Bioinformatic Peptide Binding Prediction for Conserved Microbial Epitopes
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Solution Overview
Problem
Current methods struggle to identify conserved MHC high-affinity binding peptides and B-cell epitopes across multiple strains of microorganisms, leading to challenges in vaccine development for infectious diseases, cancer therapies, and autoimmune diseases, with no effective vaccine for brucellosis and limited understanding of peptide binding mechanisms.
Innovation Solution
A computer-implemented process using mathematical expressions and machine learning tools to modify amino acid sequences for enhanced or reduced MHC binding affinity, identifying epitopes and designing peptides with specific characteristics, applicable to various proteins, including microbial and mammalian proteins, and developing vaccines and diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional epitope mapping methods are used, then identification of MHC high affinity binding peptides and B-cell epitopes is attempted, but the methods fail to identify conserved epitopes across multiple strains of microorganisms
Solution Approach 1:
The patent segments the epitope identification process into distinct computational modules: sequence alignment to identify conserved regions, MHC binding affinity prediction using machine learning models, and B-cell epitope prediction. This segmentation allows each module to be optimized independently and enables the system to handle multiple strains by processing their sequences through the same segmented workflow, thereby achieving both precision and cross-strain versatility.
Solution Approach 2:
The patent changes key parameters including using multiple MHC allele specificity profiles, adjusting binding affinity thresholds, and modifying sequence alignment parameters to account for strain variability. By dynamically adjusting these parameters based on the input strain sequences, the system maintains high identification accuracy while adapting to diverse microbial strains.
2Productivity
If reverse vaccinology approach is adopted, then rapid identification of proteins with potential epitopes is achieved, but the approach cannot handle antigenic variability between different isolates
Solution Approach 1:
The patent performs preliminary sequence alignment and identification of conserved regions across multiple strains before proceeding to epitope prediction. This preliminary action filters out variable regions that would not provide cross-protection, ensuring that subsequent high-speed predictions focus only on conserved, reliable epitope candidates. This maintains both speed and reliability by pre-processing the input data to eliminate unreliable variants.
Solution Approach 2:
The patent creates a universal epitope prediction system that processes sequences from multiple microbial strains simultaneously. The system uses multi-functional algorithms that can handle both highly variable organisms like RNA viruses and more stable bacteria, applying the same core methodology across diverse targets. This universality enables rapid identification of conserved epitopes that provide cross-protection across different isolates.
3Ease of operation
If cancer vaccine therapies use a single tumor antigen, then activation of cytotoxic T-lymphocytes is achieved, but the therapy cannot treat the diversity of cancers
Solution Approach 1:
The patent implements a dynamic epitope prediction system that adapts to different cancer types by analyzing their specific antigen sequences. Rather than using a fixed single-antigen approach, the system dynamically selects and prioritizes epitopes based on the input tumor antigen sequence, MHC allele profile, and predicted immunogenicity. This dynamic adaptation maintains operational simplicity while achieving versatility across diverse cancer types.
Solution Approach 2:
The patent applies local quality optimization by identifying and emphasizing specific high-value epitopes within each cancer antigen rather than treating all antigens uniformly. The system predicts and prioritizes epitopes with highest binding affinity and immunogenicity for each specific cancer type, creating customized vaccine compositions tailored to local cancer characteristics while maintaining a streamlined prediction process.
4Productivity
If live attenuated Brucella organisms are used as vaccines, then control of disease in livestock is achieved, but the vaccines can still cause disease in humans and livestock if incorrectly applied
Solution Approach 1:
The patent extracts and identifies specific protective epitopes from Brucella proteins using computational prediction. By taking out only the essential immunogenic peptide sequences rather than using whole attenuated organisms, the system creates subunit vaccines that provide protection without the safety risks of live organisms. This extraction approach maintains vaccine effectiveness by preserving key epitopes while eliminating harmful factors associated with live attenuated vaccines.
Solution Approach 2:
The patent creates synthetic copies of protective epitopes through computational design and peptide synthesis. Rather than using live or killed Brucella organisms, the system synthesizes precise copies of immunogenic epitope sequences that can be administered as safe subunit vaccines. These synthetic epitope copies replicate the protective immunogenicity without the safety concerns of biological organisms, particularly for human vaccination where safety is paramount.
Data Source
AI summary
This invention relates to the identification of peptide binding to ligands, and in particular to identification of epitopes expressed by microorganisms and by mammalian cells. The present invention provides polypeptides comprising the epitopes, and vaccines, antibodies and diagnostic products that utilize or are developed using the epitopes.