Peptide PTM Identification Using MILP Without Combinatorial Search
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Solution Overview
Problem
Existing methods struggle to efficiently identify peptides with multiple post-translational modifications (PTMs) due to the exponential growth of database sizes and unsatisfactory performance in characterizing PTM patterns.
Innovation Solution
A method utilizing a mixed integer linear programming (MILP) model to optimize the identification of peptides with multiple PTMs, employing a fuzzy and bidirectional matching approach with a FM-indexed protein database and a weighted directed graph to segment protein candidates into sections, maximizing matched spectral peak intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing database search methods are used to identify peptides with multiple PTMs, then database coverage increases, but computational complexity and time consumption increase exponentially
Solution Approach 1:
The patent segments the peptide identification process into distinct phases: spectral peak picking, tag extraction, candidate retrieval using FM-index, and MILP optimization. This segmentation allows each step to be optimized independently, reducing overall computational complexity while maintaining comprehensive database coverage
Solution Approach 2:
The patent introduces an FM-indexed protein database as an intermediary structure between the spectral data and the final peptide identification. This intermediary enables efficient candidate retrieval by transforming the search space from exponential to linear complexity, thereby reducing computational burden while maintaining comprehensive coverage
2Reliability
If existing methods enumerate all possible PTM combinations to identify peptides with multiple PTMs, then identification completeness improves, but processing time increases exponentially
Solution Approach 1:
The patent performs preliminary actions by pre-processing spectral data to extract tags and pre-building an FM-indexed protein database before the actual peptide identification. This preliminary processing organizes the data structure to enable faster querying and reduces the time required for the main identification process, allowing comprehensive PTM enumeration without exponential time penalty
Solution Approach 2:
The patent changes the optimization parameter from exhaustive enumeration to MILP-based optimization with an objective function that maximizes matched spectral peak intensity. This parameter change transforms the problem from exponential time complexity to polynomial time complexity while maintaining identification completeness through the constrained optimization framework
3Productivity
If short amino acid sequence tags are extracted from experimental peaks to characterize PTMs, then measurement speed increases, but measurement precision decreases for peptides with multiple PTMs
Solution Approach 1:
The patent merges multiple short tags extracted from spectral peaks with candidate peptide sequences retrieved from the FM-indexed database. By combining tag-based rapid filtering with sequence-based precise matching and MILP optimization, the system achieves both high measurement speed and high PTM characterization precision for peptides with multiple PTMs
Data Source
AI summary
Identifying peptides with multiple post-translational modifications (PTMs) by tandem mass spectrometry (MS2) is computationally challenging since it involves finding the optimal PTM pattern that produces theoretical spectra most closely resembling experimental spectra. To address this issue, a mixed integer linear programming (MILP) model is used to find an optimal solution to peptide identification and PTM characterization. The optimal solution is integrated into a tool named as PIPI3. PIPI3 identifies the optimal PTM pattern without enumerating all possible PTM combinations. On simulation datasets with up to four PTMs per peptide, PIPI3 correctly identified over 99% of the spectra and characterized the PTM patterns with a precision of 85%, while the numbers of the best competitor MODplus are 92% and 76%, highlighting PIPI3's advantage in handling peptides with multiple PTMs compared to state-of-the-art techniques.