Resources :: GRAMM v1.03

Global Range Molecular Matching

Protein-Protein Docking and Protein-Ligand Docking

Installation instructions


GRAMM is a program for protein docking. To predict the structure of a complex, it requires only the atomic coordinates of the two molecules (no information about the binding sites is needed). The program performs an exhaustive 6-dimensional search through the relative translations and rotations of the molecules. The molecular pairs may be: two proteins, a protein and a smaller compound, two transmembrane helices, etc. GRAMM may be used for high-resolution molecules, for inaccurate structures (where only the gross structural features are known), in cases of large conformational changes, etc.

The Global RAnge Molecular Matching (GRAMM) methodology is an empirical approach to smoothing the intermolecular energy function by changing the range of the atom-atom potentials. The technique locates the area of the global minimum of intermolecular energy for structures of different accuracy. The quality of the prediction depends on the accuracy of the structures. Thus, the docking of high-resolution structures with small conformational changes yields an accurate prediction, while the docking of ultralow-resolution structures will give only the gross features of the complex. More information about the GRAMM methodology is on our laboratory research page.


GRAMM was made publicly available following a number of requests from different labs. We would like to make it clear, however, that both the methodology and the program, at present, are in the process of active development, and have to be viewed like that. The program is free. However, we would expect proper references. Bug reports will be also appreciated.


GRAMM is compiled on SGI R10000, SGI R4000, SGI R4400, SGI R8000, Sun SPARC, IBM RS6000, DECAlpha, and PC (Windows and Linux). Windows version must work on all 32-bit flavors of the MS Windows operating system. Linux version was compiled on RedHat with glibc2.0.

Basic papers on GRAMM methodology