Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

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dc.creatorMadugula, Sita S.
dc.creatorPujar, Pranav
dc.creatorBharani, Nammi
dc.creatorWang, Shouyi
dc.creatorJayasinghe-Arachchige, Vindi M.
dc.creatorPham, Tyler
dc.creatorMashburn, Dominic
dc.creatorArtilis, Maria
dc.creatorLiu, Jin
dc.creator.orcid0000-0002-1067-4063 (Liu, Jin)
dc.date.accessioned2024-05-30T16:50:37Z
dc.date.available2024-05-30T16:50:37Z
dc.date.issued2024-02-08
dc.description.abstractThe 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 like 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 the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. 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 (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of 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 datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.
dc.description.sponsorshipThis work was supported by a grant from National Institute of General Medical Sciences of the National Institutes of Health (R35GM133657). We thank our collaborator professor Shouyi Wang and his group from the University of Texas Arlington (UTA) for their continuous and valuable research support all along this study.
dc.identifier.citationMadugula, S. S., Pujar, P., Bharani, N., Wang, S., Jayasinghe-Arachchige, V. M., Pham, T., Mashburn, D., Artilis, M., & Liu, J. (2024). Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum. bioRxiv : the preprint server for biology, 2024.01.22.576286. https://doi.org/10.1101/2024.01.22.576286
dc.identifier.urihttps://hdl.handle.net/20.500.12503/32814
dc.publisherCold Spring Harbor Laboratory
dc.relation.urihttps://doi.org/10.1101/2024.01.22.576286
dc.rights.holderThe copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
dc.rights.licenseAttribution-NoDerivs 4.0 International (CC BY-ND 4.0 Deed)
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.sourcebioRxiv
dc.titleIdentification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum
dc.typeArticle
dc.type.materialtext

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