Crosslink Identification Algorithm for Mass Spectrometry Data
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Solution Overview
Problem
Conventional techniques for analyzing mass spectrometry data to identify protein-protein interactions suffer from low efficiency and high rates of false positives.
Innovation Solution
A processing platform configured to implement a crosslink identification and validation algorithm across multiple levels of mass spectrometry data, utilizing header matching and mass validation filters, and machine learning to identify and validate protein-protein interactions with improved efficiency and reduced false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional techniques are used for analyzing mass spectrometry data to identify protein-protein interactions, then the analysis can be performed with simple methods, but the efficiency is low and false positive rates are high
Solution Approach 1:
The patent segments the crosslink identification process into multiple distinct filtering stages: header matching filter, mass validation filters, and confidence scoring. Each stage processes the data differently and eliminates potential false positives before the next stage, thereby improving both efficiency and reliability through systematic division of the analysis workflow
Solution Approach 2:
The patent applies preliminary filtering actions before full analysis by using header matching to quickly identify potential crosslinks based on spectral headers, and then applies mass validation filters as preliminary checks before generating confidence scores. This preliminary action eliminates obvious false positives early in the process, improving efficiency without compromising reliability
2Measurement precision
If multiple levels of mass spectrometry data are processed through rigorous validation, then the accuracy of protein-protein interaction mapping is improved, but the computational complexity increases
Solution Approach 1:
The patent divides the complex validation process into segmented filtering stages (header matching, mass validation, confidence scoring) that can be applied sequentially to multiple levels of mass spectrometry data. This segmentation makes the complex algorithm more manageable and implementable while maintaining high measurement precision through systematic validation at each stage
Solution Approach 2:
The patent processes mass spectrometry data across multiple dimensional levels (MS1, MS2, MS3 spectra) by applying filters that operate on different spectral dimensions simultaneously. This multi-dimensional approach improves validation accuracy by cross-referencing information across different spectral resolutions without requiring exponentially increased algorithmic complexity
Data Source
AI summary
A processing platform in one embodiment comprises one or more processing devices each including at least one processor coupled to a memory. The processing platform is configured to implement a crosslink identification and validation algorithm for processing multiple levels of mass spectrometry data in order to identify and validate protein-protein interactions within the mass spectrometry data. In conjunction with execution of the crosslink identification and validation algorithm, the processing platform is further configured to obtain mass spectrometry spectra for each of the multiple levels, to apply a header matching filter to identify at least one potential crosslink relating one or more first level spectra and one or more second level spectra utilizing a plurality of third level spectra, and to apply one or more mass validation filters to identify whether or not the potential crosslink is a valid crosslink.