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Methods Mol Biol. 2016;1404:753-60. doi: 10.1007/978-1-4939-3389-1_49.

MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.

Xu Y1,2, Luo C1,2, Mamitsuka H3, Zhu S4,5.

Author information

1
School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China.
2
Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, 200433, China.
3
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, 611-0011, Japan.
4
School of Computer Science, Fudan University, 220 Handan Road, Shanghai, 200433, China. zhusf@fudan.edu.cn.
5
Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, 200433, China. zhusf@fudan.edu.cn.

Abstract

Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info .

KEYWORDS:

Binding peptides; MHC-I; MHC-II; MHC2MIL; MHC2SKpan; MetaMHCpan; TEPITOPEpan

PMID:
27076335
DOI:
10.1007/978-1-4939-3389-1_49
[Indexed for MEDLINE]

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