Aug 2026· ACS Synthetic Biology· 0 citations· 34 references
TL;DR
DeepCRISPR-Typer is presented, a comprehensive computational framework integrating a large protein language model (TEMC-Cas), a deep sequence feature extractor (CRISPR-RepTyper), and an adaptive targeted HMM profiling strategy that significantly reduces computational overhead by dynamically invoking subtype-specific HMM subsets.
Abstract
Accurate identification and classification of CRISPR-Cas systems are crucial for understanding microbial immune mechanisms and developing novel genome-editing tools. However, traditional homology-based mining methods face severe computational bottlenecks and assembly fragmentation challenges when processing massive metagenomic data. Here, we present DeepCRISPR-Typer, a comprehensive computational framework integrating a large protein language model (TEMC-Cas), a deep sequence feature extractor (CRISPR-RepTyper), and an adaptive targeted HMM profiling strategy. DeepCRISPR-Typer integrates array and Cas evidence and significantly reduces computational overhead by dynamically invoking subtype-specific HMM subsets. In metagenomic dataset evaluations, DeepCRISPR-Typer achieved a classification accuracy of 94.26% and demonstrated a significant acceleration of approximately 1 orders of magnitude compared to existing mainstream tools. This research provides a robust and scalable engine for metagenome-scale CRISPR system discovery, significantly expanding the mining toolbox for genome engineering applications.
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
Five previously uncharacterized MG102-like Cas9d orthologs are identified that share the hallmark genomic, sequence, and structural features of type II-D Cas9 and establish compact MG102-like Cas9d orthologs as robust and specific genome editors and provide promising, single-AAV– compatible scaffolds for in vivo therapeutic genome editing.
Qiaochu Wang, Ahmed Saleh, G. S. Rao et al.· bioRxiv· 0 citations
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
Anti-CRISPR (Acr) proteins are natural inhibitors of CRISPR-Cas systems and are important regulators for controllable genome-editing applications. However, their computational identification remains challenging because Acrs are sequence-diverse, weakly conserved, and supported by limited labeled data. Here, we present AcrPLMEvo, a two-stage framework that integrates protein language model (PLM) representations with PSSM derived evolutionary profiles for low-homology Acr prediction. We systematically compared four representative PLM backbones, parameter-efficient adaptation strategies, and alternative PSSM-coupling routes. Evolutionary profiles were not universally beneficial; instead, their effects depended on both PLM backbone and the stage at which they were incorporated. A key finding was that evolutionary information was more consistently beneficial when retained at the downstream decision stage than when used only during PLM adaptation. Guided by this observation, AcrPLMEvo combines PSSM-aware DoRA adaptation of ESM-2 with frozen feature extraction and final-stage evolutionary feature reintroduction. In the matched benchmark comparison, AcrPLMEvo achieved the best overall performance among competing Acr predictors, with an AUC of 0.965 and an AUPRC of 0.778. Its predictive reliability was further supported on an independently curated external set of 44 proteins, where it correctly classified 41 proteins and produced no false positives. These results indicate that stage-consistent integration of evolutionary profiles can improve PLM-based Acr prediction and support the prioritization of low-homology Acr candidates.
K. Tan, Wei-Di Sun, M. Fullwood et al.· IEEE journal of biomedical a...· 0 citations
Accurate identification of CRISPR-Cas9 off-target sites is essential for the safety assessment of genome-editing-based therapies. While numerous in silico prediction tools have been developed, their comparative performance and practical utility in preclinical workflows remain incompletely defined. We performed a systematic benchmarking of 14 in silico CRISPR-Cas9 off-target prediction tools, including both standard approaches and machine learning-based models. The analysis was based on a curated dataset derived from the CRISPRoffT database, comprising 3,827 deep-sequenced genomic sites across 26 guide RNA/Cas9 combinations in human cells. Sites with indel frequencies ≥0.1% were operationally defined as true off-targets. We evaluated tool performance using score distributions, correlation with indel frequencies, precision-recall characteristics, recall among top-ranked candidate sites, and the effect of combining tools. All tools assigned higher scores to true off-target sites compared with nontarget sites, although substantial overlap between classes was observed. Correlation between prediction scores and indel frequencies was weak to moderate, indicating limited ability to predict editing magnitude. Precision-recall performance was moderate across all tools, reflecting inherent trade-offs between sensitivity and specificity. Recall increased with the number of predicted sites considered, reaching approximately 77% among the top 500 and up to 83% among the top 1,250 sites, but leaving a substantial fraction of true off-targets undetected. Combining tools yielded only modest improvements. Current in silico tools enable prioritization of CRISPR-Cas9 off-target candidates but remain limited in their ability to comprehensively identify and quantitatively predict off-target activity. Our findings highlight the importance of considering both ranking performance and candidate site coverage and support the use of combined computational and experimental strategies for robust off-target assessment in preclinical gene editing workflows.
M. M. Kaufmann, Maren Hackenberg, William Jobson Pargeter et al.· Human Gene Therapy· 0 citations
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.
Smith Le, Trung Thach· The FEBS Journal· 0 citations
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