DIA Peptide Search Using Calibrated Spectrum-Centric Libraries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing library-free workflows for Data Independent Acquisition (DIA) in mass spectrometry suffer from poor reproducibility, high computational demand, and limited depth of proteome coverage due to the use of unspecific in-silico predicted spectral libraries, which lack empirical calibration and result in suboptimal peptide identification.
Innovation Solution
A novel workflow that combines spectrum-centric analysis with in-silico predicted libraries, utilizing empirical data to calibrate and refine prediction models, creating a curated library through iterative filtering and calibration steps to improve peptide identification accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If in-silico predicted spectral libraries are used for library-free DIA analysis, then additional empirical measurements are eliminated, but the libraries become unspecific and peptide identification accuracy deteriorates
Solution Approach 1:
The patent performs spectrum-centric analysis as a preliminary step before peptide-centric analysis. This preliminary action generates empirical spectral data that is then used to calibrate and refine the in-silico predicted spectral library, thereby improving the accuracy of subsequent peptide identification without requiring separate empirical measurement experiments
Solution Approach 2:
The patent implements a feedback mechanism where the results of spectrum-centric analysis are fed back into the predicted spectral library to optimize it. The empirical data from spectrum-centric analysis serves as feedback to refine the in-silico predictions, creating a self-improving system that enhances peptide identification accuracy while maintaining the library-free approach
2Device complexity
If in-silico predicted spectral libraries are used, then workflow is simplified, but computational demand increases due to large library size
Solution Approach 1:
The patent extracts and utilizes only the relevant empirical spectral information from the spectrum-centric analysis results to refine the predicted library. By taking out and using only the necessary empirical data for calibration, the method reduces the computational burden of processing entire large-scale predicted libraries while maintaining workflow simplicity
3Speed
If traditional peptide-centric analysis is used without empirical calibration, then analysis speed is maintained, but sensitivity and depth of proteome coverage are limited
Solution Approach 1:
The patent performs spectrum-centric analysis as a preliminary step that generates empirical calibration data. This preliminary action does not significantly increase overall analysis time but substantially improves the sensitivity and depth of proteome coverage by providing empirically calibrated parameters for the subsequent peptide-centric analysis
Solution Approach 2:
The patent changes key parameters of the predicted spectral library based on empirical data from spectrum-centric analysis. By adjusting parameters such as mass-to-charge ratios, retention times, and spectral intensities according to empirical observations, the method enhances sensitivity and proteome coverage while maintaining efficient analysis speed
Data Source
AI summary
A method for performing library-free search analysis including performing a search using a spectrum-centric approach for a data; performing at least one of improving peptide centric analysis of a predicted spectral library by using the results of the spectrum centric search for creating a sub-selection of precursors, including a calibration by using results from the spectrum-centric approach; creating an optimized predicted library by refining static prediction models; using the calibration and/or the optimized predicted library to initiate a peptide-centric search for the data based on an in-silico library; creating a curated library by combining the results of the spectrum-centric approach with the results from the peptide-centric approach; and analyzing the results of the curated library using a second peptide-centric search of the data.