Latent-Space Residue-Pair Networks from Molecular Dynamics Distinguish Ligand-Activated and SAVI-Associated STING
- 1National Institute of Advanced Industrial Science and Technology
Abstract
Stimulator of interferon genes (STING) provides an informative model system for developing methods to extract and compare residue-pair fluctuation patterns from molecular dynamics (MD) trajectories. We combined a one-dimensional convolutional autoencoder trained on apo STING with temporal mean pooling, Mahalanobis-distance clustering, and residue-pair similarity networks to compare apo, ligand-bound, and SAVI-associated multi-mutant datasets using 25-dimensional latent descriptors. Networks constructed from clusters capturing state-dependent fluctuation patterns showed lid-dominated organization in the ligand-bound dataset and a1b1-enriched, structurally distributed relationships involving residue 281 in the multi-mutant dataset. These findings identify contrasting residue-pair fluctuation signatures in the analyzed trajectories and provide an interpretable framework for generating hypotheses about ligand-associated and mutation-associated dynamic allostery.
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BibTeX
@misc{tsuchiya2026latentspace,
title = {{Latent-Space Residue-Pair Networks from Molecular Dynamics Distinguish Ligand-Activated and SAVI-Associated STING}},
author = {Y. Tsuchiya and Y. Yonezawa},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.08.757787},
url = {https://www.biorxiv.org/content/10.64898/2026.10.08.757787v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Latent-Space Residue-Pair Networks from Molecular Dynamics Distinguish Ligand-Activated and SAVI-Associated STING
AU - Y. Tsuchiya
AU - Y. Yonezawa
PY - 2026
DA - 2026/10/08
PB - bioRxiv
DO - 10.64898/2026.10.08.757787
UR - https://www.biorxiv.org/content/10.64898/2026.10.08.757787v1
AB - Stimulator of interferon genes (STING) provides an informative model system for developing methods to extract and compare residue-pair fluctuation patterns from molecular dynamics (MD) trajectories. We combined a one-dimensional convolutional autoencoder trained on apo STING with temporal mean pooling, Mahalanobis-distance clustering, and residue-pair similarity networks to compare apo, ligand-bound, and SAVI-associated multi-mutant datasets using 25-dimensional latent descriptors. Networks constructed from clusters capturing state-dependent fluctuation patterns showed lid-dominated organization in the ligand-bound dataset and a1b1-enriched, structurally distributed relationships involving residue 281 in the multi-mutant dataset. These findings identify contrasting residue-pair fluctuation signatures in the analyzed trajectories and provide an interpretable framework for generating hypotheses about ligand-associated and mutation-associated dynamic allostery.
ER -