GRAMMCell
Docking-based Cell Modeling


The web interface for the docking-based approach simulating cell crowded environment by sampling the intermolecular energy landscape generated by GRAMM (Global RAnge Molecular Matching). GRAMM systematically maps the intermolecular energy landscape by a spectrum of docking poses corresponding to stable (deep energy minima) and transient (shallow minima) protein interactions. The sampling of these energy landscapes of a large system of proteins is performed in GRAMMCell using Markov Chain Monte Carlo protocol.



Conditions of Use

This computationally intensive service is provided to the research community free of charge utilizing our computational resources, which are limited.

To ensure the fair use of this server, we ask that no person submits more than ten simulation requests per day. We reserve the right to block access from those not honoring this request or involved in any other misuse of this service.

The simulation requests will be queued and the wait time will vary depending on the load of our cluster.

Disclaimer

This is research software under active development, and we make no guarantees regarding the accuracy of its predictions. The results of simulations are provided AS IS, and should be used at your own risk. We do not warrant or assume any liability or responsibility for the accuracy, completeness, or usefulness of any information provided on this server.

Questions

Send questions or comments to grammcell@ku.edu.

References

GRAMMCell web-server should be cited as:

Singh, A., et al., GRAMMCell: Docking-based cell modeling resource, J. Mol. Biol., 2025; DOI:10.1016/j.jmb.2025.169085

Other GRAMMCell references:

Tytarenko, A.M., et al., Highly Optimized Simulation of Atomic Resolution Cell-like Protein Environment, J. Phys. Chem. B, 2025; DOI: 10.1021/acs.jpcb.4c07769

Singh A., Kundrotas P.J., Vakser I.A., Diffusion of proteins in crowded solutions studied by docking-based modeling. J. Chem. Phys. 2024; 161 (9): 095101.

Vakser, I.A. et al., Docking-based long timescale simulation of cell-size protein systems at atomic resolution. Proc. Natl. Acad. Sci. USA, 2022, 119: e2210249119

GRAMMCell resource is being developed in collaboration with Eugene Feinberg, Stony Brook University and Pavlo Kasyanov & Andrii Tytarenko, Institute for Applied System Analysis at the Igor Sikorsky Kyiv Polytechnic Institute.