Prediction of CRISPR On-Target Effects via Deep Learning (2024)

Abstract: Since the advent of CRISPR-Cas9, a groundbreaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked increase in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of additional CRISPR systems, including CRISPR-Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique advantages for gene modulation. We focus on Cas13d, a variant known for its collateral activity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature critical to its function. We introduce DeepFM-Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolutionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative experiments show that DeepFM-Crispr not only surpasses traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.

## introduction the discovery and development of clustered regularly interspaced short palindromic repeats (crispr) and their associated cas proteins have revolutionized biotechnology and biomedical sciences. initially identified within the adaptive immune systems of bacteria and archaea, these crispr-cas systems have been ingeniously adapted for genome editing. they exploit their inherent ability to make precise and efficient genetic alterations @xcite . the crispr locus is characterized by repetitive base sequences interspersed with spacers derived from past viral and plasmid invaders. this locus is transcribed into a long precursor crispr rna (pre-crrna), which is then processed into mature guide rnas (crrnas). these crrnas direct the cas proteins to cleave complementary sequences in invading genetic elements, thereby providing adaptive immunity. among the various crispr-cas systems, crispr-cas9 of type ii has attracted significant attention due to its simplicity and versatility in genome editing. it employs a dual-rna structure consisting of crrna and trans-activating crrna (tracrrna), guiding the cas9 enzyme to specific dna targets @xcite . crispr-cas12, a type v system, offers unique advantages over cas9 by utilizing a single rna for both crispr array processing and target dna recognition. this system cleaves target dna in a staggered manner, increasing the diversity of editable sequences and enhancing the potential for multiplex editing. another innovative addition to the crispr toolkit is crispr-cas13, which targets rna instead of dna, facilitating not only gene modulation without altering the genome but also enabling novel diagnostic applications due to its collateral cleavage activity upon target recognition @xcite . the specificity and efficacy of guide rna design are crucial for maximizing on-target actions and minimizing offtarget effects, which are particularly concerning with cas9's potential dna off-targets and cas13's rna-targeted collateral activity @xcite . crispr screens, including those for viability and facs-sorting, have become instrumental in evaluating the effectiveness and specificity of crispr systems. these screens employ a multitude of guide rnas to ascertain factors influencing knockout efficiency and to refine guide designs to balance activity and specificity @xcite . unlike tools focused solely on crispr-cas9, those designed for cas13 must account for rna secondary structures, significantly impacting guide rna efficiency @xcite . to address challenges related to data scarcity and the complexity of integrating structural and evolutionary information, we introduce deepfm-crispr. this model leverages advanced transformer-based architectures and large language models, which have revolutionized fields ranging from natural language processing @xcite @xcite to bioinformatics due to their ability to handle large datasets and extract deep, contextual relationships within data. our transformer @xcite model processes a wide array of inputs, offering a predictive score for on-target efficiency based on enriched representations of evolutionary and structural insights. validated against a dataset of 22,599 cas13d sgrnas @xcite , deepfm-cas13d outperforms conventional machine learning methods and existing prediction tools, particularly in targeting non-coding rnas @xcite . ## method our methodology integrates multiple deep learning architectures and data representation techniques to predict the on-target efficiency of sgrnas based on their sequences and secondary structures. this approach harnesses the power of large language models, convolutional networks, and transformer encoders to process and analyze the complex biological data. the details of deepfm-crispr shown in fig. 1 . ## dataset to explore the efficiency and specificity of the cas13d system, we performed a comprehensive two-vector crispr/cas13d proliferation screen on the a375 melanoma cell line, guided by protocols from a well-established benchmark study @xcite . the screening library consisted of 10,830 sgrnas targeting a total of 426 genes, including 192 protein-coding genes and 234 long non-coding rnas (lncrnas). this selection included 94 essential genes and 14 non-essential genes, previously identified in a375 cells via rna interference and crispr-based screenings. our library design aimed to robustly model the efficiencies of cas13d sgrnas, allocating approximately 30 guides per gene. this strategy was intended to overcome potential biases observed in previous tiling screens that were limited to only 2-3 genes, thereby enhancing the representativity of the sgrna library. following the screening, sgrna abundance was quantitatively assessed through high-throughput sequencing. data analysis was conducted using the mageck algorithm to evaluate sgrna performance and gene essentiality. quality control checks were stringent, with an average of 5.6 million reads per sample, ensuring reliable data capture. each guide was represented by over 300 reads, with fewer than four guides missing per gene-indicative of comprehensive coverage. the gini coefficient was maintained below 0.06, confirming a uniform and non-biased distribution of guide representation across the dataset. most notably, 20 of the essential genes demonstrated significant depletion, with a false discovery rate (fdr) of less than 10%, underscoring the screening's effectiveness in identifying gene functionalities crucial for cell proliferation in melanoma. ## related work the crispr-cas systems @xcite have revolutionized the field of genetic engineering, offering unprecedented precision in gene editing @xcite @xcite . since its inception, the crispr-cas9 system has been extensively studied and applied across various biological contexts due to its ability to make targeted dna modifications. however, the discovery of @xcite , which targets rna, has opened new avenues for gene modulation without altering the dna itself. among the variants, cas13d is particularly notable for its collateral activity, where it cleaves nearby non-target rna sequences upon activation, offering potential for diagnostic as well as therapeutic applications @xcite . recent advancements have leveraged computational models to enhance the predictability and efficiency of crispr systems. for instance, models like crisprpred-seq @xcite and deepcas @xcite have utilized traditional machine learning and deep learning techniques to predict sgrna efficacy, primarily focusing on crispr-cas9. these models often rely on sequence-based features and have shown significant promise in reducing off-target effects and enhancing on-target efficiency. however, the unique mechanisms and rna targeting properties of cas13d @xcite present distinct challenges and opportunities that these models are not tailored to address. deepfm-crispr introduces an innovative approach by integrating large language models and transformer-based architectures to specifically enhance the performance of crispr-cas13d systems. this model surpasses traditional and recent computational approaches by effectively capturing and processing extensive evolutionary and structural information pertinent to rna. the use of a large language model allows deepfm-crispr to understand and predict the complex dynamics of rna interactions @xcite , which are crucial for achieving high precision in rna-targeted gene editing. our model not only builds upon the existing body of knowledge but also sets a new benchmark for computational tools in the crispr field, particularly for rna-targeting systems. by focusing on cas13d, deepfm-crispr addresses a critical gap in the existing tools, offering refined predictions and insights that are vital for both research and therapeutic applications. ## conclusion the deepfm-crispr model has proven to be exceptionally versatile and robust, demonstrating not only its efficacy with the cas13d system but also its potential applicability to other crispr-cas systems. leveraging advanced large language model techniques, deepfm-crispr adeptly captures complex genetic interactions and sequence nuances essential for precise genome editing. the model's strength lies in its ability to integrate sophisticated deep learning techniques with large-scale genomic data, enabling it to surpass traditional models in both predictive accuracy and operational efficiency. this adaptability positions deepfm-crispr as a valuable asset across a broad spectrum of crispr technologies, potentially revolutionizing gene editing methodologies across diverse applications.