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bioRxiv · 10.64898/2026.03.02.709104

Progressive Backmapping of Highly Coarse-Grained Protein Models

Zhu, Y.·Remington, J. M.·Song, S.·Yang, B.·Magee, B. P.·Schneebeli, S. T.·Li, J.
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Abstract

Reconstructing all-atom (AA) structures from highly coarse-grained (HCG) models remains a significant challenge in multiscale molecular dynamics (MD) simulations, particularly for mesoscale biomolecular assemblies that are beyond the reach of conventional MD methods. Building upon ProNet Backmapping, a neural-network-based thermodynamically consistent approach, we introduce a progressive backmapping framework that reconstructs AA models in a stepwise manner across neighboring resolutions, for example, from a 3-residue-per-site HCG model to a 1-residue-per-site model, then to an AA model. This progressive backmapping method achieves high accuracy across a wide range of proteins and effectively reconstructs flexible linkers in multidomain architectures. Moreover, it supports hierarchical reconstruction of complex protein assemblies, including multiple virus-like particles spanning tens of nanometers and containing hundreds of subunits. Using this framework, we demonstrate--for the first time--the ability to hierarchically backmap entire viral assemblies from HCG to full AA resolution, covering at least three different resolutions. Overall, our method provides a scalable framework for incorporating atomistic detail into mesoscale simulations of complex systems across many applications in chemistry and biology. Table of contents figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/709104v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@4af423org.highwire.dtl.DTLVardef@e2669borg.highwire.dtl.DTLVardef@1be80eforg.highwire.dtl.DTLVardef@2e679_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Zhu, Y., Remington, J. M., Song, S., Yang, B., Magee, B. P., Schneebeli, S. T., Li, J.. 2026-03-04. Progressive Backmapping of Highly Coarse-Grained Protein Models. https://doi.org/10.64898/2026.03.02.709104

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