ArticleNature computational science2025
Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Real-time electrical monitoring of enzymatic catalytic dynamics at the single-molecule level.Nature communications · 2026Article
- Single-Molecule Conductance of Non-Redox Proteins: Mechanisms, Measurements, and Applications.Biomolecules · 2026Review
- An Exact Multiple-Time-Step Variational Formulation for the Committor and the Transition Rate.The journal of physical chemistry. B · 2026Article
- Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.Nature computational science · 2025Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
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5 authors.
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Abstract
Identifying collective variables (CVs) that accurately capture the slowest timescales of protein conformational changes is crucial to comprehend numerous biological processes. Here we introduce memory kernel minimization-based neural networks (MEMnets), a deep learning framework that accurately identifies the slow CVs of biomolecular dynamics. Unlike popular CV-identification methods, which typically assume Markovian dynamics, MEMnets is built on the integrative generalized master equation theory, which incorporates non-Markovian dynamics by encoding them in a memory kernel for continuous CVs. The key innovation of MEMnets is the identification of optimal CVs by minimizing the upper bound for the time-integrated memory kernels through parallel encoder networks. We demonstrate that MEMnets effectively identifies slow CVs involved in the folding of the FIP35 WW domain, revealing two parallel folding pathways. In addition, we illustrate MEMnets' robust numerical stability in identifying meaningful CVs in large biomolecular dynamic systems with limited sampling by applying it to the clamp opening of bacterial RNA polymerase, a much more complex conformational change.
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