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Related Concept Videos

Oligosaccharide Assembly01:24

Oligosaccharide Assembly

Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
Multiple sugar molecules that may or may...

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Exploring Protein-Glycan Interactions: Advances in Nuclear Magnetic Resonance
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Identification of mannose interacting residues using local composition.

Sandhya Agarwal1, Nitish Kumar Mishra, Harinder Singh

  • 1Institute of Microbial Technology, Chandigarh, India.

Plos One
|September 21, 2011
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Summary

Identifying mannose interacting residues (MIRs) is crucial for understanding pathogen recognition by mannose binding proteins (MBPs). This study developed machine learning models to accurately predict MIRs, aiding in protein function annotation and immune system research.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Immunology

Background:

  • Mannose binding proteins (MBPs) are key in the immune system, recognizing pathogens by binding to mannose on their surfaces.
  • Identifying mannose interacting residues (MIRs) is essential for understanding this pathogen recognition mechanism.

Purpose of the Study:

  • To develop computational modules for predicting mannose interacting residues (MIRs) in proteins.
  • To enhance understanding of protein-carbohydrate interactions and their role in immunity.

Main Methods:

  • Support Vector Machine (SVM) models were trained on datasets of mannose binding protein chains.
  • Models utilized binary, PSSM, and compositional profiles of residue patterns.
  • Performance was evaluated using Matthews Correlation Coefficient (MCC) and accuracy.

Main Results:

  • SVM models using compositional profiles achieved high performance, with MCC around 0.74 and 86.64% accuracy on the main dataset.
  • A model developed on a realistic dataset yielded an MCC of 0.62 and 93.08% accuracy.
  • A web server and standalone program were created for MIR prediction.

Conclusions:

  • Compositional analysis reveals specific residue preferences for mannose interaction.
  • The developed prediction strategy shows potential for identifying other types of interacting residues.
  • This work aids in protein function annotation and understanding mannose's role in the immune system.