A knowledge based coarse grained forcefield (CGMM) has been developed with the aim to reproduce realistic dynamics of protein and use this information in different research problems. This forcefield is based on protein model described by pseudo atoms corresponding to 20 naturally occurring amino acids located at the Cα atoms positions. The proposed potential is based on statistical distribution of properties like bonded and nondonded distance, virtual angle and dihedral. Statistical distributions are calculated from known unbiased protein database. The forcefield potential functions are parameterized against the unbiased database by Boltzmann inversion approach and modeled by regression method.

Potential energy function:
U ^total = U ^virtual_bond + U ^{virtual_angle} + U ^{virtual_dihedral} + U ^{virtual_nonbonded}

CGMM can produce simulation results that match closely (overall correlation-coefficient: 0.81) with the root-mean-square fluctuation (RMSF) graphs calculated from the 315 unique NMR structures having ensembles of 10 models or greater.

The proposed forcefield is useful to do long time scale protein dynamics on various kinds of proteins, including metalloproteins. For metalloproteins, please use the Metal-CGMM potential functions. The application to multimer and multidomain proteins is slightly different from monomeric proteins and appropriate utilities have to be used. Please check the Tutorial.