Identification of tissue-specific targeting peptide.

Eunkyoung Jung, Nam Kyung Lee, Sang Kee Kang, Seung Hoon Choi, Daejin Kim, Kisoo Park, Kihang Choi, Yun Jaie Choi, Dong Hyun Jung

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1267-1275
Number of pages9
JournalJournal of computer-aided molecular design
Issue number11
Publication statusPublished - 2012 Nov

Bibliographical note

Funding 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 (

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Physical and Theoretical Chemistry


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