Convolutional Neural Network for Mass Spectral Data Analysis
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Solution Overview
Problem
Current mass spectrometry methods are limited in identifying proteins with unknown mutations, post-translational modifications, and chemical variations, as they rely on canonical database sequences and are unable to efficiently search for and match non-canonical data, resulting in a low matching rate of around 20% in proteomics datasets.
Innovation Solution
The use of convolutional neural networks (CNNs) for deep learning approaches to analyze mass spectral data, allowing for the identification and characterization of proteins, peptides, and other small molecules by discretizing spectra into weighted vectors and employing feature learning and classification to predict amino acid sequences and recognize the presence or absence of specific amino acids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional database matching methods are used for protein identification, then the search process is straightforward and computationally manageable, but the matching rate is low (around 20%) and mutations/PTMs cannot be detected
Solution Approach 1:
The patent replaces conventional database search engines with deep learning neural networks that automatically learn spectral features and protein sequences from training data, eliminating the need for manual database querying and enabling detection of mutations and PTMs through pattern recognition rather than exact sequence matching
Solution Approach 2:
The patent transforms the approach by changing from exact sequence matching to probabilistic pattern recognition, where neural networks learn to identify proteins based on spectral patterns rather than requiring precise database sequence matches, thereby detecting previously invisible variations
2Adaptability or versatility
If the search space is expanded to include all possible mutations and PTMs, then the ability to detect variants is improved, but the computational complexity becomes too large to search efficiently
Solution Approach 1:
The patent performs preliminary action by pre-training neural networks on comprehensive datasets that include various mutations and PTMs, so that the model learns to recognize these variants during training rather than requiring exhaustive search during actual analysis, reducing computational complexity at query time
Solution Approach 2:
The patent uses training datasets that copy and include representative examples of various mutations and PTMs, allowing the neural network to learn from these copies without needing to enumerate all possible variants in the actual search process
Data Source
AI summary
Methods are provided to classify and identify features in mass spectral data using neural network algorithms. A convolutional neural network (CNN) was trained to identify amino acids from an unknown protein sample. The CNN was trained using known peptide sequences to predict amino acid presence, diversity, and frequency, peptide length, subsequences of amino acids classified by features include aliphatic/aromatic, hydrophobic/hydrophilic, positive/negative charge, and combinations thereof. Mass spectra data of a sample unknown to the trained CNN was discretized into a one-dimensional vector and input into the CNN. The CNN models can potentially be integrated to determine the complete peptide sequence from a spectrum, thereby improving the yield of identifiable protein sequences from mass spec analysis.