Cas1-Anchored Class 2 CRISPR-Cas Effector Discovery
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Solution Overview
Problem
The existing CRISPR-Cas systems lack a comprehensive classification method due to the extreme diversification of Cas protein sequences and locus architecture, making it difficult to identify novel Class 2 CRISPR-Cas systems effectively.
Innovation Solution
A computational pipeline is developed to identify novel Class 2 CRISPR-Cas systems by using Cas1 as a seed, analyzing genomic and metagenomic sequences, and employing unsupervised learning to classify CRISPR loci based on structural features and nuclease domains, thereby discovering new CRISPR effectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional classification methods are used for CRISPR-Cas systems, then existing known systems can be categorized, but novel Class 2 systems cannot be effectively identified due to extreme sequence and architectural diversification
Solution Approach 1:
The patent segments the CRISPR-Cas system classification into two distinct classes based on effector module architecture: Class 1 with multi-subunit complexes and Class 2 with single effector proteins. This segmentation enables the development of targeted identification methods for each class, particularly improving the detection of novel Class 2 systems by focusing on their distinctive single-effector architecture rather than attempting universal classification of all CRISPR-Cas variants
Solution Approach 2:
The patent changes the classification parameters from relying on sequence similarity of Cas proteins (which shows extreme diversification) to using architectural features such as effector module composition, locus organization, and the presence/absence of specific genes like cas1 and cas2. This parameter change enables effective identification of novel Class 2 systems that would be missed by sequence-based methods
2Adaptability or versatility
If comprehensive classification of all CRISPR-Cas systems is attempted, then all variants can be covered, but the complexity increases due to lack of universal cas genes and extreme diversification
Solution Approach 1:
The patent identifies universal features that define Class 2 CRISPR-Cas systems across all its diverse variants: the presence of a single effector protein that performs multiple functions (crRNA processing, target recognition, and cleavage), the association with cas1 and cas2 genes, and specific locus architectural patterns. This universal framework simplifies classification by providing consistent criteria applicable to all Class 2 systems regardless of their sequence diversification
Solution Approach 2:
The patent adds new dimensions to the classification system by incorporating locus organization analysis and effector module architectural features beyond simple sequence similarity. This multi-dimensional approach includes examining the arrangement of CRISPR arrays, cas genes, and effector loci, as well as the structural characteristics of effector proteins, thereby comprehensively covering diverse CRISPR-Cas variants without excessive complexity
3Measurement precision
If sequence similarity analysis is used for Cas proteins, then conserved variants can be identified, but novel effectors are missed due to extreme diversification of Cas protein sequences
Solution Approach 1:
The patent uses conserved adapter proteins cas1 and cas2 as intermediaries to identify novel Class 2 CRISPR-Cas systems. Instead of directly searching for highly divergent effector proteins, the method first identifies loci containing cas1 and cas2 genes, then examines associated effector candidates within these loci. This intermediary approach leverages the conservation of cas1 and cas2 to anchor the search, making it feasible to detect novel effectors that have diverged beyond sequence similarity recognition
Solution Approach 2:
The patent performs preliminary identification of candidate effector proteins by filtering for open reading frames (ORFs) with specific characteristics (length >300 amino acids, absence of known protein domains) in proximity to CRISPR arrays and cas genes. This preliminary screening narrows down the search space before applying more rigorous classification, enabling the detection of novel effectors that would be missed by direct sequence similarity analysis of highly diversified Cas proteins
Data Source
AI summary
Disclosed here is a method of identifying novel CRISPR effectors, comprising: identifying sequences in a genomic or metagenomic database encoding a CRISPR array; identifying one or more Open Reading Frames (ORFs) in said selected sequences within 10 kb of the CRISPR array; discarding all loci encoding proteins which are assigned to known CRISPR-Cas subtypes and, optionally, all loci encoding a protein of less than 700 amino acids; and identifying putative novel CRISPR effectors, and optionally classifying them based on structure analysis.