Forcefield_PTM: AMBER Forcefield Parameters for Post-translational Modifications

Forcefield_PTM is a set of self-consistent AMBER forcefield parameters for frequently occurring post-translational modifications (PTMs).Partial charges reproduce an ab initio electrostatic potential surface and key torsions were re-fit to reproduce the ab initio rotational profile.

Instructions to Download and Use Forcefield_PTM in AMBER

Download and read the instructions for use.

Download and unzip FF_PTM.

Go to the Multiple Modification Submission Page

Submit Protein Structure to Be Post-Translationally Modified

Currently can modify positions 2 through N-1


If using the parameters or this web server, please cite the associated publications, which describe the details of our method.

Khoury, G.A., Thompson, J.P., Smadbeck, J., Kieslich, C.A., and Floudas, C. A. Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications. Journal of Chemical Theory and Computation 2013, 9 (12) 5653-5674. DOI: 10.1021/ct400556v

Khoury, G.A., Smadbeck, J., Tamamis, P., Vandris, A.C., Kieslich, C.A., and Floudas, C.A. Forcefield_NCAA: Ab Initio Charge Parameters to Aid in the Discovery and Design of Therapeutic Proteins and Peptides with Unnatural Amino Acids and Their Application to Complement Inhibitors of the Compstatin Family. ACS Synthetic Biology 2014 DOI: 10.1021/sb400168u

This webserver uses the AMBER11 package.

Related Work

For charge parameters of 147 non-canonical amino acids and a corresponding web interface to introduce them into PDB structures, please visit Forcefield_NCAA.

Additional PTM Resources

If you are interested in parameters for performing simulations/calculations in GROMACS, please see the Vienna-PTM resource developed by Zagrovic and coworkers.

If you are interested in continuously updated statistics on the frequency of different post-translational modifications, see our PTM Curation resource.


CAF acknowledges support from The National Institutes of Health grant number R01GM052032, and the National Science Foundation. GAK is grateful for support by a National Science Foundation Graduate Research Fellowship under grant number DGE-1148900. This work used the Extreme Science and Engineering Discovery Environment (XSEDE) under allocation TG-MCB110039 to perform simulations on Kraken, which is supported by National Science Foundation grant number OCI-1053575.