Researchers use machine learning to engineer 'bespoke enzymes' for gene editing
Genome editing has advanced at a rapid pace with promising results for treating genetic conditions -- but there is always room for improvement. A new paper showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm -- known as PAMmla -- that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets.
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Genome editing has advanced at a rapid pace with promising results for treating genetic conditions -- but there is always room for improvement. A new paper showcases the power of scalable protein engineering combined with machine learning to boost progress in the field of gene and cell therapy. In their study, authors developed a machine learning algorithm -- known as PAMmla -- that can predict the properties of about 64 million genome editing enzymes. The work could help reduce off-target effects and improve editing safety, enhance editing efficiency, and enable researchers to predict customized enzymes for new therapeutic targets.
