Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach

Abstract

The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C7eq (β-sheet structure) and C7ax (left handed α-helix structure), have been primarily characterized using the main chain dihedral angles, φ (C–N–Cα–C) and ψ (N–Cα–C–N). However, our recent deep learning combined with the “Explainable AI” (XAI) framework has shown that the transition state can be adequately captured by a free energy landscape using φ and θ (O–C–N–Cα) [Kikutsuji et al., J. Chem. Phys. 156, 154108 (2022)]. In the perspective of extending these insights to other collective variables, a more detailed characterization of the transition state is required. In this work, we employ interatomic distances and bond angles as input variables for deep learning rather than the conventional and more elaborate dihedral angles. Our approach utilizes deep learning to investigate whether changes in the main chain dihedral angle can be expressed in terms of interatomic distances and bond angles. Furthermore, by incorporating XAI into our predictive analysis, we quantified the importance of each input variable and succeeded in clarifying the specific interatomic distance that affects the transition state. The results indicate that constructing a free energy landscape based on the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.

Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK

“…The transient dynamics of MD trajectories from AF2-generated intermediates motivate one to use them in path sampling methods. Future directions include extending the current approach toward TPS and concomitant reaction coordinate (RC) estimation. ,− TPS and RC estimation have recently been combined with deep learning to automate the process. In particular, autonomous TPS, AIMMD, is a powerful method to identify molecular transition mechanisms .…”