Deep learning based design of buried hydrogen bond networks with HBDesigner
- 1University of North Carolina at Chapel Hill
Abstract
Accurate design of hydrogen-bonding (H-bonding) interactions is a longstanding goal in protein design, as they can facilitate specific protein-protein interactions while improving the solubility of the proteins in the unbound state. Despite this, computational design of H-bond networks remains underexplored in the deep learning era. Here, we present HBDesigner, a novel algorithm for H-bond network design. Through a combination of deep learning-based sampling and atomistic energy scoring, HBDesigner outperforms existing tools in designing connected H-bond networks onto protein scaffolds. We demonstrate the usefulness of HBDesigner by creating monomeric proteins with buried polar interactions and homodimers with extended interface H-bond networks, and by installing specificity into a family of homologous heterodimers where prior design tools fail to do so. The ability to design H-bond networks into arbitrary protein scaffolds should be broadly useful for a wide range of design applications.
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BibTeX
@misc{dieckhaus2026deep,
title = {{Deep learning based design of buried hydrogen bond networks with HBDesigner}},
author = {H. Dieckhaus and B. T. Harvey and T. Mulikova and J. T. Horenstein and N. I. Nicely and N. Z. Randolph and B. Kuhlman},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.08.730848},
url = {https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Deep learning based design of buried hydrogen bond networks with HBDesigner
AU - H. Dieckhaus
AU - B. T. Harvey
AU - T. Mulikova
AU - J. T. Horenstein
AU - N. I. Nicely
AU - N. Z. Randolph
AU - B. Kuhlman
PY - 2026
DA - 2026/06/08
PB - bioRxiv
DO - 10.64898/2026.06.08.730848
UR - https://www.biorxiv.org/content/10.64898/2026.06.08.730848v2
AB - Accurate design of hydrogen-bonding (H-bonding) interactions is a longstanding goal in protein design, as they can facilitate specific protein-protein interactions while improving the solubility of the proteins in the unbound state. Despite this, computational design of H-bond networks remains underexplored in the deep learning era. Here, we present HBDesigner, a novel algorithm for H-bond network design. Through a combination of deep learning-based sampling and atomistic energy scoring, HBDesigner outperforms existing tools in designing connected H-bond networks onto protein scaffolds. We demonstrate the usefulness of HBDesigner by creating monomeric proteins with buried polar interactions and homodimers with extended interface H-bond networks, and by installing specificity into a family of homologous heterodimers where prior design tools fail to do so. The ability to design H-bond networks into arbitrary protein scaffolds should be broadly useful for a wide range of design applications.
ER -