Weissman, R. | bioRxiv Science

bioRxiv2026

Reprogramming Cas9 PAM Recognition for Allele-Specific Editing

The therapeutic potential of CRISPR-Cas9 genome editing is fundamentally constrained by the requirement for specific short DNA sequences (PAMs) flanking the target site, limiting access to many clinically relevant genomic loci. This stringent PAM requirement is particularly problematic in applications which require precise positioning, such as base editing and allele-specific editing. Although PAM-relaxed variants have expanded the targetable genome, they incur trade-offs in on-target activity, off-target editing, and cleavage kinetics. This highlights an unmet need for variants that are re-targeted to alternative PAMs in order to maintain the specificity and enzymatic performance inherent to stringent dinucleotide PAM recognition. To overcome these limitations, we developed a yeast selection platform to engineering SpCas9 variants with re-specified PAM recognition. Using a clinically relevant Huntingtons disease gene (HTT) SNP as a proof-of-concept target, we engineered variants with reciprocal NGC and NGT PAM selectivity, as a step toward allele-specific editing in a large percentage of Huntingtons disease patients. These yeast-selected SpCas9 variants retained their modified activity across multiple endogenous HEK293T loci, demonstrating that this specificity is robust across diverse genomic contexts. The variants surpassed PAM-broadened variants on their respective on-target PAM while displaying broad loss of activity across alternative PAMs, effectively re-specifying PAM recognition toward a single dinucleotide sequence. Retargeted variants recovered on-target cleavage kinetics approaching that of wild-type SpCas9, even under competing substrate conditions, demonstrating that PAM re-specification can simultaneously restore catalytic efficiency and improve specificity. Beyond NGC and NGT, we leveraged our high-throughput platform to engineer Cas9 with re-specified activity across multiple additional non-canonical PAMs in yeast, further demonstrating its utility as a general and programmable framework for expanding the therapeutic reach of precision genome editing.

molecular biology↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗