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CABS-dock standalone: a toolbox for flexible protein-peptide docking. | LitMetric

AI Article Synopsis

  • CABS-dock standalone is a Python package designed for protein-peptide docking that allows for flexible backbone movement, enabling more realistic simulations of molecular interactions.
  • It features customizable flexibility settings and gives users complete control over the docking process from setup to results analysis, improving upon the original web server version.
  • The software is available for free under the MIT license and supports various operating systems, with resources like source code and documentation accessible through its repository on Bitbucket.

Article Abstract

Summary: CABS-dock standalone is a multiplatform Python package for protein-peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein-peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation-it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.

Availability And Implementation: CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792116PMC
http://dx.doi.org/10.1093/bioinformatics/btz185DOI Listing

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