Protein eXplosion Imaging (PXI): Protein Structures from Laser-Driven Explosions
Abstract
We investigate the structural information retained in the distribution of explosion ion trajectories from proteins subjected to strong ionization. Using molecular-dynamics simulations and machine-learning analysis benchmarked against an analytical approach, we show that low-resolution structural information can be retrieved from the explosion distributions alone. An ensemble of convolutional neural networks trained on simulated spherical ion maps recovers the radius of gyration and the three semi-axes of an ellipsoidal molecular envelope with prediction errors of 1.2 Å and 1.5 Å, respectively, while an analytical ellipsoid charge model returns similar estimates and performs best for globular structures. We further investigate how higher-level structural information and symmetries can be extrapolated from ion measurements. This study supports the viability for structural determination of single proteins without large-scale X-ray facilities.
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BibTeX
@misc{bellisario2026protein,
title = {{Protein eXplosion Imaging (PXI): Protein Structures from Laser-Driven Explosions}},
author = {Alfredo Bellisario and Tomas André and Carl Caleman and Nicuşor Tîmneanu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13538},
eprint = {2609.13538},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13538},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Protein eXplosion Imaging (PXI): Protein Structures from Laser-Driven Explosions
AU - Alfredo Bellisario
AU - Tomas André
AU - Carl Caleman
AU - Nicuşor Tîmneanu
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.13538
UR - https://arxiv.org/abs/2609.13538
AB - We investigate the structural information retained in the distribution of explosion ion trajectories from proteins subjected to strong ionization. Using molecular-dynamics simulations and machine-learning analysis benchmarked against an analytical approach, we show that low-resolution structural information can be retrieved from the explosion distributions alone. An ensemble of convolutional neural networks trained on simulated spherical ion maps recovers the radius of gyration and the three semi-axes of an ellipsoidal molecular envelope with prediction errors of 1.2 Å and 1.5 Å, respectively, while an analytical ellipsoid charge model returns similar estimates and performs best for globular structures. We further investigate how higher-level structural information and symmetries can be extrapolated from ion measurements. This study supports the viability for structural determination of single proteins without large-scale X-ray facilities.
ER -