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Related Concept Videos

Proteomics01:33

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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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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The technique...
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Protein Networks02:26

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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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Protein Families02:47

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Proteins: From Genes to Degradation02:11

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Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
Transcription is the synthesis of RNA...
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Conservation of Protein Domains Over Different Proteins02:26

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Updated: Jun 22, 2025

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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Decoding functional proteome information in model organisms using protein language models.

Israel Barrios-Núñez1, Gemma I Martínez-Redondo2, Patricia Medina-Burgos1

  • 1Computational Biology and Bioinformatics Group, Andalusian Center for Developmental Biology (CABD-CSIC), 41013 Sevilla, Spain.

NAR Genomics and Bioinformatics
|July 4, 2024
PubMed
Summary
This summary is machine-generated.

Protein language models outperform deep learning methods in extracting functional information from entire proteomes. These models offer a precise and informative approach for large-scale biological data analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Protein language models (PLMs) show promise on curated datasets but lack application to full proteomes.
  • Evaluating machine learning (ML) methods for functional genomics is crucial.

Purpose of the Study:

  • To assess the performance of PLMs versus deep learning (DL) methods for functional information extraction from model organism proteomes.
  • To determine the suitability of PLMs for large-scale proteomic annotation and analysis.

Main Methods:

  • Comparative analysis of two ML-based methods (PLMs and DL) on full proteomes.
  • Evaluation across three gene ontologies (GO) and transcriptomic data.
  • Testing on selected model organisms.

Main Results:

  • PLMs demonstrated superior precision and informativeness compared to DL methods across all tested species and GOs.
  • PLMs more effectively recovered functional information from transcriptomic experiments.
  • The study identified PLMs as a robust tool for proteome-wide functional annotation.

Conclusions:

  • Protein language models are highly effective for decoding functional information from complete proteomes.
  • PLMs offer a precise and scalable solution for large-scale proteomic annotation and downstream analyses.
  • A recommended guide for utilizing PLMs in biological research is provided.