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Machine learning study for the prediction of transdermal peptide

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Abstract

In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

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Acknowledgments

This work was supported by the Korea Science and Engineering Foundation (KOSEF) NRL Program grant funded by the Korea government (MEST) (No. R0A-2008-000-20024-1). We thank Accelrys Korea for the support of SciTegic Pipeline Pilot software.

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Authors and Affiliations

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

    Eunkyoung Jung, Seung-Hoon Choi & Dong Hyun Jung

  2. School of Agriculture Biotechnology, Seoul National University, San 56-1, Shilim-Dong, Kwanak-gu, Seoul, 151-742, Korea

    Nam Kyung Lee, Sang-Kee Kang & Yun-Jaie Choi

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

    Jae-Min Shin

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

    Kihang Choi

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  1. Eunkyoung Jung
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Correspondence to Dong Hyun Jung.

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Jung, E., Choi, SH., Lee, N.K. et al. Machine learning study for the prediction of transdermal peptide. J Comput Aided Mol Des 25, 339–347 (2011). https://doi.org/10.1007/s10822-011-9424-2

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  • DOI: https://doi.org/10.1007/s10822-011-9424-2

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