Viral Strain Identification via Characteristic Residue Extraction
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Solution Overview
Problem
Current methods are inadequate for unambiguously distinguishing among strains of single-stranded RNA (ssRNA) viruses, particularly picornaviruses and caliciviruses, due to high sequence divergence and subtle sequence variations, which poses challenges in epidemiology, diagnostics, and vaccine development.
Innovation Solution
A software product that uses unique characterizing residues to automatically identify strains of partial or complete capsid sequences through residue-wise comparisons, employing partitioned phylogenetic trees and decision-making algorithms to accurately classify and predict virus strains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sequence similarity scores are used to identify virus strains, then the method is simple to implement, but it fails to distinguish strains with subtle sequence variations (sequence identity ≤10%)
Solution Approach 1:
The patent extracts and focuses on specific characteristic residues (key positions) within the capsid protein sequence that are most informative for strain differentiation. Instead of analyzing the entire sequence or relying on overall similarity scores, the method identifies and compares only the critical residues at predetermined positions, thereby achieving high precision in distinguishing subtle strain variations while maintaining computational efficiency.
Solution Approach 2:
The patent introduces characteristic residue profiles as an intermediary between raw sequence data and strain identification. These profiles serve as mediators that capture the essential discriminatory information from complex sequences, enabling accurate strain classification without requiring direct comparison of entire sequences or reliance on similarity scoring systems.
2Adaptability or versatility
If capsid sequences are analyzed to identify strains, then the most diverse regions are captured, but the sequences show maximum variations making identification intractable
Solution Approach 1:
The patent extracts only the essential discriminatory information from capsid sequences by focusing on characteristic residues at key positions. This extraction approach captures the maximum virus diversity information present in capsid sequences while eliminating the complexity introduced by excessive sequence variations, making strain identification tractable through comparison of selected critical positions rather than entire sequences.
Solution Approach 2:
The patent applies local quality by assigning different importance weights to different positions in the capsid sequence. Rather than treating all residues equally, the method identifies specific local regions (characteristic residue positions) that contain the most discriminatory power for strain differentiation, thereby efficiently capturing diversity while simplifying the identification process.
3Reliability
If experimental techniques are used to detect ssRNA virus strains, then direct detection is possible, but the methods are time-consuming and fail when strains are non-cultivable
Solution Approach 1:
The patent creates a computational model (characteristic residue profile database) that serves as a copy or representation of viral strain characteristics. Instead of performing time-consuming experimental procedures on actual virus samples, the method compares sequence data against pre-established computational profiles, enabling rapid strain identification without the time delays and limitations of culturing-based experimental techniques.
Solution Approach 2:
The patent performs preliminary action by pre-analyzing and storing characteristic residue profiles of known virus strains in a database before actual detection is needed. This advance preparation allows rapid comparison and identification during actual detection scenarios, eliminating the need for time-consuming experimental procedures and enabling quick response even for non-cultivable strains.
4Loss of information
If homology comparison based computational methods are used, then existing sequences can be matched, but the methods are time-consuming and ambiguous when only partial sequences are available
Solution Approach 1:
The patent extracts and utilizes only the most informative characteristic residues from partial sequences, rather than attempting to perform comprehensive homology comparisons that require full sequences. This extraction approach enables effective use of limited partial sequence data by focusing on the critical positions that provide maximum discriminatory power, thereby reducing computation time while maintaining identification accuracy.
Solution Approach 2:
The patent changes the parameter of comparison from overall sequence homology to specific residue matching at characteristic positions. This parameter transformation allows the method to work effectively with partial sequences by focusing computational effort on the most informative positions, thereby reducing ambiguity and computation time compared to traditional homology-based approaches that require complete sequences.
Data Source
AI summary
A system implemented on a computer is disclosed for automatically identifying strains of partial or complete capsid sequences of picorna and caliciviruses, two of the most highly diverse ssRNA virus families.