CRISPR-Surfaceome
CRISPR-Surfaceome designs highly efficient single guide RNAs (sgRNAs) targeting human cell surface proteins for functional genomics, pathogen receptor identification, and drug target discovery.
Key Features:
- Targeted design for cell surface proteins: Tailors sgRNA design specifically to human cell surface proteins, including pathogen receptors and potential drug targets.
- Curated cell surface protein sequence database: Provides access to a curated database of cell surface protein sequences for candidate selection.
- sgRNA candidate generation: Generates multiple sgRNA candidates optimized for effective gene knockout.
- Off-target prediction and on-target optimization: Applies computational algorithms to predict off-target effects and enhance on-target activity.
- Experimental validation: Has been validated by experimental knockout studies on ICAM-1 achieving over 80% disruption.
Scientific Applications:
- Pathogen receptor identification: Enables interrogation of genes such as ICAM-1 to assess roles in pathogen entry, with ICAM-1 knockout cells showing resistance to rhinovirus infection.
- Drug target discovery: Facilitates exploration of cell surface proteins' roles in disease pathways to identify therapeutic targets.
- Functional genomics studies: Supports investigations into the biological functions and interactions of cell surface proteins via targeted knockout.
Methodology:
Generates multiple sgRNA candidates from target gene input and applies computational algorithms to predict off-target effects and optimize on-target activity.
Topics
Details
- Tool Type:
- web application
- Operating Systems:
- Mac, Linux, Windows
- Added:
- 10/3/2022
- Last Updated:
- 10/3/2022
Operations
Publications
Mei H, Gu Q, Wang W, Meng Y, Jiang L, Liu J. CRISPR-surfaceome: An online tool for designing highly efficient sgRNAs targeting cell surface proteins. Computational and Structural Biotechnology Journal. 2022;20:3833-3838. doi:10.1016/j.csbj.2022.07.026. PMID:35891797. PMCID:PMC9307495.
PMID: 35891797
PMCID: PMC9307495
Funding: - Postdoctoral Research Foundation of China: 2017M621551
- ShanghaiTech University: 2019F0301-000-01, JYJC202126