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Tumor Necrosis Factor (TNF), a proinflammatory cytokine, contributes significantly to the inflammation seen in Crohn's disease. It exists as soluble TNF and membrane-bound TNF, with actions mediated through TNF receptors (TNFR). TNFR activation leads to the release of proinflammatory cytokines, T-cell activation, collagen production, and leukocyte migration, all contributing to inflammation in Crohn's disease. Anti-TNF monoclonal antibodies, namely infliximab (Remicade), adalimumab (Humira),...
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Related Experiment Video

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Determination of the Relative Potency of an Anti-TNF Monoclonal Antibody (mAb) by Neutralizing TNF Using an In Vitro Bioanalytical Method
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An improved understanding of TNFL/TNFR interactions using structure-based classifications.

Cedrik Magis1, Almer M van der Sloot, Luis Serrano

  • 1Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain.

Trends in Biochemical Sciences
|July 14, 2012
PubMed
Summary
This summary is machine-generated.

Tumor Necrosis Factor Ligand (TNFL)-Tumor Necrosis Factor Receptor (TNFR) interactions are key to cellular processes. A new structure-based method, T-RMSD, reclassifies TNFLs and TNFRs, revealing co-evolutionary links.

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Area of Science:

  • Molecular biology
  • Structural biology
  • Evolutionary biology

Background:

  • Tumor Necrosis Factor Ligand (TNFL) and Tumor Necrosis Factor Receptor (TNFR) interactions regulate critical cellular functions.
  • The molecular mechanisms governing the specificity of TNFL-TNFR interactions are not well understood.
  • Existing classification schemes for TNFLs and TNFRs do not fully align with their structural and functional characteristics.

Purpose of the Study:

  • To re-analyze existing structural data of TNFL-TNFR interactions using a novel structure-based sequence clustering method.
  • To develop an improved classification system for TNFLs and TNFRs based on structural and functional features.
  • To investigate the evolutionary history of TNFL-TNFR interactions.

Main Methods:

  • Development and application of the T-RMSD (tree based on root mean square deviation) method, a novel structure-based sequence clustering approach.
  • Re-analysis of available structural data for TNFLs and TNFRs.
  • Comparative analysis of existing and newly proposed classification schemes.

Main Results:

  • The T-RMSD method provides an improved classification of TNFLs and TNFRs that better reflects their structural and functional properties.
  • The new classification aligns more closely with known biological functions than previous schemes.
  • Evidence for convergent evolution in TNFLs and TNFRs was identified.

Conclusions:

  • The T-RMSD method offers a more accurate way to classify TNFLs and TNFRs based on structural data.
  • The findings suggest a co-evolutionary relationship between TNFLs and the third cysteine-rich domain (CRD) of large TNFRs.
  • This study provides new insights into the evolutionary processes shaping TNFL-TNFR interactions.