distogram_MuStd

Python script to compute and visualize residue pairwise mean distance and standard deviation of a distogram

General description

Since distograms are multidimensional arrays, we can only visualize them in some kind of condensed form- for instance, by extracting the features/moments of distrograms. Already within the repository distogram this issue was tackled by computing the mean, standard deviation and skewness of distogram, which are after normalization stacked into a R,G,B array (0-1 float), in order to create a RGB image. However, such representation is difficult to interpret, because all three features of inter-residue distance distribution are combined into one color code. Furthermore, we might be more interested to be able to separately inspect moments of distribution. Script moments_of_distogram.py calculates mean distance and standard deviation of all pairwise inter-residue distance distributions, and plots them separately on upper and lower triangle of the residue pairwise matrix. Such reduced representation of a distogram is particularly convenient for the comparison of two distograms.

Illustration is adapted from the article by Dittrich et al, 2023.¹

Input file requirements

As input files are required:

• Counts (i.e. distance bin occupancies) and distance bins as two numpy objects with extension .npy. For details on how distogram is computed, reader is referred to repository distogram. Exemplary input files are provided in the example_data directory of this repository.

Dependencies

moments_of_distogram.py is a python script built on Python 3.8.8. Script was tested with provided exemplary data under the following configuration:

• Windows 10
• Python 3.8.8
• numpy 1.23.0
• matplotlib 3.7.1

References

1. Dittrich, J.; Popara, M.; Kubiak, J.; Dimura, M.; Schepers, B.; Verma, N.; Schmitz, B.; Dollinger, . P.; Kovacic, F.; Jaeger, K. E.; Seidel, C. A. M.; Peulen, T. O.; Gohlke, H., Resolution of Maximum Entropy Method-Derived Posterior Conformational Ensembles of a Flexible System Probed by FRET and Molecular Dynamics Simulations. J Chem Theory Comput 2023, 19 (8), 2389-2409.

Authors

• Milana Popara
• Thomas-Otavio Peulen