SPECTRUS Web Server

The SPECTRUS web server (Spectral-based Rigid Unit Subdivision) performs a decomposition into quasi-rigid domains of proteins or protein complexes, based on the analysis of the distance fluctuations between pairs of amino acids. The server processes entries of up to 2000 amino acids and trajectory files of up to 50MB. If your data exceed this limit, please consider to run SPECTRUS locally on your machine by installing the source code, available here.

Ponzoni L, Polles G, Carnevale V, Micheletti C, "SPECTRUS: a dimensionality reduction approach for identifying dynamical domains in protein complexes from limited structural datasets", Structure, 2015.

SPECTRUS-evo Web Server

The SPECTRUS-evo web server (Spectral-based Rigid Unit Subdivision) performs a decomposition of a protein sequence into evolutionarily-related domains, based on the analysis of coevolutionary couplings between pairs of amino acid positions in a multiple sequence alignment.

Granata D, Ponzoni L, Micheletti C, Carnevale V, "Patterns of coevolving amino acids unveil structural and dynamical domains", PNAS, 2017.

Intrinsic Dimension Estimator

The collective behavior of a large number of degrees of freedom can be often described by a handful of variables. This observation justifies the use of dimensionality reduction approaches to model complex systems and motivates the search for a small set of relevant “collective” variables. We ahve analyzed this issue by focusing on the optimal number of variable needed to capture the salient features of a generic dataset and developed a novel estimator for the intrinsic dimension (ID). By approximating geodesics with minimum distance paths on a graph, we have analyzed the distribution of pairwise distances around the maximum and exploited its dependency on the dimensionality to obtain an ID estimate. The resulting estimator does not depend on the shape of the intrinsic manifold and is highly accurate, even for exceedingly small sample sizes.

Python code for intrinsic dimension estimation of generic datasets, from the paper:
D. Granata and V. Carnevale. Accurate Estimation of the Intrinsic Dimension Using Graph Distances: Unraveling the Geometric Complexity of Datasets, Sci Rep. 2016; 6: 31377.