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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.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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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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Related Experiment Video

Updated: Feb 28, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity.

Radwa El-Awadi1, Oscar D Gomez1, Daniel Castillo-Secilla2

  • 1Department of Computer Engineering, Automation and Robotics, University of Granada, 18071 Granada, Spain.

Biomedicines
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

INPROF is a new web server that analyzes protein sequences using multiple sequence alignment (MSA) to extract 46 features. This tool accurately predicts disease severity, outperforming traditional methods using expression data.

Keywords:
COVID-19SARS-CoV-2classificationfeature extractionmultiple sequence alignment (MSA)protein interrelationproteinsweb server

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein sequence comparison is crucial for biomedicine, phylogeny, and evolution.
  • Open-access databases have expanded the availability of biological data.
  • Multiple sequence alignment (MSA) remains a key method for analyzing conserved protein regions.

Purpose of the Study:

  • To introduce INPROF, a novel web server for centralizing and automating the computation of protein features.
  • To enable deep analysis of protein relationships and conserved information via MSA.
  • To provide a comprehensive feature catalogue for AI-driven protein modeling.

Main Methods:

  • Developed INPROF, a web server computing up to 46 distinct metrics from protein sequences.
  • Utilized MSA to compare protein sequences and identify conserved elements.
  • Integrated features across multiple proteomic levels: sequences, structures, domains, and ontological terms.

Main Results:

  • Validated INPROF by predicting disease severity in COVID-19 patient RNA-Seq datasets.
  • Extracted INPROF features from canonical proteins linked to differentially expressed genes.
  • Demonstrated INPROF's superior classification performance compared to traditional expression data methods.

Conclusions:

  • INPROF is a versatile platform for computing 46 quantitative metrics of protein interactions.
  • These metrics offer biologically significant characteristics for downstream predictive modeling.
  • The INPROF web server facilitates advanced protein analysis and AI applications.