Researchers at the Wellcome Sanger Institute have developed a new tool to predict the chances of successfully inserting a gene-edited sequence of DNA into the genome of a cell, using a technique known as prime editing. An evolution of CRISPR-Cas9 gene editing technology, prime editing has huge poten
has used machine learning to accelerate the development of prime editing, a promising gene-editing technology. The study analyzed thousands of DNA sequences introduced into the genome using prime editors, and used the data to train a machine learning algorithm to design the best fix for a given genetic flaw. By using machine learning to streamline the process of designing genetic fixes, this research could help speed up efforts to bring prime editing into clinical use.
to help researchers design the best fix for a given genetic flaw, which promises to speed up efforts to bring prime editing into the clinic.These ‘molecular scissors’ enabled researchers to cut DNA at any position in the genome in order to remove, add or alter sections of the DNA sequence. The technology has been used to study which genes are important for various conditions, from cancer to rare diseases, and to develop treatments that fix or turn off harmful mutations or genes.
In this new study, researchers at the Wellcome Sanger Institute designed 3,604 DNA sequences of between one and 69 DNA bases in length. These sequences were inserted into three different human cell lines, using different prime editor delivery systems in various DNA repair contexts.The insertion efficiency, or success rate, of each sequence was assessed to determine common factors in the success of each edit.
Juliane Weller, a first author of the study from the Wellcome Sanger Institute, said: “Put simply, several different combinations of three DNA letters can encode for the same aminoin a protein. That’s why there are hundreds of ways to edit a gene to achieve the same outcome at the protein level. By feeding these potential gene edits into a machine learning algorithm, we have created a model to rank them on how likely they are to work.
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