AcrCasPPI: Ensemble-based machine learning architecture for prediction of protein-protein interaction between anti-crispr protein (acr) and CRISPR-associated (cas) protein
The CRISPR-Cas system is a powerful tool in genome editing that has transformed research in animals and plants due to its effectiveness. The Cas protein, a key component of this system, cuts the targeted genetic material to introduce desired changes. However, the uncontrolled activity of Cas protein can lead to unintended effects. Anti-CRISPR (Acr) proteins, found in phages and other genetic elements, naturally inhibit Cas proteins to help phages evade the host's immune system. Acr proteins regulate Cas nuclease activity by interacting with different domains, effectively blocking the CRISPR function. Understanding the interactions between these rival proteins is crucial to control the cutting machinery when necessary. Experimental methods for studying protein interactions are costly and time-consuming, prompting the use of computational approaches. In this study, a machine learning-based predictive model was developed with an accuracy of over 95% to identify interactions between Acr and Cas proteins. This model can automate the discovery of natural inhibitors for Cas proteins, enhancing the precision and efficiency of CRISPR-Cas technology. To support different users, a web application called AcrCasPPI (available at http://login1.cabgrid.res.in:5050/) and a Python package named acrcasppi-ml (available at https://pypi.org/project/acrcasppi-ml/) were created for easy access. The web server is hosted at the Advanced Supercomputing Hub of Omics Knowledge in Agriculture (ASHOKA), ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012.
Murmu, S., Chaurasia, H., Guha Majumdar, S., Rao, A. R., Rai, A., & Archak, S. (2022). Prediction of protein–protein interactions between anti-CRISPR and CRISPR-Cas using machine learning technique. Journal of Plant Biochemistry and Biotechnology, 1-13. https://doi.org/10.1007/s13562-022-00813-1