TY - JOUR
T1 - Fuzzy neural network-based prediction of the motif for MHC class II binding peptides
AU - Noguchi, Hideki
AU - Hanai, Taizo
AU - Honda, Hiroyuki
AU - Harrison, Leonard C.
AU - Kobayashi, Takeshi
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2001
Y1 - 2001
N2 - Characterizing the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is critical for understanding immunity and developing immunotherapies for autoimmune diseases and cancer. To identify the peptide binding motif and predict peptides that bind to the human MHC classII molecule HLA-DR4(*0401), we applied a fuzzy neural network (FNN) capable of extracting the relationship between input and output. Analysis of the peptide binding motif revealed that the hydrophilicity of the position 1 residue located on the N-terminal side of the nonamer (9mer) was the most important variable and that the van der Waals volume and hydrophilicity of the position 6 residue and the hydrophilicity of the position 7 residue were also important variables. The estimation accuracy (AROC value) was high and the binding motif extracted from the FNN agreed with that derived experimentally. This study demonstrates that FNN modeling allows candidate antigenic peptides to be selected without the need for further experiments.
AB - Characterizing the interaction between major histocompatibility complex (MHC) molecules and antigenic peptides is critical for understanding immunity and developing immunotherapies for autoimmune diseases and cancer. To identify the peptide binding motif and predict peptides that bind to the human MHC classII molecule HLA-DR4(*0401), we applied a fuzzy neural network (FNN) capable of extracting the relationship between input and output. Analysis of the peptide binding motif revealed that the hydrophilicity of the position 1 residue located on the N-terminal side of the nonamer (9mer) was the most important variable and that the van der Waals volume and hydrophilicity of the position 6 residue and the hydrophilicity of the position 7 residue were also important variables. The estimation accuracy (AROC value) was high and the binding motif extracted from the FNN agreed with that derived experimentally. This study demonstrates that FNN modeling allows candidate antigenic peptides to be selected without the need for further experiments.
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U2 - 10.1016/S1389-1723(01)80254-1
DO - 10.1016/S1389-1723(01)80254-1
M3 - Article
C2 - 16233088
AN - SCOPUS:0034776133
VL - 92
SP - 227
EP - 231
JO - Journal of Bioscience and Bioengineering
JF - Journal of Bioscience and Bioengineering
SN - 1389-1723
IS - 3
ER -