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Here, we present a detailed workflow for clustering and enhanced sampling of biomolecular conformations using the ShapeGMM methodology. This approach fits a probabilistic model of biomolecular conformations rooted in the idea that the free energy can be expressed in terms of local fluctuations in atomic positions around metastable states. We demonstrate using a single model system how to generate and fit equilibrium molecular dynamics simulation data. We then demonstrate how to use the resulting model to generate a reaction coordinate between two states, how to sample along that coordinate using metadynamics using our size-and-shape PLUMED module, and how to cluster those biased conformations to obtain a refined equilibrium ShapeGMM model.
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Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM
Quantifying the conformational ensembles of biomolecules is fundamental to describing mechanisms of processes such as protein folding, interconversion between folded states, ligand binding, and allosteric regulation. Accurate quantification of these ensembles remains a challenge for conventional molecular simulations of all but the simplest molecules due to insufficient sampling. Enhanced sampling approaches, such as metadynamics, were designed to overcome this challenge; however, the nonuniform frame weights that result from many of these approaches present an additional challenge to ensemble quantification techniques such as Markov State Modeling or structural clustering. Here, we present rigorous inclusion of nonuniform frame weights into a structural clustering method entitled shapeGMM. The result of frame-weighted shapeGMM is a high dimensional probability density and generative model for the unbiased system from which we can compute important thermodynamic properties such as relative free energies and configurational entropy. The accuracy of this approach is demonstrated by the quantitative agreement between GMMs computed by Hamiltonian reweighting and direct simulation of a coarse-grained helix model system. Furthermore, the relative free energy computed from a shapeGMM probability density of alanine dipeptide reweighted from a metadynamics simulation quantitatively reproduces the underlying free energy in the basins. Finally, the method identifies hidden structures along the actin globular to filamentous-like structural transition from a metadynamics simulation on a linear discriminant analysis coordinate trained on GMM states, illustrating how structural clustering of biased data can lead to biophysical insight. Combined, these results demonstrate that frame-weighted shapeGMM is a powerful approach to quantifying biomolecular ensembles from biased simulations.
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- Award ID(s):
- 2238706
- PAR ID:
- 10504773
- Publisher / Repository:
- ACS
- Date Published:
- Journal Name:
- Journal of Chemical Theory and Computation
- ISSN:
- 1549-9618
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1021/acs.jctc.4c00223
