ArticleJournal of chemical theory and computation2024
Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Structure and Dynamics of the HIV-1 Envelope Protein on the Virion Envelope.Journal of the American Chemical Society · 2026Article
- Free energy of collagen-mimetic peptide dimerization and implications for fibrillization.Biophysical journal · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Structural Heterogeneity of the Membrane-Interacting Region of the HIV-1 Envelope Glycoprotein.Journal of the American Chemical Society · 2025Article
- High-Throughput Computation of Anharmonic Low-Frequency Protein Vibrations.The journal of physical chemistry. B · 2025Article
- Bottom-up Coarse-Grained Models of Asymmetric Membranes.The journal of physical chemistry. B · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
- Free Energy, Rates, and Mechanism of Transmembrane Dimerization in Lipid Bilayers from Dynamically Unbiased Molecular Dynamics Simulations.The journal of physical chemistry. B · 2025Article
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2 authors.
Funding
Abstract
The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been computed from one-dimensional free energy surfaces derived from sampling along a single collective variable. Importantly, recent advances in enhanced sampling methodology have facilitated the characterization of multidimensional free energy landscapes, often exposing multiple thermodynamically important minima missed by more restrictive sampling methods. A key to the effectiveness of this multidimensional sampling approach is the identification of collective variables that effectively define the configurational space of dissociated and associated states. Here we present the application of two machine learning methods for the unbiased determination of collective variables for enhanced sampling and analysis of protein-protein association. Our results both validate prior work, based on intuition derived collective variables, and demonstrate the effectiveness of the machine learning methods for the identification of collective variables for association reactions in complex biomolecular systems.
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