Bayesian-Maximum-Entropy reweighting of IDP ensembles based on NMR chemical shifts

Crehuet, Ramón; Jorro, Pedro Juan Buigues; Lindorff‐Larsen, Kresten; Salvatella, Xavier

doi:10.1101/689083

Cited by 6 publications

(4 citation statements)

References 73 publications

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“…In these equations the parameter θ sets the balance between fitting the data (minimizing χ 2 ) and keeping as much as possible of the original simulation (maximizing ϕ eff ). We refer the reader to previous literature about the methods overall and how best to select this parameter ( Andrae et al, 2010 ; Hummer and Köfinger, 2015 ; Bottaro et al, 2018 ; Cesari et al, 2018 ; Köfinger et al, 2019 ; Crehuet et al, 2019 ; Chen et al, 2019 ; Bottaro et al, 2020 ; Orioli et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Fitting side-chain NMR relaxation data using molecular simulations

Kümmerer

Orioli

Harding-Larsen

et al. 2020

Preprint

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Proteins display a wealth of dynamical motions that can be probed using both experiments and simulations. We present an approach to integrate side chain NMR relaxation measurements with molecular dynamics simulations to study the structure and dynamics of these motions. The approach, which we term ABSURDer (Average Block Selection Using Relaxation Data with Entropy Restraints) can be used to find a set of trajectories that are in agreement with relaxation measurements. We apply the method to deuterium relaxation measurements in T4 lysozyme, and show how it can be used to integrate the accuracy of the NMR measurements with the molecular models of protein dynamics afforded by the simulations. We show how fitting of dynamic quantities leads to improved agreement with static properties, and highlight areas needed for further improvements of the approach.

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Section: Resultsmentioning

confidence: 99%

Fitting side-chain NMR relaxation data using molecular simulations

Kümmerer

Orioli

Harding-Larsen

et al. 2020

Preprint

Self Cite

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“…An alternative to the χ 2 r = 1 method is cross-validation, i.e. fitting the optimal ensemble to a separate set of data that has not been used in the analysis [62,82,131,192,193]. The reweighting or experiment-biased simulation may then be done for several values of θ to monitor when the goodness of fit to the unused data starts to decrease.…”

Section: Balance Between Simulations and Experimental Datamentioning

confidence: 99%

“…First, the data needs to be divided into independent subsets, which may sometimes be difficult for highly correlated or interdependent data. Second, as different sources of data may report on very different aspects of the data, they may in practice not be useful for cross validation [193]. Finally, in a more Bayesian approach, the probability at each value of θ may be calculated to find the most probable value [69], or to integrate out θ, treating it as a nuisance parameter [22].…”

Section: Balance Between Simulations and Experimental Datamentioning

confidence: 99%

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Orioli

Larsen

Bottaro

et al. 2020

Progress in Molecular Biology and Translational Science

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167

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Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modelling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to static data, and we discuss more recent developments aimed to analyse time-dependent or time-resolved data.

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“…62 Maximum entropy optimization has become widely popular due to its relation to statistical mechanics and information theory. [63][64][65][66][67][68][69] We further introduced an iterative algorithm based on maximum entropy optimization to create a transferable force field capable of reproducing the radius of gyration for various IDPs (MOFF-IDP). 44 As detailed in the SI, the essence of this algorithm is to reparameterize the protein-specific linear bias derived from maximum entropy optimization with a transferable contact potential between pairs of amino acids,…”

Section: Amino Acid Contact Potential From Maximum Entropy Optimizationmentioning

confidence: 99%

Consistent Force Field Captures Homolog Resolved HP1 Phase Separation

Latham

Zhang

2021

Preprint

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Many proteins have been shown to function via liquid-liquid phase separation. Computational modeling could offer much needed structural details of protein condensates and reveal the set of molecular interactions that dictate their stability. However, the presence of both ordered and disordered domains in these proteins places a high demand on the model accuracy. Here, we present an algorithm to derive a coarse-grained force field, MOFF, that can model both ordered and disordered proteins with consistent accuracy. It combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to ensure that MOFF recreates experimental radii of gyration while predicting the folded structures for globular proteins with lower energy. The theta temperature determined from MOFF separates ordered and disordered proteins at 300 K and exhibits a strikingly linear relationship with amino acid sequence composition. We further applied MOFF to study the phase behavior of HP1, an essential protein for posttranslational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologs, despite their high sequence similarity. We carried out large scale simulations with hundreds of proteins to determine the critical temperature of phase separation and uncover multivalent interactions that stabilize higher-order assemblies. In all, our work makes significant methodological strides to connect theories of ordered and disordered proteins and provides a powerful tool for studying liquid-liquid phase separation with near-atomistic details.

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Bayesian-Maximum-Entropy reweighting of IDP ensembles based on NMR chemical shifts

Cited by 6 publications

References 73 publications

Fitting side-chain NMR relaxation data using molecular simulations

Fitting side-chain NMR relaxation data using molecular simulations

How to learn from inconsistencies: Integrating molecular simulations with experimental data

Consistent Force Field Captures Homolog Resolved HP1 Phase Separation

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