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Proteomics

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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...

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Related Experiment Video

Updated: Dec 7, 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

Protein profiles: Biases and protocols.

Gregor Urban¹, Mirko Torrisi², Christophe N Magnan¹

¹Department of Computer Science & Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA.

Computational and Structural Biotechnology Journal

|September 30, 2020

Summary

This summary is machine-generated.

Evolutionary profiles improve protein structure prediction accuracy. However, standard evaluation methods can inflate performance due to profile similarity, leading to biased comparisons. A new protocol, EVALpro, offers fairer, more reliable assessments.

Keywords:

Evaluation protocols Evolutionary information Profile-based prediction

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Last Updated: Dec 7, 2025

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

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Evolutionary profiles have been integral to protein secondary structure prediction since the 1990s, enhancing accuracy over sequence-based methods.
Despite their utility, the precise role of evolutionary profiles is not fully understood, especially since proteins do not utilize them during in vivo folding.
Existing evaluation protocols, developed for sequence-based predictors, may introduce biases when applied to profile-based predictors.

Purpose of the Study:

To critically evaluate the impact of evolutionary profiles on protein secondary structure prediction accuracy.
To investigate potential biases in current evaluation methodologies for profile-based predictors.
To introduce and validate a new protocol for more accurate and fair assessment of these predictors.

Main Methods:

Analysis of profile similarity between training and test datasets using standard sequence-based redundancy reduction protocols.
Evaluation of the contribution of profile similarity to the accuracy gains of profile-based predictors.
Development and implementation of the EVALpro protocol for assessing predictor accuracy as a function of profile similarity.

Main Results:

Standard protocols reveal high profile similarity between training and test proteins, significantly influencing predictor accuracy.
Accuracy improvements of profile-based predictors are strongly correlated with this observed profile similarity.
Existing evaluation metrics provide a biased estimation of true predictor performance and hinder fair inter-predictor comparisons.

Conclusions:

The perceived accuracy of profile-based predictors is inflated by inherent profile similarity in standard evaluation sets.
The EVALpro protocol mitigates bias by evaluating predictors based on profile similarity, enabling equitable comparisons.
EVALpro reduces the influence of similarity cutoffs on test set selection, leading to more robust performance estimations.