Protein Identification via Mass Spectrum Intensity Pattern Matching
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Solution Overview
Problem
Current peptide mass fingerprinting methods struggle to reliably distinguish highly similar biopharmaceutical molecules, such as monoclonal antibodies, due to limitations in mass spectrometry analysis, requiring additional techniques like LC-MS or ELISAs for specific identification.
Innovation Solution
A method that compares the similarity of mass spectrum intensity patterns between a target protein and reference proteins using cosine similarity or cross-correlation, accounting for variations in intensity patterns, and employing rapid denaturing and digestion techniques, followed by MALDI-TOF mass spectrometry for identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional peptide mass fingerprinting methods are used, then protein identification can be performed, but highly similar biopharmaceutical molecules cannot be reliably distinguished
Solution Approach 1:
The patent segments the mass spectrum data by focusing on specific diagnostic peptide regions rather than analyzing the entire spectrum. This segmentation allows for targeted comparison of characteristic peptide patterns that differentiate highly similar molecules, improving both reliability and precision in identification.
Solution Approach 2:
The patent changes the analytical parameters by using intensity pattern matching and cosine similarity calculations instead of traditional mass matching alone. This parameter transformation enables differentiation of molecules with identical or near-identical masses by comparing the relative intensity patterns of their peptide fragments.
2Measurement precision
If additional techniques like LC-MS or ELISAs are used for specific identification, then molecular differentiation precision improves, but analysis time and costs increase
Solution Approach 1:
The patent merges the advantages of mass spectrometry with pattern recognition algorithms to achieve differentiation precision previously requiring multiple techniques. By combining MS data with cosine similarity analysis of intensity patterns, the method achieves high precision in a single step, eliminating the need for sequential LC-MS or ELISA procedures.
Solution Approach 2:
The patent replaces complex mechanical separation systems (LC-MS) and antibody-based assays (ELISA) with a computational approach using cosine similarity analysis. This substitution maintains high differentiation precision while dramatically reducing analysis time and operational complexity.
3Ease of operation
If traditional peptide mass fingerprinting is used, then analysis can be performed, but it requires pure protein or very simple mixtures
Solution Approach 1:
The patent extracts and focuses on diagnostic peptide intensity patterns from complex mixtures, isolating the relevant identification information from interfering background signals. This extraction approach allows analysis of complex samples without requiring extensive purification, as the method identifies target proteins through their characteristic peptide patterns regardless of mixture complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables fast and reliable identification of target proteins with high specificity, reducing analysis time and costs, and effectively differentiating between highly similar molecules by utilizing reduced mass spectra and multivariate statistical methods.
Implementation Method 1
The resulting mixture of peptides is analyzed by mass spectrometry
Implementation Method 2
The protein is digested with an enzyme of high specificity; usually trypsin
Data Source
AI summary
The invention provides methods for characterizing a target protein wherein a mass spectrum of digest peptides of the target protein is acquired and compared with measured reference mass spectra of digest peptides of reference proteins or of proteins of reference host cells. The comparison comprises determining similarity scores of the intensity patterns of the mass spectrum and the reference mass spectra. The characterization comprises assigning the target protein to a reference protein having a reference mass spectrum with a similarity score above a predetermined threshold.