Motivation: Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations. Results: PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs. Availability and implementation: https://github.com/pcnproject/PyPCN.

PyPCN: protein contact networks in PyMOL / Rosignoli, Serena; DI PAOLA, Luisa; Paiardini, Alessandro. - In: BIOINFORMATICS. - ISSN 1367-4811. - 39:11(2023). [10.1093/bioinformatics/btad675]

PyPCN: protein contact networks in PyMOL

Serena Rosignoli
Primo
;
Luisa di Paola
Secondo
;
Alessandro Paiardini
Ultimo
2023

Abstract

Motivation: Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations. Results: PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs. Availability and implementation: https://github.com/pcnproject/PyPCN.
2023
PyMOL, protein contact networks, protein structure
01 Pubblicazione su rivista::01a Articolo in rivista
PyPCN: protein contact networks in PyMOL / Rosignoli, Serena; DI PAOLA, Luisa; Paiardini, Alessandro. - In: BIOINFORMATICS. - ISSN 1367-4811. - 39:11(2023). [10.1093/bioinformatics/btad675]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1691811
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