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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 by investigators from Mass General Brigham 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, the authors developed a machine learning algorithm—known as PAMmla—that can predict the properties of approximately 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. The results are in Nature.

"Our study is a first step in dramatically expanding our repertoire of effective and safe CRISPR-Cas9 enzymes. In our manuscript, we demonstrate the utility of these PAMmla-predicted enzymes to precisely edit disease-causing sequences in primary human cells and in mice," said corresponding author Ben Kleinstiver, Ph.D., Kayden-Lambert MGH Research Scholar associate investigator at Massachusetts General Hospital (MGH).

"Building on these findings, we are excited to have these tools utilized by the community and also apply this framework to other properties and enzymes in the genome editing repertoire."

CRISPR-Cas9 enzymes can be used to edit genes at locations throughout the genome, but there are limitations to this technology. Traditional CRISPR-Cas9 enzymes can have off-target effects, cleaving or otherwise modifying DNA at unintended sites in the genome. The newly published study aims to improve this by using machine learning to better predict and tailor enzymes to hit their targets with greater specificity.

The approach also offers a scalable solution. Other attempts at engineering enzymes have had a lower throughput and typically yielded enzymes in quantities that were lower by orders of magnitude.

One of the key elements of utilizing CRISPR-Cas9 technologies is that the enzymes must locate and bind to a short DNA sequence called a protospacer adjacent motif (PAM). Researchers used machine learning to predict the PAMs of millions of Cas9 enzymes, identifying a set of novel engineered Cas9 enzymes that would have the best on-target activity and specificity. The researchers conducted proof-of-concept experiments in human cells and a mouse model of retinitis pigmentosa, finding that the bespoke enzymes had greater specificity.

"A major outcome of this work is the creation of this PAMmla model that can now be used by researchers to predict customized enzymes that are uniquely tuned for their specific use cases," said lead author Rachel A. Silverstein, Ph.D. candidate, NSERC postgraduate scholar and 2024 Albert J. Ryan Fellow in the Kleinstiver lab at MGH.

"The result of this model is that we now have an enormous toolbox of safe and precise Cas9 proteins that can be utilized for a variety of research and therapeutic applications."