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An Integrated Approach for Microprotein Identification and Sequence Analysis
Published on: July 12, 2022
Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete
Sita Sirisha Madugula1, Pranav Pujar2, Nammi Bharani2
1Department of Pharmaceutical Sciences, University of North Texas System College of Pharmacy, University of North Texas Health Science Center, Fort Worth, Texas, United States.
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 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.
Insights
Researchers identified key protein features distinguishing Cas12 and Cas9 gene editing systems. This discovery aids in understanding their catalytic mechanisms and designing improved CRISPR-Cas tools for genetic diseases.
Area of Science:
- Biotechnology and Genetic Engineering
- Computational Biology and Bioinformatics
Background:
- CRISPR-Cas technology offers gene-level defect correction for genetic diseases, utilizing Cas proteins as nucleases guided by RNA.
- Limitations of current Cas proteins (e.g., Cas9) include large size, low efficiency, and off-target effects, necessitating the discovery of novel variants.
- Understanding the distinct features of Cas protein families is crucial for developing improved gene editing tools.
Approach:
- Developed Random Forest (RF) binary classifiers to differentiate Cas12 and Cas9 from non-Cas proteins using 13,495 features.
- Constructed multiclass RF classifiers to distinguish between Cas9, Cas12, and non-Cas proteins.
- Validated model performance on test and independent datasets, achieving high accuracy (95% for Cas12, 97% for Cas9) and F1 score (0.97 for multiclass).
Key Points:
- Identified specific protein descriptors crucial for distinguishing Cas12 (e.g., Schneider-lag, charge, volume, polarizability) and Cas9 (e.g., Tripeptide Composition - TPC) families.
- Discovered conserved tripeptides (PWN, PYY, HHA, DHI) in Cas9, with HHA and DHI linked to DNA cleavage activity.
- Proposed PWN and PYY as potentially essential for Cas9 family function.
Conclusions:
- The study elucidates unique protein attributes differentiating Cas9 and Cas12 families.
- Identified key descriptors provide insights into the catalytic mechanisms of Cas9 and Cas12.
- Findings facilitate the design of novel Cas systems with enhanced gene-editing properties for therapeutic applications.
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