During the Molecular Dynamics Simulations, molecule conformations changes considerably and identifying the conformations is very important to study the biomolecular dynamics. Conformational clustering can be performed to identify different conformations sampled during the simulations.

Most widely approach for conformational clustering is to calculate Root Mean Square Deviations between all conformations and cluster them according to these deviations. However, for large MD trajectories, this RMSD matrix could be huge and takes very long time to calculate. Therefore, an alternative method such as features based clustering can be used to identify the cluster of conformations.

gmx_clusterByFeatures can be used to cluster the conformations of a molecule in a molecular dynamics trajectory using collection of features. The features could be any quantity as a function of time such as Projections of egienvector from PCA or dihedral-PCA, distances, angles, channel radius etc.


It is developed for GROMACS MD trajectory. However, it can be used with any other trajectory format after converting it to GROMACS format trajectory.

When Projections of egienvector from PCA or dihedral-PCA is used as features, it yields clusters depending on the largest conformational changes during the simulations. Depending on the Clustering metrics, a cluster may contain small conformational fluctuations around the respective central structure.

When other features such as distances, angles, channel radius etc are used as the features, the obtained clusters of conformations depends on these features. It can be used to study the specific conformations given the features while ignoring all other conformational fluctuations.

Clustering metrics

To determine the number of clustering, following metrics are implemented: