Research article

Artificial neural network models for prediction of intestinal permeability of oligopeptides

Eunkyoung Jung¹ , Junhyoung Kim¹ , Minkyoung Kim¹ , Dong Hyun Jung¹ , Hokyoung Rhee¹ , Jae-Min Shin² , Kihang Choi³ , Sang-Kee Kang⁴ , Min-Kook Kim⁴ , Cheol-Heui Yun⁴ , Yun-Jaie Choi⁴ and Seung-Hoon Choi¹

¹Insilicotech Co. Ltd., A-1101 Kolontripolis, 210, Geumgok-Dong, Bundang-Gu, Seongnam-Shi, 463-943, Korea

²SBScience Co. Ltd., Sung-Ok BD, Sunae-Dong, Bundang-Gu, Seongnam-Shi, 463-825, Korea

³Department of Chemistry, Korea University, 1, Anam-dong 5-Ga, Seongbuk-Gu, Seoul, 136-701, Korea

⁴School of Agriculture Biotechnology, Seoul National University, San56-1, Shilim-Dong, Kwanak-gu, 151-742, Korea

author email corresponding author email

BMC Bioinformatics 2007, 8:245doi:10.1186/1471-2105-8-245

Published:	11 July 2007

Abstract

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.

Conclusion

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.