Using phage display technique, we identified tissue-targeting peptide sets that recognize specific tissues (bone-marrow dendritic cell, kidney, liver, lung, spleen and visceral adipose tissue). In order to rapidly evaluate tissue-specific targeting peptides, we performed machine learning studies for predicting the tissue-specific targeting activity of peptides on the basis of peptide sequence information using four machine learning models and isolated the groups of peptides capable of mediating selective targeting to specific tissues. As a representative liver-specific targeting sequence, the peptide "DKNLQLH" was selected by the sequence similarity analysis. This peptide has a high degree of homology with protein ligands which can interact with corresponding membrane counterparts. We anticipate that our models will be applicable to the prediction of tissue-specific targeting peptides which can recognize the endothelial markers of target tissues.
Bibliographical noteFunding Information:
Acknowledgments This research is supported by National Research Foundation of Korea (NRF), Korea government (MEST) (Project No. 2011-0029416). We thank Accelrys Korea for the support of SciTegic Pipeline Pilot and Discovery Studio software, and acknowledge the assistance of BioMedES (http://www.bio medes.co.uk/home).
ASJC Scopus subject areas
- Drug Discovery
- Computer Science Applications
- Physical and Theoretical Chemistry