: 99%

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Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data

Pesce

Lindorff‐Larsen

2021

Preprint

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Intrinsically disordered proteins and flexible regions in multi-domain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modelling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model, i.e. an algorithm to calculate experimental observables from individual conformations or ensembles. In many cases, this involve one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle X-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer to the background-subtracted experimental data. Often, one also needs to fit a scale and an offset for the SAXS data, but across the entire ensemble. Here, we present a protocol to dissect the effect of free-parameters on the calculated SAXS intensities, and to identify a reliable set of values. We have implemented this procedure in our Bayesian/Maximum Entropy framework for ensemble refinement, and demonstrate the results on three intrinsically disordered proteins and a three-domain protein connected by flexible linkers. Our results show that the resulting ensembles can depend substantially on the parameters used for hydration effects, and suggests that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.

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Section: Assessing the Influence Of The Prior On The Parameters Searchsupporting

confidence: 89%

Section: Assessing the Influence Of The Prior On The Parameters Searchmentioning

confidence: 99%

Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data

Pesce

Lindorff‐Larsen

2021

Preprint

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“…While experimental NMR [53][54][55][56][57][58][59][60][61][62] , and high-quality molecular simulations [63][64][65][66][67][68][69][70][71][72][73][74] are some of the most accurate methods for determining the (dis)ordered nature and dynamics of proteins, fast and computationally efficient methods play an important role.…”

Section: Discussionmentioning

confidence: 99%

AlphaFold2: A role for disordered protein prediction?

Wilson

Choy

Karttunen

2021

Preprint

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The development of AlphaFold2 was a paradigm-shift in the structural biology community; herein we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that a naive use of Dictionary of Secondary Structure of Proteins (DSSP) to separate ordered from disordered regions leads to a dramatic overestimation in disorder content, and that the Predicted Aligned Error (PAE) provides a much more rigorous metric. In addition, we show that even when used for disorder prediction, conventional predictors can outperform the PAE in disorder identification, and note an interesting relationship between the PAE and secondary structure that may explain our observations and hints at a broader application of the PAE to IDP dynamics.

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“…Experimental accuracy can be obtained by Metainference via the introduction of restraints toward a set of experimental data. Different issues could affect the success of Metainference simulations, including the quality and quantity of experimental data ( Löhr et al, 2017 ) and the quality of the forward model, as also discussed in this special issue ( Ahmed et al, 2021 ). Here, we highlighted how the combination of MetaD and Metainference (M&M) could create an additional issue related to the number of effective replicas.…”

Section: Discussionmentioning

confidence: 99%

How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case

Paissoni

Camilloni

2021

Front. Mol. Biosci.

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The reliability and usefulness of molecular dynamics simulations of equilibrium processes rests on their statistical precision and their capability to generate conformational ensembles in agreement with available experimental knowledge. Metadynamics Metainference (M&M), coupling molecular dynamics with the enhanced sampling ability of Metadynamics and with the ability to integrate experimental information of Metainference, can in principle achieve both goals. Here we show that three different Metadynamics setups provide converged estimate of the populations of the three-states populated by a model peptide. Errors are estimated correctly by block averaging, but higher precision is obtained by performing independent replicates. One effect of Metadynamics is that of dramatically decreasing the number of effective frames resulting from the simulations and this is relevant for M&M where the number of replicas should be large enough to capture the conformational heterogeneity behind the experimental data. Our simulations allow also us to propose that monitoring the relative error associated with conformational averaging can help to determine the minimum number of replicas to be simulated in the context of M&M simulations. Altogether our data provides useful indication on how to generate sound conformational ensemble in agreement with experimental data.

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Refinement ofα-synuclein ensembles against SAXS data: Comparison of force fields and methods

Cited by 10 publications

References 64 publications

Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data

Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data

AlphaFold2: A role for disordered protein prediction?

How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case

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