ArticleBioinformatics (Oxford, England)2023
PyPCN: protein contact networks in PyMOL.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- In silico study of cytochrome-C binding to a cardiolipin-containing membrane.European biophysics journal : EBJ · 2026Article
- Integrated WGCNA of lncRNA-mRNA Networks Identifies Novel Hub Genes and Potential Therapeutic Agents for Liver Cirrhosis via Molecular Docking Validation.International journal of molecular sciences · 2026Article
- Elucidating the molecular mechanisms of paeoniflorin intervention in oral lichen planus: a computational biology and bioinformatics-based research strategy.Frontiers in bioinformatics · 2026Article
- Chemophysical Characterization of Carotenoproteins From Crustacean Shell Waste for Pharmaceutical Applications.International journal of food science · 2026Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationProtein 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.
resultsPyPCN 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.