PyPCN: protein contact networks in PyMOL.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
received:
27
04
2023
revised:
25
09
2023
accepted:
03
11
2023
medline:
14
11
2023
pubmed:
9
11
2023
entrez:
9
11
2023
Statut:
ppublish
Résumé
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. 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. https://github.com/pcnproject/PyPCN.
Identifiants
pubmed: 37941462
pii: 7371299
doi: 10.1093/bioinformatics/btad675
pmc: PMC10641099
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
Références
J Chem Inf Model. 2018 Jul 23;58(7):1325-1330
pubmed: 29956925
Nucleic Acids Res. 2016 Jul 8;44(W1):W375-82
pubmed: 27151201
Sci Rep. 2023 Feb 17;13(1):2837
pubmed: 36808182
J R Soc Interface. 2021 Jan;18(174):20200591
pubmed: 33402024
Biophys Chem. 2012 May;165-166:21-9
pubmed: 22464849
Nucleic Acids Res. 2000 Jan 1;28(1):235-42
pubmed: 10592235
J Chem Inf Model. 2014 Jan 27;54(1):159-68
pubmed: 24289204
Biomed Res Int. 2017;2017:2483264
pubmed: 28243596
PLoS Comput Biol. 2022 Jan 24;18(1):e1009818
pubmed: 35073311
Chem Rev. 2013 Mar 13;113(3):1598-613
pubmed: 23186336
Entropy (Basel). 2022 Jul 19;24(7):
pubmed: 35885221
Nucleic Acids Res. 2023 Jan 6;51(D1):D488-D508
pubmed: 36420884
Curr Opin Struct Biol. 2015 Apr;31:43-8
pubmed: 25796032
Bioinformatics. 2022 Sep 2;38(17):4235-4237
pubmed: 35799364
Biophys J. 2005 Dec;89(6):4159-70
pubmed: 16150969
Nat Protoc. 2012 Mar 15;7(4):670-85
pubmed: 22422314
Trends Biochem Sci. 2011 Apr;36(4):179-82
pubmed: 21345680
Nucleic Acids Res. 2020 Jul 2;48(W1):W94-W103
pubmed: 32427333
Front Bioinform. 2021 Jun 18;1:684970
pubmed: 36303777
Infect Genet Evol. 2021 Sep;93:104921
pubmed: 34004362
Nature. 2021 Aug;596(7873):583-589
pubmed: 34265844
Curr Protein Pept Sci. 2008 Feb;9(1):28-38
pubmed: 18336321
PLoS Comput Biol. 2016 Jun 02;12(6):e1004966
pubmed: 27253437
Bioinformatics. 2015 Mar 1;31(5):779-81
pubmed: 25355786