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Fig. 3 | BMC Bioinformatics

Fig. 3

From: pyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

Fig. 3

Detection and characterization of ADRP substrate-binding cavity of SARS-CoV-2 and its comparison to related coronaviruses and human macroD1 and macroD2 proteins. a Three different characterizations of the apo ADRP substrate-binding cavity of SARS-CoV-2 (PDB ID: 6WEN) using pyKVFinder. The upper panel shows the detected cavity represented as gray surface and residues surrounding it as red sticks. The cavity area and volume are displayed. The middle panel presents the same cavity colored by depth, while the bottom panel shows the cavity colored by hydropathy using Eisenberg and Weiss scale. b Conservation analysis of the ADP-ribose binding site in ADRP domain of SARS-CoV-2 (PDB ID: 6WEN, chain A), SARS-CoV (PDB ID: 2ACF, chain B), MERS-CoV (PDB ID: 5HIH, chain A), NL63 (PDB ID: 2VRI, chain A), HCoV-229E (PDB ID: 3EJG, chain A), FCoV (PDB ID: 3ETI, chain B) and human macrodomain proteins macroD1 (PDB ID: 2X47, chain A) and macroD2 (PDB ID: 6Y73, chain D) from human. These protein domains were selected using Dali and choosing homologs in apo form. The structures were realigned using MUSTANG algorithm [39] from YASARA program [40]. The figure presents cavity points that were detected in at least two structures and the points are colored by conservation percentage. c Hydropathy profile of the same compared cavities collected from pyKVFinder ndarrays. d Hierarchical clustering dendrogram of the frequency of residues surrounding the compared cavities. The correlation metric was used to assess the similarity and the complete method was chosen as linkage method. All the images and graphics were created inside a Jupyter notebook. To create images of tridimensional structures, we used NGL Viewer tool and to plot graphics, we used matplotlib library

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