Identification of tissue-specific targeting peptide

  • Jung, Eunkyoung
  • Lee, Nam Kyung
  • Kang, Sang-Kee
  • Choi, Seung-Hoon
  • Kim, Daejin
  • ... Choi, Kihang
  • 외 3명
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초록

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.

키워드

Machine learningPartial least squaresArtificial neural networkBayesianSupport vector machineTissue-specific targeting peptideROC scoreARTIFICIAL NEURAL-NETWORKVIVO PHAGE DISPLAYIN-VIVODRUG-DELIVERYHOMING PEPTIDECELLSTHERAPYBINDINGLUNGHETEROGENEITY
제목
Identification of tissue-specific targeting peptide
저자
Jung, EunkyoungLee, Nam KyungKang, Sang-KeeChoi, Seung-HoonKim, DaejinPark, KisooChoi, KihangChoi, Yun-JaieJung, Dong Hyun
DOI
10.1007/s10822-012-9614-6
발행일
2012-11
유형
Article
저널명
Journal of Computer-Aided Molecular Design
26
11
페이지
1267 ~ 1275