Universality and Specificity in Protein Fluctuation Dynamics

Copperman, Jeremy; Dinpajooh, Mohammadhasan; Beyerle, Eric R.; Guenza, Marina

doi:10.1103/physrevlett.119.158101

Cited by 11 publications

(10 citation statements)

References 47 publications

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“…Through multiple investigations considering nine different proteins [83][84][85], the authors have demonstrated that LE4PD is capable of reproducing NMR relaxation time scales as well as nuclear Overhauser effects. The LE4PD model has also recently been employed to identify universal hierarchical scaling features underlying sequence-specific protein dynamics, resembling the directed polymer in random media model [86].…”

Section: G Application To Proteinsmentioning

confidence: 99%

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Rudzinski

2019

Computation

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Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. The removal of degrees of freedom from the system modifies the relationship between the relative time scales of distinct dynamical processes through both a loss of friction and a "smoothing" of the free-energy landscape. While these effects typically result in faster dynamics, decreasing the computational expense of the model, they also obscure the connection to the true dynamics of the system. The lack of consistent dynamics is a serious limitation for CG models, which not only prevents quantitatively accurate predictions of dynamical observables but can also lead to qualitatively incorrect descriptions of the characteristic dynamical processes. With many methods available for optimizing the structural and thermodynamic properties of chemically-specific CG models, recent years have seen a stark increase in investigations addressing the accurate description of dynamical properties generated from CG simulations. In this review, we present an overview of these efforts, ranging from bottom-up parametrizations of generalized Langevin equations to refinements of the CG force field based on a Markov state modeling framework. We aim to make connections between seemingly disparate approaches, while laying out some of the major challenges as well as potential directions for future efforts.

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Section: G Application To Proteinsmentioning

confidence: 99%

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Rudzinski

2019

Computation

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“…8,12,13 In this study, we revisit the PCA formalism and formally connect it to a Langevin equation of motion, which was de- veloped to identify slow dynamical modes and study their kinetics in protein dynamics. [14][15][16][17]37 Like the PCA, the LE4PD decomposes the protein's motion into an orthogonal set of collective coordinates or modes.…”

Section: Discussionmentioning

confidence: 99%

“…To account for the effects of energy barriers in the decay of the correlations of the residue fluctuations, such as those shown in Figure 1, the friction coefficient in Eq. 6 is re-normalized using a Kramers-type approach 17,51 : , where F MAD,a = median(| F ( θ a , ϕ a ) − min( F ( θ a , ϕ a )) |) is an average free-energy barrier calculated for LE4PD-XYZ mode a using the median absolute deviation (MAD) 52,53 from the minimum of energy on the surface. This approach rescales the diffusive mode timescales as Using the MAD statistic removes any poorly sampled regions of the energy surface when the calculating the barrier, and using the MAD to rescale the friction coefficients has previously been shown to be effective in describing the slow-down in the decay of the M 1 (t) time correlation function calculated from the LE4PD theory at the 2.5 ns timescale for ubiquitin, 14 the same protein under study here.…”

Section: Contribution Of the High Energy Barriers In The Fluctuation Dynamics Of Proteins Detected By The Pca And Le4pd-xyz Methodsmentioning

confidence: 99%

“…In this study, we make a formal connection between the PCA expressed in the cartesian coordinates of a protein's alpha-carbons and an approach we have developed to analyze the slow modes in protein dynamics, called the Langevin Equation for Protein Dynamics (LE4PD) [14][15][16][17] . The LE4PD theory, initially formalized as an isotropic equation of motion, is extended here to describe the anisotropic dynamics of proteins in the LE4PD-XYZ method.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between slow, anisotropic LE4PD fluctuations and the Principal Component Analysis modes of Ubiquitin

Beyerle

Guenza

2021

Preprint

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Proteins’ biological function and folding mechanisms are often guided by large-scale, slow motions, which involve crossing high energy barriers. In a simulation trajectory, these slow fluctuations are commonly identified using a principal component analysis (PCA). Despite the popularity of this method, a complete analysis of its predictions based on the physics of protein motion has been so far limited. This study formally connects the PCA to a Langevin model of protein dynamics and analyzes the contributions of energy barriers and hydrodynamic interactions to the slow PCA modes of motion. To do so, we introduce an anisotropic extension of the Langevin Equation for Protein Dynamics, called the LE4PD-XYZ, which formally connects to the PCA ‘essential dynamics’. The LE4PD-XYZ is an accurate coarse-grained diffusive method to model protein motion, which describes anisotropic fluctuations in the protein’s alpha-carbons. The LE4PD accounts for hydrodynamic effects and mode-dependent free-energy barriers. This study compares large-scale anisotropic fluctuations identified by the LE4PD-XYZ to the mode-dependent PCA’s predictions, starting from a microsecond-long alpha-carbon molecular dynamics atomistic trajectory of the protein ubiquitin. We observe that the inclusion of free-energy barriers and hydrodynamic interactions has important effects on the identification and timescales of ubiquitin’s slow modes.

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“…In this study, we make a formal connection between the PCA expressed in the cartesian coordinates of a protein's alpha-carbons and an approach we have developed to analyze the slow modes in protein dynamics, called the Langevin Equation for Protein Dynamics (LE4PD) [13][14][15][16][17][18] . The LE4PD theory, initially formalized as an isotropic equation of motion, is extended here to describe the anisotropic dynamics of proteins in the LE4PD-XYZ method.…”

Section: Introductionmentioning

confidence: 99%

Comparison between slow anisotropic LE4PD fluctuations and the principal component analysis modes of ubiquitin

Beyerle

Guenza

2021

The Journal of Chemical Physics

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Proteins' biological function and folding mechanisms are often guided by large-scale, slow motions, which involve crossing high energy barriers. In a simulation trajectory, these slow fluctuations are commonly identified using a principal component analysis (PCA). Despite the popularity of this method, a complete analysis of its predictions based on the physics of protein motion has been so far limited. This study formally connects the PCA to a Langevin model of protein dynamics and analyzes the contributions of energy barriers and hydrodynamic interactions to the slow PCA modes of motion. To do so, we introduce an anisotropic extension of the Langevin Equation for Protein Dynamics, called the LE4PD-XYZ, which formally connects to the PCA 'essential dynamics'. The LE4PD-XYZ is an accurate coarse-grained diffusive method to model protein motion, which describes anisotropic fluctuations in the protein's alpha-carbons. The LE4PD accounts for hydrodynamic effects and mode-dependent free-energy barriers. This study compares large-scale anisotropic fluctuations identified by the LE4PD-XYZ to the mode-dependent PCA's predictions, starting from a microsecond-long alpha-carbon molecular dynamics atomistic trajectory of the protein ubiquitin. We observe that the inclusion of free-energy barriers and hydrodynamic interactions has important effects on the identification and timescales of ubiquitin's slow modes..

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Universality and Specificity in Protein Fluctuation Dynamics

Cited by 11 publications

References 47 publications

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties

Comparison between slow, anisotropic LE4PD fluctuations and the Principal Component Analysis modes of Ubiquitin

Comparison between slow anisotropic LE4PD fluctuations and the principal component analysis modes of ubiquitin

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