AF ‐ CALVADOS : AlphaFold ‐guided simulations of multi‐domain proteins at the proteome level
- 1Structural Biology and NMR Laboratory, Linderstrøm‐Lang Centre for Protein Science, Department of Biology University of Copenhagen Copenhagen Denmark
Abstract
Deep-learning methods have transformed our ability to predict the three-dimensional structures of folded proteins from sequence, and coarse-grained simulations have made it possible to study intrinsically disordered proteins at the proteome scale. More than half of human proteins, however, contain mixtures of disordered regions and one or more folded domains, and the biological function of these multi-domain proteins (MDPs) depends on the interplay between the folded and disordered regions. Here, we developed AF-CALVADOS, a coarse-grained simulation model that is informed by AlphaFold to model the dynamics of intrinsically disordered proteins and MDPs containing mixtures of folded and disordered regions. AF-CALVADOS leverages information from AlphaFold 2 to model folded regions that we then integrate with the coarse-grained CALVADOS model. Our automated framework makes it possible to perform simulations of any soluble folded or disordered protein without manually defining the folded regions, enabling scaling to the proteome level. We validate AF-CALVADOS using experimental small-angle x-ray scattering data for more than 400 proteins and find that it performs well across proteins with varying amounts of ordered and disordered regions. We demonstrate the scalability of AF-CALVADOS by performing simulations of 12,483 intracellular human proteins and make the data freely available; we envisage that large-scale simulation data generated by AF-CALVADOS can be used to benchmark or train machine learning models for flexible, MDPs. The conformational ensembles can also be used to study sequence-dynamics-function relationships at scale, and can shed light on the interplay between folded and disordered regions. We illustrate this by analyzing the disordered regions in 1487 human transcription factors. AF-CALVADOS is available as part of the CALVADOS package at: github.com/KULL-Centre/CALVADOS. Single-chain simulations with AF-CALVADOS can be run via Google Colab: colab.research.google.com/github/KULL-Centre/_2025_buelow_AF-CALVADOS/blob/main/AF_CALVADOS.ipynb.
Related
- CEM 2.0: Co-evolution-guided mining of superior extremophilic proteins via a curated proteome database and protein language model.
- Predictive all-atom simulations of disordered proteins and biomolecular condensates through osmometry-guided force-field optimization
- When can AlphaFold predict the oligomeric states of proteins?
- Non-standard proteins in the lens of AlphaFold 3: a case study of amyloids
- VAEBAC: a representation-learning framework for proteome-scale prediction of amyloidogenic proteins and functional stratification of nucleic-acid-binding proteins.
BibTeX
@article{bulow2026calvados,
title = {{AF ‐ CALVADOS : AlphaFold ‐guided simulations of multi‐domain proteins at the proteome level}},
author = {Sören von Bülow and Kristoffer E. Johansson and Kresten Lindorff‐Larsen},
journal = {Protein Science},
year = {2026},
month = jul,
doi = {10.1002/pro.70694},
url = {https://doi.org/10.1002/pro.70694},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - AF ‐ CALVADOS : AlphaFold ‐guided simulations of multi‐domain proteins at the proteome level
AU - Sören von Bülow
AU - Kristoffer E. Johansson
AU - Kresten Lindorff‐Larsen
PY - 2026
DA - 2026/07/10
JO - Protein Science
DO - 10.1002/pro.70694
UR - https://doi.org/10.1002/pro.70694
AB - Deep-learning methods have transformed our ability to predict the three-dimensional structures of folded proteins from sequence, and coarse-grained simulations have made it possible to study intrinsically disordered proteins at the proteome scale. More than half of human proteins, however, contain mixtures of disordered regions and one or more folded domains, and the biological function of these multi-domain proteins (MDPs) depends on the interplay between the folded and disordered regions. Here, we developed AF-CALVADOS, a coarse-grained simulation model that is informed by AlphaFold to model the dynamics of intrinsically disordered proteins and MDPs containing mixtures of folded and disordered regions. AF-CALVADOS leverages information from AlphaFold 2 to model folded regions that we then integrate with the coarse-grained CALVADOS model. Our automated framework makes it possible to perform simulations of any soluble folded or disordered protein without manually defining the folded regions, enabling scaling to the proteome level. We validate AF-CALVADOS using experimental small-angle x-ray scattering data for more than 400 proteins and find that it performs well across proteins with varying amounts of ordered and disordered regions. We demonstrate the scalability of AF-CALVADOS by performing simulations of 12,483 intracellular human proteins and make the data freely available; we envisage that large-scale simulation data generated by AF-CALVADOS can be used to benchmark or train machine learning models for flexible, MDPs. The conformational ensembles can also be used to study sequence-dynamics-function relationships at scale, and can shed light on the interplay between folded and disordered regions. We illustrate this by analyzing the disordered regions in 1487 human transcription factors. AF-CALVADOS is available as part of the CALVADOS package at: github.com/KULL-Centre/CALVADOS. Single-chain simulations with AF-CALVADOS can be run via Google Colab: colab.research.google.com/github/KULL-Centre/_2025_buelow_AF-CALVADOS/blob/main/AF_CALVADOS.ipynb.
SN - 0961-8368
SN - 1469-896X
ER -