Aug 2026· The FEBS Journal· 0 citations· 55 references
Medicine
TL;DR
UCRISPRa achieved selective activation of olfr544 among more than a thousand homologous olfactory receptor genes in skeletal muscle cells, leading to enhanced mitochondrial biogenesis and broad potential for precise gene regulation and functional studies of complex macromolecular systems.
Abstract
Multidomain proteins play central roles in cellular regulation, yet their intrinsic flexibility and structural instability often hinder optimization for biotechnological applications. Here, we present an integrated structure-guided and deep learning-assisted engineering strategy that combines structure modeling with Protein Message Passing Neural Network (ProteinMPNN)-based sequence design to generate an ultracompact CRISPR activator (uCRISPRa) derived from the miniature CRISPR/Cas12f. Structural and computational analyses identified flexible, nonessential regions within both Cas12f and its single-guide RNA (sgRNA), enabling rational truncation and sequence redesign while preserving DNA-targeting capability. When delivered as mRNA encapsulated in lipid nanoparticles, uCRISPRa achieved selective activation of olfr544 among more than a thousand homologous olfactory receptor genes in skeletal muscle cells, leading to enhanced mitochondrial biogenesis. These findings demonstrate that the integration of structure-based protein engineering with deep learning sequence optimization provides a powerful framework for developing compact and efficient CRISPR effectors, offering broad potential for precise gene regulation and functional studies of complex macromolecular systems.
The CRISPR-Cas12f system is an ultracompact genome-editing platform, yet only a few orthologs exhibit robust activity in mammalian cells. Here, we systematically screened 23 Cas12f orthologs and identified two active nucleases, PspCas12f1 and TcCas12f1, capable of genome editing in human cells. sgRNA scaffold optimization enhanced the basal activity of PspCas12f1. To further improve its performance, we combined structure-guided rational design with protein language model-assisted filtering. Candidate mutations predicted by SaProt were further screened based on structural proximity to the DNA-binding interface and electrostatic compatibility. This integrative strategy identified Q100R and E293R, whose combination yielded the optimized variant enPspCas12f1. enPspCas12f1 achieved genome-editing efficiencies comparable to SpCas9 across multiple endogenous loci while maintaining high specificity. Collectively, our results demonstrate that integrating protein language model-assisted filtering with structure-guided rational design provides an effective strategy for engineering PspCas12f1 and may facilitate the optimization of additional compact CRISPR nucleases.
Jingtong Liu, Sheng-Zhou Wang, Bei Wang et al.· Molecular Therapy· 0 citations
Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute adaptive immune systems in prokaryotes and have transformed life sciences, precision medicine, and synthetic biology as programmable genome-editing tools. Despite their broad utility, naturally occurring DNA-targeting Cas effectors remain constrained by several intrinsic limitations, including large protein size that complicates delivery, stringent protospacer adjacent motif (PAM) requirements that restrict targetable genomic space, and mismatch tolerance that can lead to off-target activity and potential genotoxicity. These challenges have made Cas protein engineering and the discovery of novel CRISPR and CRISPR-like systems from metagenomic resources central to the development of next-generation genome-editing platforms. This Review places recent advances within an integrated synthetic biology engineering continuum that links natural effector discovery, structure-guided hypothesis generation, high-throughput functional screening, machine learning-enabled model construction, and iterative redesign. This Review summarizes progress in the screening, optimization, and functional engineering of DNA-targeting CRISPR and CRISPR-like effectors, with emphasis on structure-guided rational design, directed evolution coupled with high-throughput screening, bioinformatics- and evolution-guided mining of novel systems from large-scale sequence databases, and artificial intelligence-assisted development. By integrating these strategies, we highlight how CRISPR effector engineering is moving toward design-build-test-learn (DBTL)-inspired workflows that expand the functional landscape of genome-editing technologies and advance genome editing toward improved efficiency, safety, and programmability.
Lingwei She, Zeyu Liang, Qin Zou et al.· ACS Synthetic Biology· 1 citation
This review uniquely addresses that gap by presenting an integrated framework that connects molecular design principles with clinical applicability in gRNAs by presenting an integrated framework that connects molecular design principles with clinical applicability.
Masaki Kawamata, S. Niwa, Atsushi Suzuki· Chemical Biology and Drug De...· 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.
Abstract Summary CRISPR-Cas9 has become a widely used tool for genome editing. However, its off-target cleavage caused by partial sequence matches with guide RNAs (gRNAs) remains a critical limitation. Recently, abasic gRNAs (ØXØ) have been developed to enhance target specificity, but their effects vary depending on the positional sequence context. Here, we present abCRISPR, a deep neural network (DNN) framework for the rational design of ØXØ sequences with minimized off-target activity. abCRISPR leverages informative few-shot training with paired datasets of abasic and unmodified gRNAs, using high-quality random mismatch target libraries, exhaustively sequenced for mismatched off-target substrates (n = 97583) in in vitro CRISPR-Cas9 cleavage experiments. Predicted off-target activities for both abasic and unmodified gRNAs showed strong correlation with experimental data (r ≥ 0.95, 10-fold cross-validation). Notably, these comprehensive training sets provide robust ground-truth negatives, enabling accurate and sensitive prediction of off-targets. For unmodified gRNAs, abCRISPR (AUC = 0.98) was validated to outperform existing deep learning-based methods (AUC = 0.45–0.68). When applied to the human genome, abCRISPR generated ØXØ sequences, covering 58 875 004 potent CRISPR-targetable sites with improved target specificity. Together, this work provides a comprehensive bioinformatics resource for safe and precise CRISPR-Cas9 genome editing. Availability and implementation The source code for abCRISPR and training data are available at https://doi.org/10.5281/zenodo.20398246. abCRISPR results for the human genome are available at http://clip.korea.ac.kr/abCRISPR/
Geun-Woo D. Kim, Dowoon Gu, Mingyo Park et al.· Bioinform.· 0 citations
CRISPR-Cas9 gene editing is constrained by unintended off-target effects (OTEs) and inefficient homology-directed repair (HDR). Although modern CRISPR engineering increasingly co-evaluates activity, specificity, repair outcome, delivery, and genomic stability, OTEs and HDR are often analyzed through distinct intervention frameworks. This review formalizes their interdependence as three levels of convergence that define a Pareto-like optimization frontier. Operationally, finite cell numbers, ex vivo editing constraints, and delivery limitations prevent sequential optimization. Mechanistically, Cas9 exposure time influences both off-target accumulation and synchronization with HDR-competent cell-cycle windows. Structurally, on-target activity, Cas9 specificity, and HDR efficiency form a mutually constraining trade-off in which no single point maximizes all three objectives. Convergence therefore extends optimization into an iterative design framework with two additional engineering responses. If the architecture of DSB-based CRISPR systems prevents the optimization frontier from intersecting the clinical acceptance region, circumvention through base- or prime-editor architectures may be preferable. Conversely, when an architecture is clinically viable but constrained by strong mechanistic coupling, collapse weakens those couplings and reshapes the frontier toward greater clinical utility. This review therefore develops an iterative optimization-circumvention-collapse framework in which convergence generates the optimization frontier, identifies mechanistic couplings that may be collapsed, and indicates when architectural dependencies should be circumvented.
Federico Filippone-Thaulero· Journal of high school scien...· 0 citations
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