EscalNet: Learn isotropic representation space for biomolecular dynamics based on effective energy

TL;DR

EscalNet introduces a neural network with Fourier analysis to denoise biomolecular trajectories, create isotropic representations, and identify structural determinants, improving dynamical analysis and classification of biomolecular states.

Contribution

The paper presents a novel neural network architecture that combines Fourier Transform analysis with deep learning to enhance biomolecular trajectory analysis and interpretability.

Findings

Produces smoother state similarity matrices

Generates more uniform feature distributions

Identifies key structural determinants linked to energy landscapes

Abstract

Deep learning has emerged as a powerful framework for analyzing biomolecular dynamics trajectories, enabling efficient representations that capture essential system dynamics and facilitate mechanistic studies. We propose a neural network architecture incorporating Fourier Transform analysis to process trajectory data, achieving dual objectives: eliminating high-frequency noise while preserving biologically critical slow conformational dynamics, and establishing an isotropic representation space through the last hidden layer for enhanced dynamical quantification. Comparative protein simulations demonstrate our approach generates more uniform feature distributions than linear regression methods, evidenced by smoother state similarity matrices and clearer classification boundaries. Moreover, by using saliency score, we identified key structural determinants linked to effective energy landscapes governing system dynamics. We believe that the fusion of neural network features with physical order parameters creates a robust analytical framework for advancing biomolecular trajectory analysis.