ArticleJournal of chemical information and modeling2024
Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Advancing CRISPR with deep learning: A comprehensive review of models and databases.Molecular therapy. Nucleic acids · 2025Review
- Engineering stimuli-responsive CRISPR-Cas systems for versatile biosensing.Analytical and bioanalytical chemistry · 2025Review
- CasGen: A Regularized Generative Model for CRISPR Cas Protein Design with Classification and Margin-Based Optimization.bioRxiv : the preprint server for biology · 2025Article
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Abstract
The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In this study, we aim to elucidate the unique protein features associated with Cas9 and Cas12 families and identify the features distinguishing each family from non-Cas proteins. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,494 features) encoding various physiochemical, topological, constitutional, and coevolutionary information on Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and non-Cas proteins. All the models were evaluated rigorously on the test and independent data sets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 92% and 95% on their respective independent data sets, while the multiclass classifier achieved an F1 score of close to 0.98. We observed that Quasi-Sequence-Order (QSO) descriptors like Schneider.lag and Composition descriptors like charge, volume, and polarizability are predominant in the Cas12 family. Conversely Amino Acid Composition descriptors, especially Tripeptide Composition (TPC), predominate the Cas9 family. Four of the top 10 descriptors identified in Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all Cas9 proteins and located within different catalytically important domains of the
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