Machine Learning Engineers Bespoke Cas9 Enzymes for Gene Editing – Genetic Engineering and Biotechnology News
2025-04-22T22:58:50Z
Researchers have developed a machine learning model that predicts Cas9 proteins that can be tailored with designer properties for therapeutic use.
In a new study published in Nature titled, “Custom CRISPR-Cas9 PAM variants via scalable engineering and machine learning,” researchers from Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) have developed a machine learning model that permits the prediction of bespoke Cas9 proteins that are more uniquely suited to specific targets and can be tailored with designer properties for research or therapeutic use. The authors stated that this scalable methodology can engineer and characterize the biochemical properties of hundreds or thousands of novel Cas9 proteins.
“We’re excited to share these enzymes with the genome editing community and to get feedback on their performance as nucleases, base editors, and as other genome editing modalities,” said Benjamin Kleinstiver, PhD, associate professor of pathology at HMS, in an interview with GEN.
To safely and precisely correct mutations that cause genetic diseases, genome editing technology must be programmed to target patient-specific sequences while limiting off-target effects. Cas nucleases recognize genomic targets by reading protospacer adjacent motifs (PAMs), which initiate guide RNA pairing with the target site. In the case of commonly used Streptococcus pyogenes Cas9 (SpCas9), pairing requires the standard PAM sequence, 3’NGG, which inevitably restricts the use of the enzyme to PAM-encoding genomic sequences.
To expand access to the genome, a common engineering strategy is the relaxation of the PAM to allow editing to new sites while retaining activity against NGG, thereby creating generalist enzymes for broad applications. However, enabling efficient on-target editing while minimizing off-target effects for relaxed PAM enzymes remains a challenge.
In the new study, Kleinstiver’s team generated experimental PAM profiles for hundreds of engineered SpCas9 enzymes to train a neural network that related amino-acid sequence to PAM specificity. The resulting PAM machine learning algorithm (PAMmla) predicted the PAMs of some 64 million SpCas9 enzymes. It then identified efficacious and specific enzymes that outperformed existing evolution-based and engineered candidates as nucleases and base editors in human cells while reducing off-target effects.
Among the examples of user-directed Cas9 enzyme design, the MGH group performed selective targeting of the P23H mutation of rhodopsin in human cells and mice. This mutation is a common cause of autosomal dominant retinitis pigmentosa (adRP), a genetic eye disease leading to vision loss.
“We envision that the general framework of scalable engineering, deep characterization, and utilizing machine learning to predict a larger universe of proteins would be extensible to many exciting areas, including other properties of Cas9 enzymes, like target site specificity and on-target activity, to non-CRISPR enzymes entirely,” said Rachel Silverstein, first author of the study and a graduate student at HMS, in an interview with GEN.
Additional applications include extending this engineering workflow to other protein domains in next-generation editors, such as deaminase domains for base editors, reverse transcriptase domains for prime editors, and DNA polymerases for click editors.
Machine learning offers key advantages over traditional experimental engineering strategies, which often struggle with predicting the functional impact of multiple simultaneous mutations in addition to facing laborious and time-consuming experimental selection strategies. The authors stated that computational predictions can screen larger numbers of enzymes bearing more diverse combinations of amino-acid substitutions compared to experimental methods alone, thereby increasing the probability of identifying optimal enzymes across a deeper mutational space.
According to Silverstein, a key to this method is to establish a facile and scalable biochemical assay that can yield rich data about thousands of enzymes in parallel, providing the requisite data to train machine learning models.
“Longer-term, we envision that the use of machine learning can be widely applied to potentiate the activities of genome-editing technologies that will be beneficial for creating a diverse and complete toolbox of technologies,” Silverstein told GEN.
The authors have made PAMmla available as a web tool to expedite the customization of SpCas9 enzymes for research or translational uses.
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