Clustering with CLoNe

CLoNe was developed mostly for use with structural ensembles, such as those obtained from Molecular Dynamics simulations or integrative modeling attempts. Written in Python3.7, it can be used as any other clustering tool from the Scikit-learn package. When applied to structural ensembles, it will output helpful scripts for automatic loading of the results in the molecular visualization software VMD.

CLoNe is a clustering algorithm with highly general applicability. Based on the Density Peaks algorithm from Rodriguez and Laio (Science, 2014), it improves on it by requiring a single parameter, ‘pdc’, that is intuitive and easy to use. ‘pdc’ can be incremeted if there are too many clusters, and decremented if there are not enough. Integer values between 1 and 10 are usually enough, with many values leading to the same results in most cases.

CLoNe first performs a Nearest Neighbour step to derive the local densities of every data point. Putative cluster centers are then identified as local density maxima. Then, CLoNe takes advantage of the Bhattacaryaa coefficient to merge clusters if needed and relies on a Bayes classifier to effectively remove outliers.

See the GitHub repository below for latest development.

Lipid Builder

LipidBuilder is a webserver which allows to create lipid bilayers models with atomic resolution, suitable to be used as input for molecular dynamics simulations

LipidBuilder generates automatically the topology and template of a given phospholipid. First, the lipid’s topology is created by combining the selected phospholipid head groups, extracted from a built-in library of structures and the provided hydrocarbon chains. Since the hydrocarbon chains have been parameterized based on a “plug and play” philosophy in the CHARMM force field, the acyl chains are generated by linking a series of alkanes. Three different classes of hydrocarbons have been defined: the saturated, the unsaturated and the cyclopropane. Finally, the psfgen algorithm and the generated topology file are combined to build the template PDB and PSF files of the phospholipid.


power: a parallel optimization workbench to enhance resolution in biological systems

power is an open source optimization framework designed for dynamic integrative modeling of biological systems. In its current version is based on particle swarm optimization and can approach the prediction of molecular assemblies based on a limited set of experimental spatial restraints.

power has been concieved to be modular, thus that the creation of specific modules is easy even for a user unaware of its internal architecture.

The current version supports function minimization, experiment-driven prediction of protein hetero-multimers (see pipeline on the right), and parameterization of multiscale models for molecular simulations.


  • power : a parallel optmization workbench to enhance resolution in biological systems, in preparation.


last update: 04.07.2013


Interactive plot with CSP data from Biophys J 2016 is available at this link: