CRISPR-based antimicrobials; Drug design; In silico docking; RNA secondary structure; RNA tertiary structure; RNA–protein interaction; Structural biology; Anti-Bacterial Agents; RNA, Bacterial; Ribonucleases; Bacteria/genetics; Humans; Ribonucleases/genetics; Ribonucleases/metabolism; CRISPR-Cas Systems; Bacteria; Immunology; Ecology, Evolution, Behavior and Systematics; Modeling and Simulation; Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all); Applied Mathematics; General Agricultural and Biological Sciences; General Biochemistry, Genetics and Molecular Biology
Abstract :
[en] RNA-protein interactions are crucial for diverse biological processes. In prokaryotes, RNA-protein interactions enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements through crRNA-mediated nuclease activities of Cas proteins. Thanks to the programmability and specificity of CRISPR-Cas systems, CRISPR-based antimicrobials have the potential to be repurposed as new types of antibiotics. Unlike traditional antibiotics, these CRISPR-based antimicrobials can be designed to target specific bacteria and minimize detrimental effects on the human microbiome during antibacterial therapy. In this study, we explore the potential of CRISPR-based antimicrobials by optimizing the RNA-protein interactions of crRNAs and Cas13 proteins. CRISPR-Cas13 systems are unique as they degrade specific foreign RNAs using the crRNA template, which leads to non-specific RNase activities and cell cycle arrest. We show that a high proportion of the Cas13 systems have no colocalized CRISPR arrays, and the lack of direct association between crRNAs and Cas proteins may result in suboptimal RNA-protein interactions in the current tools. Here, we investigate the RNA-protein interactions of the Cas13-based systems by curating the validation dataset of Cas13 protein and CRISPR repeat pairs that are experimentally validated to interact, and the candidate dataset of CRISPR repeats that reside on the same genome as the currently known Cas13 proteins. To find optimal CRISPR-Cas13 interactions, we first validate the 3-D structure prediction of crRNAs based on their experimental structures. Next, we test a number of RNA-protein interaction programs to optimize the in silico docking of crRNAs with the Cas13 proteins. From this optimized pipeline, we find a number of candidate crRNAs that have comparable or better in silico docking with the Cas13 proteins of the current tools. This study fully automatizes the in silico optimization of RNA-protein interactions as an efficient preliminary step for designing effective CRISPR-Cas13-based antimicrobials.
Disciplines :
Mathematics Biotechnology Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Park, Ho-Min ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea ; Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
Park, Yunseol ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea
Berani, Urta; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea
Bang, Eunkyu; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea
Vankerschaver, Joris ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
Van Messem, Arnout ; Université de Liège - ULiège > Département de mathématique > Statistique appliquée aux sciences
De Neve, Wesley ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea ; Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
Shim, Hyunjin ; Center for Biosystems and Biotech Data Science, Ghent University Global Campus, Incheon, South Korea. hyunjin.shim@ghent.ac.kr
Language :
English
Title :
In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials.
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