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Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins

BACKGROUND: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization of a protein as a RIN provides insights on the topological properties (and hence...

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Detalles Bibliográficos
Autores principales: Spanò, Alvise, Fanton, Lorenzo, Pizzolato, Davide, Moi, Jacopo, Vinci, Francesco, Pesce, Alberto, Dongmo Foumthuim, Cedrix J., Giacometti, Achille, Simeoni, Marta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496328/
https://www.ncbi.nlm.nih.gov/pubmed/37697267
http://dx.doi.org/10.1186/s12859-023-05466-y
Descripción
Sumario:BACKGROUND: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. RESULTS: We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. CONCLUSIONS: RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05466-y.