10) Neural models for predicting viral vaccine targets.
Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V.
J Bioinform Comput Biol. 2005 Oct;3(5):1207-25.
PUBMED PMID: 16278955.
Out link: Full-text
Impact Factor Year 2009: NA
No. of Citations: 16 (total): 5 (non-self) & 11 (self)
ABSTRACT :
We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.
This article has been cited by other articles:
1) Srinivasan KN, Zhang GL, Khan AM, August JT, Brusic V. Prediction of class I T-cell epitopes: evidence of presence of immunological hot spots inside antigens. Bioinformatics. 2004 Aug 4;20 Suppl 1:i297-302. PMID: 15262812
2) Zhang GL, Srinivasan KN, Veeramani A, August JT, Brusic V. PREDBALB/c: a system for the prediction of peptide binding to H2d molecules, a haplotype of the BALB/c mouse. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W180-3. PMID: 15980450
3) Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W172-9. PMID: 15980449
4) Zhang, GL, August, JT, Kwo, CK, Brusic, V. Performance Evaluation of MULTIPRED1 on Prediction of MHC Class I Binders. Proceedings of the International Conference on Biomedical and Pharmaceutical Engineering, 2006. 2006 Page(s):307-313.
5) Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V. Extreme learning machine for predicting HLA-peptide binding. Proceedings of Advances in neural networks, pt 3. 2006; 3973:716-721.
6) Zhang, GL, Tong, JC, Zhang, ZH, August, JT, Zheng, Y, Kwoh, CK, Brusic, V. Computational Models for Identifying Promiscuous HLA-B7 Binders based on Information Theory and Support Vector Machine. International Conference on Biomedical and Pharmaceutical Engineering, 2006. Pages:319-323.
7) Zhang GL, Bozic I, Kwoh CK, August JT, Brusic V. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Methods. 2007 Mar 30;320(1-2):143-54. Epub 2007 Jan 25. PMID: 17303158
8) Zhang, GL, Khan, AM, Srinivasan, KN, Heiny, AT, Lee, KX, Kwoh, CK, August JT, Brusic, V. Hotspot Hunter: a computational system for large-scale screening and selection of candidate immunological hotspots in pathogen proteomes. BMC Bioinformatics 2008, 9(Suppl 1):S19
9) Nanni L, Lumini A. A genetic approach for building different alphabets for peptide and protein classification. BMC Bioinformatics. 2008 Jan 24;9:45. PMID: 18218100
10) Nanni, L., Lumini, A. Machine learning multi-classifiers for peptide classification. Neural Computing and Applications. 2009, 18 (2), pp. 185-192
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