Artificial neural network models for prediction of intestinal permeability of oligopeptides.
BACKGROUND
Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences.


RESULTS
The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence.


CONCLUSIONS
We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties) descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.
Jung
Eunkyoung
E
Insilicotech Co, Ltd., A-1101 Kolontripolis, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, Korea. jungek@insilicotech.co.kr <jungek@insilicotech.co.kr>
Kim
Junhyoung
J
Kim
Minkyoung
M
Jung
Dong Hyun
DH
Rhee
Hokyoung
H
Shin
Jae-Min
JM
Choi
Kihang
K
Kang
Sang-Kee
SK
Kim
Min-Kook
MK
Yun
Cheol-Heui
CH
Choi
Yun-Jaie
YJ
Choi
Seung-Hoon
SH
D000595:Amino Acid Sequence; D003198:Computer Simulation; D007408:Intestinal Absorption / Q000502:physiology*; D008954:Models, Biological; D008956:Models, Chemical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000737:chemistry* / Q000145:classification / Q000493:pharmacokinetics*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010539:Permeability; D020539:Sequence Analysis, Protein / Q000379:methods*
D009842:Oligopeptides