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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Published on: December 15, 2023

Deep learning neural network tools for proteomics.

Jesse G Meyer¹

¹Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Cell Reports Methods

|April 27, 2022

Summary

This summary is machine-generated.

Deep learning is accelerating shotgun proteomics by accurately predicting peptide properties and improving workflows. These artificial neural networks enhance feature selection, peptide identification, and protein inference for quantitative proteomic analysis.

Keywords:

MS/MS bioinformatics deep learning mass spectrometry neural networks peptides proteomics retention time

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Mass-spectrometry-based proteomics offers quantitative analysis of numerous human proteins.
Significant experimental and computational hurdles impede advancements in proteomics.
Machine learning, particularly deep learning, presents novel solutions to these challenges.

Purpose of the Study:

To review the impact of deep learning strategies on shotgun proteomics.
To highlight how deep learning overcomes existing limitations in the field.
To showcase the broad applicability of deep learning across the proteomics workflow.

Main Methods:

Application of artificial deep neural networks for peptide property prediction.
Utilizing deep learning for tandem mass spectra and retention time prediction from peptide sequences.
Integration of deep learning models into various stages of the proteomics workflow.

Main Results:

Deep learning accurately predicts crucial peptide physicochemical properties.
Accurate prediction of tandem mass spectra and retention times is achieved.
Demonstrated improvements in feature selection, peptide identification, and protein inference.

Conclusions:

Deep learning significantly accelerates progress in shotgun proteomics.
These methods enhance the accuracy and efficiency of quantitative proteomic analyses.
Deep learning is becoming integral to modern proteomics workflows, addressing key challenges.