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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Related Experiment Video

Updated: Aug 21, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Advances, obstacles, and opportunities for machine learning in proteomics.

Heather Desaire¹, Eden P Go¹, David Hua¹

¹Department of Chemistry, University of Kansas, Lawrence, KS 66045, USA.

Cell Reports. Physical Science

|November 16, 2022

Summary

This review highlights the growing intersection of proteomics and machine learning (ML). It encourages proteomics researchers to adopt ML techniques for advancing tool development and biomarker discovery, despite current low integration rates.

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Area of Science:

Proteomics
Machine Learning
Bioinformatics

Background:

Proteomics and machine learning (ML) are major research fields with high publication rates.
Integration of ML in proteomics research is currently limited, with only ~2% of papers utilizing ML.
A significant gap exists in the application of ML within the proteomics domain.

Purpose of the Study:

To review the intersection of proteomics and machine learning.
To inspire proteomics researchers to develop ML skills.
To highlight advances and opportunities at the confluence of these fields.

Main Methods:

Literature review focusing on studies combining proteomics and ML.
Tutorial introduction to machine learning concepts for a proteomics audience.
Identification and enumeration of knowledge gaps and future research directions.

Main Results:

Few studies currently combine proteomics and ML, indicating a nascent research area.
ML applications show promise in proteomics tool development and biomarker discovery.
Specific areas for advancement and knowledge acquisition are identified.

Conclusions:

There is substantial untapped potential for ML in proteomics research.
Further integration of ML can accelerate discoveries in proteomics, particularly for biomarker identification.
Education and skill development in ML are crucial for proteomics researchers to leverage these advancements.