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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...

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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Related Experiment Video

Updated: Dec 5, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Artificial intelligence and machine learning for protein toxicity prediction using proteomics data.

Shubham Vishnoi¹, Himani Matre², Prabha Garg³

¹Department of Physics, Bernal Institute, University of Limerick, Limerick, Ireland.

Chemical Biology & Drug Design

|October 15, 2020

Summary

This summary is machine-generated.

Researchers are using artificial intelligence and machine learning to predict protein toxicity. This approach analyzes proteomics data to develop safer peptide-based therapies for various diseases.

Keywords:

machine learning algorithms peptide-based therapeutics predictive analysis protein toxicity prediction toxicoproteomics science

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Published on: January 26, 2024

Area of Science:

Biotechnology and Bioinformatics
Computational Biology
Drug Discovery

Background:

Peptide-based therapies are gaining traction for treating diverse diseases, moving beyond targeted drug delivery.
Designing effective peptide therapeutics relies on understanding and anchoring receptor properties.
Peptides offer distinct physicochemical advantages over small molecules for disease treatment.

Purpose of the Study:

To review the application of artificial intelligence (AI) and machine learning (ML) in predicting protein toxicity.
To highlight the role of proteomics data in developing these predictive models.
To explore how analyzing protein toxicity mechanisms can inform therapeutic insights.

Main Methods:

Utilizing in silico models and computational approaches for protein analysis.
Applying machine learning and artificial intelligence algorithms.
Leveraging toxicoproteomic data for model development and validation.

Main Results:

Numerous existing models aim for high-performance protein toxicity prediction.
AI and ML approaches are crucial for analyzing protein properties and structural alerts.
The review synthesizes current contributions of AI/ML in protein toxicity prediction using proteomics data.

Conclusions:

AI and ML are pivotal in advancing the prediction of protein toxicity.
Understanding protein toxicity mechanisms through computational analysis offers therapeutic potential.
Further research into toxicoproteomic data utilization is needed for robust ML model development.