Aug 2026· Nature Biotechnology· 0 citations· 46 references
Medicine
TL;DR
EvoMax is established as an integrated strategy for engineering compact eukaryotic Fz2 genome editors and FanzMAX v3-hLa is identified as a high-efficiency programmable nuclease for mammalian genome editing.
Abstract
Efficient protein engineering is constrained by vast sequence space and limited experimental throughput, particularly for protein families that lack large mutational datasets. Here we combine Fanzor2 (Fz2) ortholog discovery, ωRNA scaffold engineering and EvoMax, a model-guided prioritization strategy for sparse-data engineering of compact eukaryotic Fz2 nucleases. EvoMax integrates iterative experimental profiling with Gaussian process regression, protein language models and inverse folding to navigate complex sequence-to-fitness landscapes. Applied to eukaryotic Fz2 nucleases, this strategy yielded a high-performance variant, FanzMAX v3-hLa, achieving up to 97% editing efficiency at the best-performing endogenous locus and a mean editing efficiency of ~33% across 19 endogenous loci, outperforming the established compact genome editors enNlovFz2 and enCnCas12f1 by more than 2.6-fold. In vivo editing of hPCSK9 in humanized mice supported the translational potential of optimized Fz2 editors. Together, these results establish EvoMax as an integrated strategy for engineering compact eukaryotic Fz2 genome editors and identify FanzMAX v3-hLa as a high-efficiency programmable nuclease for mammalian genome editing.
MULTI-evolve is a model guided, universal, targeted installation of multimutants framework that rapidly designs hyperactive multimutant proteins and improves the identi fi cation of productive mutations compared with individual PLMs alone.
J. Koo, Young-Ho Park, Sun-Uk Kim· Signal Transduction and Targ...· 0 citations
Natural variation in PAM recognition among SaCas9 orthologs is analyzed and StaCas9 is identified as a compact and efficient nuclease recognizing an NNG PAM, establishing StaCas9 as a high-performance genome-editing tool for therapeutic applications.
An efficient Cas9d system (Cas9dUltra) is developed through gRNA and protein engineering, and its base editors (9dBEs) further developed through gRNA and protein engineering, enabling efficient and precise genome editing in human cells.
Qingquan Xiao, Zhijin Tian, Luqi Weng et al.· Advancement of science· 0 citations
This work introduces ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics.
D. P. Kulathunga, Divyanshu Shukla, D. Potoyan· bioRxiv· 0 citations
Directed evolution is commonly used in protein engineering, where mature molecules are routinely improved through iterative local search of amino acid space. Here, we extend this principle to coding DNA. We developed a language-model-guided framework that iteratively refined industry-optimized coding sequences of clinical-stage therapeutics through synonymous exploration of codon space. Across 23 antibody-based therapeutics, SynCodonLM-guided refinement significantly increased recombinant expression in CHO cells for 17 molecules (74% responder rate), without significant compromise of product-quality or biophysical attributes. Moreover, changes in model likelihood predicted expression gains more effectively than heuristic statistical or mRNA-structure descriptors, despite no explicit expression objective. Codon-level likelihood also tracked temporal progression in influenza A H1N1 sequences, indicating the model captures evolutionary signal. These results show that even production-optimized sequences retain accessible fitness in synonymous codon space, establishing directed evolution as a practical strategy to improve biologic expression, a key manufacturing bottleneck, without altering protein sequence.
James Heuschkel, Laura Kingsley, Jon Reed et al.· bioRxiv· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
