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- Title
CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins.
- Authors
Park, Ho-min; Won, Jongbum; Park, Yunseol; Anzaku, Esla Timothy; Vankerschaver, Joris; Van Messem, Arnout; De Neve, Wesley; Shim, Hyunjin
- Abstract
Background: CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. Results: CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. Conclusion: CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA–protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.
- Subjects
MOLECULAR docking; INTERNET servers; CRISPRS; RNA-protein interactions; PROKARYOTIC genomes; PROTEINS
- Publication
BMC Bioinformatics, 2023, Vol 24, Issue 1, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-023-05296-y