Table 2.
Comparison between different methods to analyse the dynamical behaviour of proteins and their inter-residue communication
| COMMA | Bio3D [11] | GSATools [16] | PyInteraph [10] | PSN-ENM [20] | Taylor et al. [17] | |
|---|---|---|---|---|---|---|
| Software availability | ✓ | ✓ | ✓ | ✓ | - | - |
| Open source | ✓ | ✓ | ✓ | ✓ | - | - |
| Dependencies | MDtraj, Eigen and Numpy python packages | R, Muscle | GNU, Scientific Library, GROMACS | Python, Pymol | - | - |
| Programming language | C++, Python | R | C | Python | - | - |
| Input trajectory formats | AMBER, GROMACS, NAMD, CHARMM... | GROMACS (.dcd) | GROMACS | AMBER, GROMACS, NAMD, CHARMM... | - | - |
| Dynamical properties: | ||||||
| non-covalent interactions | ✓ | - | - | ✓ | ✓ | ✓ |
| inter-residue distances | ✓ | - | - | - | - | ✓ |
| secondary structures | ✓ | - | - | - | - | - |
| dynamical correlations | ✓(PCA, LFA, CP) | ✓(ENM-NMA, PCA) | ✓(between frames) | - | ✓(ENM-NMA) | ✓(MI) |
| Description levels: | ||||||
| residue | ✓ | - | ✓ | ✓ | ✓ | ✓ |
| secondary structure | ✓ | - | - | - | - | - |
| region/domain | ✓ | ✓ | ✓ | - | - | ✓ |
| protein | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Outputs: | ||||||
| protein network | ✓ | - | ✓ | ✓ | ✓ | ✓ |
| communicating regions | ✓(pathway- and clique-based blocks) | ✓(dynamic domain, correlation network) | ✓(functional fragments) | - | - | ✓(communities) |
| communicating segment pairs | ✓ | - | - | - | - | - |
| functional domains | - | - | ✓ | - | - | ✓ |
| pathways | ✓ | - | ✓ | ✓ | ✓ | ✓ |