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Related Experiment Video

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Published on: January 26, 2024

Quantitative modeling of peptide binding to TAP using support vector machine.

Carmen M Diez-Rivero1, Bernardo Chenlo, Pilar Zuluaga

  • 1Laboratorio de Inmuno Medicina, Departamento de Microbiología I-Immunología, Facultad de Medicina, Universidad Complutense, Madrid, Spain.

Proteins
|August 26, 2009
PubMed
Summary
This summary is machine-generated.

Predicting peptide binding to the transporter associated with antigen processing (TAP) is crucial for identifying CD8 T cell epitopes. Support vector machine models accurately predict TAP affinity, with key contributions from C-terminal and P1/P2 residues.

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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
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Published on: March 25, 2014

Area of Science:

  • Immunology
  • Computational Biology
  • Biochemistry

Background:

  • Peptide transport via the transporter associated with antigen processing (TAP) is essential for CD8 T cell epitope presentation.
  • Understanding peptide-TAP interactions is key to predicting T cell responses.

Purpose of the Study:

  • To evaluate the predictive performance of support vector machine (SVM) models for TAP binding affinity.
  • To identify key peptide residues influencing TAP binding.

Main Methods:

  • Trained SVM models on a dataset of 613 nonamer peptides with known TAP affinity.
  • Utilized 10-fold cross-validation and Pearson's correlation coefficients (R(p)) to assess model performance.
  • Investigated the predictive power of individual peptide positions and residue combinations.

Main Results:

  • All peptide positions (P1-P9) contribute to TAP binding, with varying degrees of influence.
  • The C-terminal end, P1, and P2 residues showed the largest contributions to TAP binding.
  • Models trained on more residues, particularly the full-length sequence or specific N- and C-terminal combinations, achieved the highest predictive accuracy (R(p) = 0.89).

Conclusions:

  • SVM models can effectively predict peptide-TAP binding affinity.
  • Specific peptide residues, especially at the C-terminus and positions P1/P2, are critical determinants of TAP binding.
  • A publicly accessible tool is available for predicting TAP binding affinity.