BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity

Lian Wang; Danling Pan; Xihao Hu; Jinyu Xiao; Yangyang Gao; Huifang Zhang; Yan Zhang; Juan Liu; Shanfeng Zhu

doi:10.1016/S1673-8527(08)60117-4

Volume 36 Issue 5

May 2009

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Article Navigation > Journal of Genetics and Genomics > 2009 > 36(5): 289-296

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BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity

doi: 10.1016/S1673-8527(08)60117-4

Lian Wang^{a, #},
Danling Pan^{a, #},
Xihao Hu^{a, #},
Jinyu Xiao^a,
Yangyang Gao^a,
Huifang Zhang^a,
Yan Zhang^a,
Juan Liu^a,
Shanfeng Zhu^{b, c}

a
School of Computer, Wuhan University, Wuhan 430079, China
b
School of Computer Science, Fudan University, Shanghai 200433, China
c
Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, 200433, China

More Information

Corresponding author: E-mail address: liujuan@whu.edu.cn (Juan Liu); E-mail address: zhusf@fudan.edu.cn (Shanfeng Zhu)
Received Date: 2008-12-01
Accepted Date: 2009-01-21
Rev Recd Date: 2009-01-08

Available Online: 2009-05-15

Publish Date: 2009-05-20

Abstract

Abstract

Effective identification of major histocompatibility complex (MHC) molecules restricted peptides is a critical step in discovering immune epitopes. Although many online servers have been built to predict class II MHC-peptide binding affinity, they have been trained on different datasets, and thus fail in providing a unified comparison of various methods. In this paper, we present our implementation of seven popular predictive methods, namely SMM-align, ARB, SVR-pairwise, Gibbs sampler, ProPred, LP-top2, and MHCPred, on a single web server named BiodMHC (http://biod.whu.edu.cn/BiodMHC/index.html, the software is available upon request). Using a standard measure of AUC (Area Under the receiver operating characteristic Curves), we compare these methods by means of not only cross validation but also prediction on independent test datasets. We find that SMM-align, ProPred, SVR-pairwise, ARB, and Gibbs sampler are the five best-performing methods. For the binding affinity prediction of class II MHC-peptide, BiodMHC provides a convenient online platform for researchers to obtain binding information simultaneously using various methods.
- MHC II,
- MHC-peptide binding predictions,
- web server

These authors contributed equally to this work.

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References(20)

References

[1]	Bui, H.H., Sidney, J., Peters, B. et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications Immunogenetics, 57 (2005),pp. 304-314
[2]	Carson, R.T., Vignali, K.M., Woodland, D.L. et al. T cell receptor recognition of MHC class II-bound peptide flanking residues enhances immunogenicity and results in altered TCR V region usage Immunity, 7 (1997),pp. 387-399
[3]	Doytchinova, I.A., Flower, D.R. Towards the in silico identification of class II restricted T-cell epitopes: A partial least squares iterative self-consistent algorithm for affinity prediction Bioinformatics, 19 (2003),pp. 2263-2270
[4]	Duda, R.O., Hart, P.E., Stork, D.G.
[5]	Guan, P., Doytchinova, I.A., Zygouri, C. et al. MHCPred: A server for quantitative prediction of peptide-MHC binding Nucleic Acids Res., 31 (2003),pp. 3621-3624
[6]	Henseler, M., Kaplan, A.M. A beginner's guide to partial least square analysis Understanding Statistics, 3 (2004),pp. 283-297
[7]	Liao, L., Noble, W.S. Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships J. Comput. Biol., 10 (2003),pp. 857-868
[8]	Murugan, N., Dai, Y. Prediction of MHC class II binding peptides based on an iterative learning model Immunome Res., 1 (2005),p. 6
[9]	Nielsen, M., Lundegaard, C., Lund, O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method BMC Bioinformatics, 8 (2007),p. 238
[10]	Nielsen, M., Lundegaard, C., Worning, P. et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach Bioinformatics, 20 (2004),pp. 1388-1397
[11]	Parham, P.
[12]	Peters, B., Sette, A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method BMC Bioinformatics, 6 (2005),p. 132
[13]	Peters, B., Bui, H.H., Frankild, S. et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules PLoS Comput. Biol., 2 (2006),p. e65
[14]	Polikar, R. Ensemble based systems in decision making IEEE Circuits and Systems Magazine, 6 (2006),pp. 21-45
[15]	Sturniolo, T., Bono, E., Ding, J. et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices Nat. Biotechnol., 17 (1999),pp. 555-561
[16]	Swets, J.A. Measuring the accuracy of diagnostic systems Science, 240 (1988),pp. 1285-1293
[17]	Tong, J.C., Zhang, G.L., Tan, T.W. et al. Prediction of HLA-DQ3.2beta ligands: Evidence of multiple registers in class II binding peptides Bioinformatics, 22 (2006),pp. 1232-1238
[18]	Wang, P., Sidney, J., Dow, C. et al. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach PLoS Comput. Biol., 4 (2008),p. e1000048
[19]	Yewdell, J.W., Bennink, J.R. Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses Annu. Rev. Immunol., 17 (1999),pp. 51-88
[20]	Zhu, S., Udaka, K., Sidney, J. et al. Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules Bioinformatics, 22 (2006),pp. 1648-1655