Using Data-Reduction Techniques to Analyze Biomolecular Trajectories

Tribello, Gareth A.; Gasparotto, Piero

doi:10.1007/978-1-4939-9608-7_19

Cited by 6 publications

(11 citation statements)

References 78 publications

(98 reference statements)

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“…However, the interpretation of the MD data in terms of molecular mechanisms is not straightforward, because of the very high number of variables, and because it is hard to decipher the precise mechanistic details on how residues functionally cooperate. This problem prompted the development of a variety of methods aimed at dimensionality reduction 60,61 . Among these methods, PCA analysis 53 of the Cartesian coordinate covariance matrix has been widely used to determine collective variables.…”

Section: Discussionmentioning

confidence: 99%

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Taddese

Garnier

Abdi

et al. 2020

Sci Rep

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The dynamic structure of proteins is essential for their functions and may include large conformational transitions which can be studied by molecular dynamics (MD) simulations. However, details of these transitions are difficult to automatically track. To facilitate their analysis, we developed two scores of correlation between sidechain dihedral angles. The CIRCULAR and OMES scores are computed from, respectively, dihedral angle values and rotamer distributions. As a case study, we applied our methods to an activation-like transition of the chemokine receptor CXCR4, observed during accelerated MD simulations. The principal component analysis of the correlation matrices was consistent with the networking structure of the top ranking pairs. Both scores identify a set of residues whose “collaborative” sidechain rotamerization immediately preceded or accompanied the conformational transition of CXCR4. Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways.

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Section: Discussionmentioning

confidence: 99%

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Taddese

Garnier

Abdi

et al. 2020

Sci Rep

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“…A common way to reduce the size of a training set is to employ a landmark selection scheme before performing a dimensionality reduction. − The idea is to select a subset of the feature samples (i.e., landmarks) representing the underlying characteristics of the simulation data.…”

Section: Methodsmentioning

confidence: 99%

Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling

Rydzewski

Valsson

2021

J. Phys. Chem. A

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Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations. There we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. In theory, these CVs should separate different metastable states and correspond to the slow degrees of freedom of the studied physical process. To this aim, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). Our work builds upon a parametric version of stochastic neighbor embedding. The technique automatically learns CVs that map a high-dimensional feature space to a low-dimensional latent space via a deep neural network. We introduce several new advancements to stochastic neighbor embedding methods that make MRSE especially suitable for enhanced sampling simulations: (1) weight-tempered random sampling as a landmark selection scheme to obtain training data sets that strike a balance between equilibrium representation and capturing important metastable states lying higher in free energy; (2) a multiscale representation of the high-dimensional feature space via a Gaussian mixture probability model; and (3) a reweighting procedure to account for training data from a biased probability distribution. We show that MRSE constructs low-dimensional CVs that can correctly characterize the different metastable states in three model systems: the Müller-Brown potential, alanine dipeptide, and alanine tetrapeptide.

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“…As a final test of our embeddings, we follow the approach presented by Tribello and Gasparotto 83,84 to compare different dimensionality reduction methods. We calculate distances between points in the high-dimensional feature space and the corresponding distances between points in the low-dimensional latent (i.e., CV) space given by the embeddings.…”

Section: B Alanine Dipeptidementioning

confidence: 99%

“…Instead, we need to select an appropriate number of landmark features (∼ 1000) that best represent the underlying simulation data. 83,84 Ideally, we want the landmark features to be representative of the equilibrium distribution. In unbiased simulations, we can achieve this by selecting landmarks at random, or by collecting landmarks at some given frequency.…”

Section: Well-tempered Landmark Selectionmentioning

confidence: 99%

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Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling

Rydzewski,

Valsson

2020

Preprint

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Machine learning methods provide a general framework for automatically finding and representing the essential characteristics of simulation data. This task is particularly crucial in enhanced sampling simulations, where we seek a few generalized degrees of freedom, referred to as collective variables (CVs), to represent and drive the sampling of the free energy landscape. These CVs should separate the different metastable states and correspond to the slow degrees of freedom. For this task, we propose a new method that we call multiscale reweighted stochastic embedding (MRSE). The technique automatically finds CVs by learning a low-dimensional embedding of the high-dimensional feature space to the latent space via a deep neural network. Our work builds upon the popular t-distributed stochastic neighbor embedding approach. We introduce several new aspects to stochastic neighbor embedding algorithms that makes MRSE especially suitable for enhanced sampling simulations: (1) a well-tempered selection scheme for the landmark features that gives close to equilibrium representation of the training data; (2) a multiscale representation via Gaussian mixture to model the probabilities of being neighbors in the high-dimensional feature space; and (3) a reweighting procedure to account for the training data being drawn from a biased probability distribution. To test the performance of MRSE, we use it to obtain low-dimensional CVs for two model systems, the Müller-Brown potential and alanine dipeptide, and provide a thorough analysis of the results.

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Using Data-Reduction Techniques to Analyze Biomolecular Trajectories

Cited by 6 publications

References 78 publications

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling

Multiscale reweighted stochastic embedding (MRSE): Deep learning of collective variables for enhanced sampling

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