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CRISPR and crRNAs

CRISPR and crRNAs

Bacteria and archaea are susceptible to viral infections just like eukaryotes; therefore, they have developed a unique adaptive immune system to protect themselves. Clustered regularly interspaced short palindromic repeats and CRISPR-associated proteins (CRISPR-Cas) are present in more than 45% of known bacteria and 90% of known archaea.
The CRISPR-Cas system stores a copy of foreign DNA in the host genome and uses it to identify the foreign DNA upon reinfection. CRISPR-Cas has three different...

CRISPR

CRISPR

Genome editing technologies allow scientists to modify an organism’s DNA via the addition, removal, or rearrangement of genetic material at specific genomic locations. These types of techniques could potentially be used to cure genetic disorders such as hemophilia and sickle cell anemia. One popular and widely used DNA-editing research tool that could lead to safe and effective cures for genetic disorders is the CRISPR-Cas9 system. CRISPR-Cas9 stands for Clustered Regularly Interspaced...

CRISPR

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The Antiviral System of Bacteria and Archaea: CRISPR

The Antiviral System of Bacteria and Archaea: CRISPR

CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats is a adaptive immune system found in bacteria and archaea that protects against viral infections. This system enables prokaryotic cells to identify, remember, and neutralize foreign genetic elements, primarily bacteriophages, by storing fragments of the invader’s DNA as a genetic memory.The CRISPR immune response begins during an initial infection. Cas (CRISPR-associated) proteins play a central role in this...

CRISPR/Cas9 Genome Editing

CRISPR/Cas9 Genome Editing

The CRISPR-Cas system serves as a bacterial defense mechanism against invading genetic elements such as viruses and plasmids, forming the foundation for its adaptation as a powerful genome-editing tool. Originally discovered in prokaryotes, this system has been repurposed to revolutionize genetic engineering across a wide range of organisms, including plants, animals, and humans. The core component, Cas9, is an endonuclease derived from Streptococcus pyogenes, capable of introducing...

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Related Experiment Video

Updated: May 5, 2026

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Published on: June 6, 2020

EnAcrPred: A robust ensemble machine learning framework for identifying anti-CRISPR proteins.

Qianchen Liu^1,2, Jiahui Guan^2,3, Xuxin He²

¹School of Informatics, Xiamen University, Xiamen, China.

Protein Science : a Publication of the Protein Society

|May 4, 2026

Summary

This summary is machine-generated.

We developed EnAcrPred, an ensemble learning framework to accurately identify anti-CRISPR proteins (Acrs). This tool aids CRISPR-Cas system research and enhances gene editing safety by predicting Acrs from protein sequences.

Keywords:

CRISPR‐Cas systems anti‐CRISPR ensemble learning feature selection machine learning protein sequence analysis

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Area of Science:

Molecular Biology
Bioinformatics
Genetics

Background:

CRISPR-Cas systems are vital for gene editing, and their regulation by anti-CRISPR proteins (Acrs) is key.
Existing experimental methods struggle to identify novel Acrs, especially those with low sequence similarity.

Purpose of the Study:

To develop an accurate and robust computational tool for predicting anti-CRISPR proteins (Acrs).
To improve the identification of Acrs for advancing CRISPR-Cas technology and gene editing safety.

Main Methods:

Ensemble learning framework (EnAcrPred) integrating sequence composition, order correlation, and inferred structure.
Stacking ensemble architecture combining multiple base models for enhanced prediction accuracy and generalization.
SHapley Additive exPlanations (SHAP) for identifying key predictive features.

Main Results:

EnAcrPred significantly outperforms existing methods in Acrs identification across multiple metrics.
The framework demonstrates robustness and superior generalization ability.
SHAP analysis revealed critical features for Acrs recognition.

Conclusions:

EnAcrPred provides an effective computational solution for Acrs identification.
The developed online platform facilitates easy access for researchers, supporting gene editing advancements.
Accurate Acrs prediction contributes to safer and more precise gene editing applications.