Cross-linking mass spectrometry guided structure prediction of disordered Encephalitozoon hellem proteins
6 affiliations
- 1Department of Pathology, Albert Einstein College of Medicine
- 2Department of Pathogenic Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University
- 3Department of Biochemistry, Albert Einstein College of Medicine
- 4Department of Systems & Computational Biology, Albert Einstein College of Medicine
- 5Department of Genetics, Albert Einstein College of Medicine
- 6Department of Medicine, Albert Einstein College of Medicine
Abstract
Microsporidia such as Encephalitozoon hellem are obligate intracellular human parasites that remain genetically intractable, limiting the functional characterization of their proteomes. Experimental structural studies are challenging, as many proteins in microsporidia comprise a large percentage of disordered regions. Computational structural studies based on homology-based modeling and the use of deep-learning algorithms for microsporidian proteins also remain limited because most have little to no sequence similarity to proteins with solved structures. To address these limitations, we developed an approach that incorporates cross-linking mass spectrometry (XL-MS) data into structure prediction. XL-MS data provide upper-bound distance constraints that can be incorporated into protein deep-learning-based modeling and subsequent docking. Using this approach, we generated a model of two interacting E. hellem spore wall proteins, spore wall protein 1B (Swp1b) and endospore protein 1 (EnP1), which have no clear homologs outside of microsporidia and contain several disordered regions. These proteins are abundant spore wall proteins of microsporidia and previously were not known to interact with one another. The resulting model not only is consistent with the experimental cross-links used to generate the model but was also subsequently confirmed by independently generated XL-MS data. The described AlphaLink-Modeller framework for structure prediction is particularly well suited to proteins with limited homology and/or substantial flexible regions, given that they adopt a defined structural state within a biological context, thereby extending integrative modeling approaches to previously inaccessible targets.
IMPORTANCE: In this article, we demonstrate an improved method for the prediction of the structure of protein complexes with no clear homologs and a considerable number of disordered regions. This is significant because these types of proteins are critical to understanding the biology of organisms, yet model generation of these classes has been inaccessible. The method incorporates experimental data obtained from cross-linking mass spectrometry (XL-MS) into the modeling process, providing a method to integrate such experimental data into computational modeling of the structure of proteins and protein interactions. For this paper, we have used this method for predicting the structure of two highly abundant Encephalitozoon hellem proteins in the spore wall that are important for host cell infection due to this pathogen of humans and other vertebrates.
Related
- Rational design of disordered proteins for sequence–function investigation
- Generating Structural Ensembles of Disordered Proteins with Diffusion Models
- Predictive all-atom simulations of disordered proteins and biomolecular condensates through osmometry-guided force-field optimization
- Solvent-Isotope Effects in Biomolecular Phase Separation and Fibrillation of Disordered Proteins and Peptides
- Proteome-scale quantification of the interactions driving condensate formation of intrinsically disordered proteins
BibTeX
@article{weyer2026crosslinking,
title = {{Cross-linking mass spectrometry guided structure prediction of disordered Encephalitozoon hellem proteins}},
author = {Elizabeth Weyer and Yongliang Wang and Tadakimi Tomita and Carlos Madrid-Aliste and Prithviraj Nandigrami and Andras Fiser and Simone Sidoli and Jennifer T. Aguilan and Bing Han and Louis M. Weiss},
journal = {mBio},
year = {2026},
month = oct,
doi = {10.1128/mbio.02261-26},
url = {https://journals.asm.org/doi/10.1128/mbio.02261-26},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Cross-linking mass spectrometry guided structure prediction of disordered Encephalitozoon hellem proteins
AU - Elizabeth Weyer
AU - Yongliang Wang
AU - Tadakimi Tomita
AU - Carlos Madrid-Aliste
AU - Prithviraj Nandigrami
AU - Andras Fiser
AU - Simone Sidoli
AU - Jennifer T. Aguilan
AU - Bing Han
AU - Louis M. Weiss
PY - 2026
DA - 2026/10/20
JO - mBio
DO - 10.1128/mbio.02261-26
UR - https://journals.asm.org/doi/10.1128/mbio.02261-26
AB - Microsporidia such as Encephalitozoon hellem are obligate intracellular human parasites that remain genetically intractable, limiting the functional characterization of their proteomes. Experimental structural studies are challenging, as many proteins in microsporidia comprise a large percentage of disordered regions. Computational structural studies based on homology-based modeling and the use of deep-learning algorithms for microsporidian proteins also remain limited because most have little to no sequence similarity to proteins with solved structures. To address these limitations, we developed an approach that incorporates cross-linking mass spectrometry (XL-MS) data into structure prediction. XL-MS data provide upper-bound distance constraints that can be incorporated into protein deep-learning-based modeling and subsequent docking. Using this approach, we generated a model of two interacting E. hellem spore wall proteins, spore wall protein 1B (Swp1b) and endospore protein 1 (EnP1), which have no clear homologs outside of microsporidia and contain several disordered regions. These proteins are abundant spore wall proteins of microsporidia and previously were not known to interact with one another. The resulting model not only is consistent with the experimental cross-links used to generate the model but was also subsequently confirmed by independently generated XL-MS data. The described AlphaLink-Modeller framework for structure prediction is particularly well suited to proteins with limited homology and/or substantial flexible regions, given that they adopt a defined structural state within a biological context, thereby extending integrative modeling approaches to previously inaccessible targets. IMPORTANCE: In this article, we demonstrate an improved method for the prediction of the structure of protein complexes with no clear homologs and a considerable number of disordered regions. This is significant because these types of proteins are critical to understanding the biology of organisms, yet model generation of these classes has been inaccessible. The method incorporates experimental data obtained from cross-linking mass spectrometry (XL-MS) into the modeling process, providing a method to integrate such experimental data into computational modeling of the structure of proteins and protein interactions. For this paper, we have used this method for predicting the structure of two highly abundant Encephalitozoon hellem proteins in the spore wall that are important for host cell infection due to this pathogen of humans and other vertebrates.
SN - 2150-7511
ER -