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., C$7_{\mathrm{eq}}$ ($β$-sheet structure) and C$7_{\mathrm{ax}}$ (left handed $α$-helix structure), have been primarily characterized using the main chain dihedral angles, $φ$ (C-N-C$_α$-C) and $ψ$ (N-C$_α$-C-N). However, our recent deep learning combined with "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-C$_α$) [T. Kikutsuji, et al. J. Chem. Phys. 156, 154108 (2022)]. In perspective of extending these insights to other collective variables, a more detailed characterization of transition state is required. In this work, we employ the 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 using the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.
Exploring the conformational dynamics of alanine dipeptide in solution subjected to an external electric field: A nonequilibrium molecular dynamics simulation
Data-Efficient Multidimensional Free Energy Estimation via Physics-Informed Score Learning
Institute for Theoretical Physics, Heidelberg University（海德堡大学理论物理研究所）；Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心）
SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks
University of Maryland（马里兰大学）；Institute for Physical Science and Technology（物理科学与技术研究所）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）
