PMID- 23815611
OWN - NLM
STAT- MEDLINE
DCOM- 20131113
LR  - 20211021
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 14 Suppl 8
IP  - Suppl 8
DP  - 2013
TI  - Integrating peptides' sequence and energy of contact residues information 
      improves prediction of peptide and HLA-I binding with unknown alleles.
PG  - S1
LID - 10.1186/1471-2105-14-S8-S1 [doi]
AB  - BACKGROUND: The HLA (human leukocyte antigen) class I is a kind of molecule 
      encoded by a large family of genes and is characteristic of high polymorphism. 
      Now the number of the registered HLA-I molecules has exceeded 3000. Slight 
      differences in the amino acid sequences of HLAs would make them bind to different 
      sets of peptides. In the past decades, although many methods have been proposed 
      to predict the binding between peptides and HLA-I molecules and achieved good 
      performance, most experimental data used by them is limited to the HLAs with a 
      small number of alleles. Thus they are inclined to obtain high prediction 
      accuracy only for data with similar alleles. Because the peptides and HLAs 
      together determine the binding, it's necessary to consider their contribution 
      meanwhile. RESULTS: By taking into account the features of the peptides sequence 
      and the energy of contact residues, in this paper a method based on the 
      artificial neural network is proposed to predict the binding of peptides and 
      HLA-I even when the HLAs' potential alleles are unknown. Two experiments in the 
      allele-specific and super-type cases are performed respectively to validate our 
      method. In the first case, we collect 14 HLA-A and 14 HLA-B molecules on Bjoern 
      Peters dataset, and compare our method with the ARB, SMM, NetMHC and other 16 
      online methods. Our method gets the best average AUC (Area under the ROC) value 
      as 0.909. In the second one, we use leave one out cross validation on MHC-peptide 
      binding data that has different alleles but shares the common super-type. 
      Compared to gold standard methods like NetMHC and NetMHCpan, our method again 
      achieves the best average AUC value as 0.847. CONCLUSIONS: Our method achieves 
      satisfactory results. Whenever it's tested on the HLA-I with single definite gene 
      or with super-type gene locus, it gets better classification accuracy. 
      Especially, when the training set is small, our method still works better than 
      the other methods in the comparison. Therefore, we could make a conclusion that 
      by combining the peptides' information, HLAs amino acid residues' interaction 
      information and contact energy, our method really could improve prediction of the 
      peptide HLA-I binding even when there aren't the prior experimental dataset for 
      HLAs with various alleles.
FAU - Luo, Fei
AU  - Luo F
AD  - School of Computer, Wuhan University, Wuhan, Hubei, China.
FAU - Gao, Yangyang
AU  - Gao Y
FAU - Zhu, Yongqiong
AU  - Zhu Y
FAU - Liu, Juan
AU  - Liu J
LA  - eng
PT  - Journal Article
DEP - 20130509
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (MHC binding peptide)
RN  - 0 (Oligopeptides)
SB  - IM
MH  - Algorithms
MH  - Alleles
MH  - Area Under Curve
MH  - Energy Metabolism
MH  - HLA Antigens/chemistry/genetics/immunology
MH  - Histocompatibility Antigens Class I/chemistry/genetics/immunology/*metabolism
MH  - Humans
MH  - *Neural Networks, Computer
MH  - Oligopeptides/*metabolism
PMC - PMC3654895
EDAT- 2013/07/17 06:00
MHDA- 2013/11/14 06:00
PMCR- 2013/05/09
CRDT- 2013/07/03 06:00
PHST- 2013/07/03 06:00 [entrez]
PHST- 2013/07/17 06:00 [pubmed]
PHST- 2013/11/14 06:00 [medline]
PHST- 2013/05/09 00:00 [pmc-release]
AID - 1471-2105-14-S8-S1 [pii]
AID - 10.1186/1471-2105-14-S8-S1 [doi]
PST - ppublish
SO  - BMC Bioinformatics. 2013;14 Suppl 8(Suppl 8):S1. doi: 10.1186/1471-2105-14-S8-S1. 
      Epub 2013 May 9.