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Gene editing is a rapidly evolving field with profound implications for agriculture, biotechnology, and medicine. One of the most promising tools in this arena is the CRISPR-Cas system, originally derived from bacterial immune defence mechanisms. This technology offers a precise means of altering genetic sequences, but its adaptation from microbial environments to more complex eukaryotic cells often compromises efficiency and specificity.
Researchers have sought to enhance the functionality of CRISPR systems to address this challenge. Traditional methods like directed evolution and structure-guided design have facilitated some progress. However, these techniques struggle with protein evolution’s intricate and unpredictable nature, often leading to suboptimal performance when these systems are applied outside their natural contexts.
A research team from Profluent Bio, Berkeley, CA, USA; Laboratory of Protein Design and Immunoengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland; Department of Microbiology, University of Washington, Seattle, WA, USA has employed artificial intelligence to overcome these limitations. The team has pioneered the design of novel gene editors by training large language models on a dataset comprising over a million CRISPR operons and 26 terabases of assembled genomes. This AI-driven approach sidesteps the slow and uncertain process of natural evolution, enabling the rapid generation of diverse and highly functional proteins.
The results of this AI-centric methodology are the newly designed proteins, including the standout OpenCRISPR-1, have significantly improved target accuracy and reduced off-target effects. For example, OpenCRISPR-1 demonstrated compatibility with base editing, a refined form of gene editing that allows for single nucleotide changes without creating double-strand breaks. In practical terms, OpenCRISPR-1 achieved editing efficiency comparable to the best existing systems like SpCas9 but with far fewer unwanted mutations, highlighting an up to 95% reduction in off-target activity in some cases.
These AI-generated proteins exhibited an expanded range of functionality. They maintained high activity across varied conditions, easily adapting to different temperatures and molecular environments. This adaptability is critical for applications in human health, where precision and reliability are paramount. The research documented the creation of over four million protein sequences, from which a select group was chosen for detailed characterisation based on their robustness and specificity.
In conclusion, the study underscores a significant advancement in gene editing technology by addressing the functional limitations of CRISPR-Cas systems in non-native environments. Researchers have innovatively generated diverse functional gene editors by harnessing artificial intelligence. The standout, OpenCRISPR-1, demonstrated high efficiency and specificity and remarkably reduced off-target effects. This breakthrough illustrates the potential of AI-driven methodologies to refine and accelerate the development of gene editing tools, paving the way for more precise and reliable applications in medicine and agriculture. This research sets a new standard in the field and promises transformative future developments.
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“}]] [[{“value”:”Gene editing is a rapidly evolving field with profound implications for agriculture, biotechnology, and medicine. One of the most promising tools in this arena is the CRISPR-Cas system, originally derived from bacterial immune defence mechanisms. This technology offers a precise means of altering genetic sequences, but its adaptation from microbial environments to more complex eukaryotic
The post OpenCRISPR: An Open-Source AI-Generated Gene Editor that Exhibits Compatibility with Base Editing appeared first on MarkTechPost.”}]] Read More AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Staff, Tech News, Technology