Artificial neural network study on organ-targeting peptides.
We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.
Jung
Eunkyoung
E
Insilicotech Co. Ltd, Geumgok-Dong, Bundang-Gu, Seongnam-Shi 463-943, Korea. jungek@insilicotech.co.kr
Kim
Junhyoung
J
Choi
Seung-Hoon
SH
Kim
Minkyoung
M
Rhee
Hokyoung
H
Shin
Jae-Min
JM
Choi
Kihang
K
Kang
Sang-Kee
SK
Lee
Nam Kyung
NK
Choi
Yun-Jaie
YJ
Jung
Dong Hyun
DH
D000595:Amino Acid Sequence; D000818:Animals; D016503:Drug Delivery Systems; D007668:Kidney / Q000378:metabolism; D008099:Liver / Q000378:metabolism; D008168:Lung / Q000378:metabolism; D008297:Male; D008954:Models, Biological; D016571:Neural Networks, Computer; D019151:Peptide Library; D010455:Peptides / Q000008:administration & dosage* / Q000737:chemistry / Q000493:pharmacokinetics*; D051381:Rats; D013154:Spleen / Q000378:metabolism
D019151:Peptide Library; D010455:Peptides