# WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins
# CONCHA: Contact-based characterization of conformational ensembles of highly flexible proteins
Welcome to WASCO, an IDP ensemble comparison tool. The method implemented in this jupyter notebook computes residue-specific distances between a pair of IDP conformational ensembles, together with an overall distance for the entire ensemble. The comparison is simultaneously made at two scales:
Welcome to CONCHA, an ensemble characterization tool. The method implemented in this jupyter notebook computes residue-specific distances between a pair of IDP conformational ensembles, together with an overall distance for the entire ensemble. The comparison is simultaneously made at two scales:
***Global scale**: distances between the distributions of the relative positions of all residue pairs in both ensembles. For each pair of residues, we compute the (2-Wasserstein) distance between a pair of probability distributions supported on the three-dimensional euclidean space (point clouds).
***Local scale**: distances between the (phi, psi) angle distributions of each ensemble, for each residue along the sequence. For each residue, we compute the (2-Wasserstein) distance between a pair of probability distributions supported on the two dimensional flat torus.
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@@ -24,11 +25,11 @@ The result of the comparison analysis is represented through a matrix, denoted $
- [8] The entries $`\mathcal{W}_{ii}`$ may be marked with a star if their associated $p$-value is less than the significance level $`\alpha=0.05`$.
- [9] The axes labels correspond to the residue position, counting from the N-terminal, relative to the sequence segment that is being compared (and not to the absolute position in the entire sequence).
#### Running WASCO
#### Running CONCHA
To apply the comparison tool for a given pair of IDP ensembles, the user can directly execute the [comparison_tool](https://gitlab.laas.fr/moma/WASCO/-/blob/master/wasco/comparison_tool.ipynb) file, which contains its specific instructions and guidelines. This file calls all the other notebooks included in the same folder, which can also be used individually if desired.
To run CONCHA to characterize an ensemble, the user can directly execute the [contact_clustering](contact_clustering.ipynb) notebook, which contains the detailed pipeline and allows a step-by-step implementation of the tool.
## Installing WASCO
## Installing CONCHA
CONCHA and its required dependencies can be automatically installed if Python >=3.8 is available. We recommend to perform the installation inside a [Python virtual environment](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/). It can be created as follows
