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Literature DB >> 12651731

MHCBN: a comprehensive database of MHC binding and non-binding peptides.

Manoj Bhasin¹, Harpreet Singh, G P S Raghava.

Abstract

MHCBN is a comprehensive database of Major Histocompatibility Complex (MHC) binding and non-binding peptides compiled from published literature and existing databases. The latest version of the database has 19 777 entries including 17 129 MHC binders and 2648 MHC non-binders for more than 400 MHC molecules. The database has sequence and structure data of (a) source proteins of peptides and (b) MHC molecules. MHCBN has a number of web tools that include: (i) mapping of peptide on query sequence; (ii) search on any field; (iii) creation of data sets; and (iv) online data submission. The database also provides hypertext links to major databases like SWISS-PROT, PDB, IMGT/HLA-DB, GenBank and PUBMED.

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Year: 2003 PMID： 12651731 DOI： 10.1093/bioinformatics/btg055

Source DB: PubMed Journal: Bioinformatics ISSN： 1367-4803 Impact factor: 6.937

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Cited

67 in total

Review 1. MHC class II epitope predictive algorithms.

Authors: Morten Nielsen; Ole Lund; Søren Buus; Claus Lundegaard
Journal: Immunology Date: 2010-04-12 Impact factor: 7.397

2. A modular concept of HLA for comprehensive peptide binding prediction.

Authors: David S DeLuca; Barbara Khattab; Rainer Blasczyk
Journal: Immunogenetics Date: 2006-11-22 Impact factor: 2.846

3. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.

Authors: Manoj Bhasin; G P S Raghava
Journal: J Biosci Date: 2007-01 Impact factor: 1.826

4. A comprehensive analysis of the thermodynamic events involved in ligand-receptor binding using CoRIA and its variants.

Authors: Jitender Verma; Vijay M Khedkar; Arati S Prabhu; Santosh A Khedkar; Alpeshkumar K Malde; Evans C Coutinho
Journal: J Comput Aided Mol Des Date: 2008-01-25 Impact factor: 3.686

MHCBN: a comprehensive database of MHC binding and non-binding peptides.

Review 1. MHC class II epitope predictive algorithms.

2. A modular concept of HLA for comprehensive peptide binding prediction.

3. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.

4. A comprehensive analysis of the thermodynamic events involved in ligand-receptor binding using CoRIA and its variants.

5. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods.

6. Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data.

7. Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network.

8. Human self-protein CD8+ T-cell epitopes are both positively and negatively selected.

9. Analysis and prediction of affinity of TAP binding peptides using cascade SVM.

10. Prediction of HLA-DQ8beta cell peptidome using a computational program and its relationship to autoreactive T cells.