Martini 3 Coarse-Grained Models for Steroid Hormones
5 affiliations
- 1Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, Oeiras, Portugal, 2780-157
- 2Programa de Pós-Graduação em Bioquímica, Departamento de Bioquímica
- 3Universidade Federal de Santa Catarina
- 4Department of Chemical and Pharmaceutical Engineering Faculty of Chemistry and Pharmacy
- 5University of Sofia
Abstract
Steroid hormones are central to the development, metabolism, homeostasis and reproduction of animals and have been the subject of many evolutionary, ecotoxicological and human health studies. Coarse-grain molecular dynamics (CG MD) simulations are a powerful tool to study the structural and dynamic behavior of steroid hormones and their interactions. Here, we expand the capabilities of the widely used Martini 3 CG framework by parameterizing and validating a set of 20 steroid hormone models — including both endogenous and synthetic molecules — to enable their study across diverse targets, systems, and fields of interest. Hormone parameterization followed Martini 3 guidelines, while leveraging the similarities with the parent cholesterol molecule, for which a Martini 3 model is available. Notably, we were able to base our parameterization on the hundreds of experimental structures in the protein data bank that contain steroid hormones, instead of relying on atomistic simulations as is typical with Martini. The parameterized models match to within 5% target molecular volumes, and recover the overall hydrophobicity trend of the hormones, although higher hydrophobicities tended to be overestimated. In free simulations of protein-hormone systems the experimentally known binding poses could be recovered for 89% of the cases, indicating a successful parameterization also from a functional standpoint. Since several Martini 3 protein models were tested, we also incidentally show that the flexibility of the OLIVES and GōMartini models makes them best suited for this type of protein–ligand recognition. Finally, models were validated in interaction with membranes by comparison to reference atomistic data. We expect that the availability of our fully validated Martini 3 CG steroid hormone models will contribute to new studies, collaborations, and applications across different scientific fields.
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BibTeX
@misc{madaloz2026martini,
title = {{Martini 3 Coarse-Grained Models for Steroid Hormones}},
author = {Tâmela Zamboni Madaloz and Karin dos Santos and Fatmegyul Mustan and Fernando Neiva Nunes and Guilherme Razzera and Manuel N. Melo},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15002998/v2},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15002998/v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Martini 3 Coarse-Grained Models for Steroid Hormones
AU - Tâmela Zamboni Madaloz
AU - Karin dos Santos
AU - Fatmegyul Mustan
AU - Fernando Neiva Nunes
AU - Guilherme Razzera
AU - Manuel N. Melo
PY - 2026
DA - 2026/10/19
PB - ChemRxiv
DO - 10.26434/chemrxiv.15002998/v2
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15002998/v2
AB - Steroid hormones are central to the development, metabolism, homeostasis and reproduction of animals and have been the subject of many evolutionary, ecotoxicological and human health studies. Coarse-grain molecular dynamics (CG MD) simulations are a powerful tool to study the structural and dynamic behavior of steroid hormones and their interactions. Here, we expand the capabilities of the widely used Martini 3 CG framework by parameterizing and validating a set of 20 steroid hormone models — including both endogenous and synthetic molecules — to enable their study across diverse targets, systems, and fields of interest. Hormone parameterization followed Martini 3 guidelines, while leveraging the similarities with the parent cholesterol molecule, for which a Martini 3 model is available. Notably, we were able to base our parameterization on the hundreds of experimental structures in the protein data bank that contain steroid hormones, instead of relying on atomistic simulations as is typical with Martini. The parameterized models match to within 5% target molecular volumes, and recover the overall hydrophobicity trend of the hormones, although higher hydrophobicities tended to be overestimated. In free simulations of protein-hormone systems the experimentally known binding poses could be recovered for 89% of the cases, indicating a successful parameterization also from a functional standpoint. Since several Martini 3 protein models were tested, we also incidentally show that the flexibility of the OLIVES and GōMartini models makes them best suited for this type of protein–ligand recognition. Finally, models were validated in interaction with membranes by comparison to reference atomistic data. We expect that the availability of our fully validated Martini 3 CG steroid hormone models will contribute to new studies, collaborations, and applications across different scientific fields.
ER -