A learning-to-rank framework for large-scale identification of unknown proteins from cryo-EM and cryo-ET maps
- 1Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, USA
- 2Structural Biology Core, Mayo Clinic, Rochester, MN, USA
Abstract
Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can yield density maps whose molecular identities are unknown. Predicted-structure databases enable proteome-scale searches, but distinguishing the correct candidates remains challenging, particularly for intermediate-resolution maps without clear side-chain features. We developed CryoAssign, an automated framework that combines large-scale structure matching with learning-to-rank prioritization. CryoAssign searches intact density maps without prior segmentation and integrates complementary measures of map-model agreement. On held-out benchmark maps, CryoAssign robustly prioritized the correct candidates. It also identified proteins or narrowed the set of candidates in previously unassigned cryo-EM maps and recovered multiple components from complex cryo-EM and cryo-ET maps. Controlled low-pass filtering showed generally robust recovery across a broad range of intermediate resolutions. CryoAssign provides a scalable approach for identifying proteins from cryo-EM and cryo-ET density maps.
Related
- VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.
- Prediction of Residue-Level Base Preference for DNA-Binding Proteins Using a Multimodal Learning Framework
- Large Language Model Framework for Structured Biomarker Extraction and Triple-Negative Breast Cancer Identification From Pathology Reports: Development and Evaluation Study.
- Identification and characterization of two transmembrane proteins required for virulence of Ustilago maydis
- FAMUS: a few-shot learning framework for large-scale protein annotation
BibTeX
@misc{hu2026learningtorank,
title = {{A learning-to-rank framework for large-scale identification of unknown proteins from cryo-EM and cryo-ET maps}},
author = {Qi Hu and Georges Mer},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.12.758677},
url = {https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A learning-to-rank framework for large-scale identification of unknown proteins from cryo-EM and cryo-ET maps
AU - Qi Hu
AU - Georges Mer
PY - 2026
DA - 2026/10/12
PB - bioRxiv
DO - 10.64898/2026.10.12.758677
UR - https://www.biorxiv.org/content/10.64898/2026.10.12.758677v1
AB - Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can yield density maps whose molecular identities are unknown. Predicted-structure databases enable proteome-scale searches, but distinguishing the correct candidates remains challenging, particularly for intermediate-resolution maps without clear side-chain features. We developed CryoAssign, an automated framework that combines large-scale structure matching with learning-to-rank prioritization. CryoAssign searches intact density maps without prior segmentation and integrates complementary measures of map-model agreement. On held-out benchmark maps, CryoAssign robustly prioritized the correct candidates. It also identified proteins or narrowed the set of candidates in previously unassigned cryo-EM maps and recovered multiple components from complex cryo-EM and cryo-ET maps. Controlled low-pass filtering showed generally robust recovery across a broad range of intermediate resolutions. CryoAssign provides a scalable approach for identifying proteins from cryo-EM and cryo-ET density maps.
SN - 2692-8205
ER -