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Phage Peptide Display

  • Jessica Newton
  • Susan L. DeutscherEmail author
Part of the Handbook of Experimental Pharmacology book series (HEP, volume 185/2)

Abstract

Molecular imaging is at the forefront in the advancement of in-vivo diagnosis and monitoring of cancer. New peptide-based molecular probes to facilitate cancer detection are rapidly evolving. Peptide-based molecular probes that target apoptosis, angiogenesis, cell signaling and cell adhesion events are in place. Bacteriophage (phage) display technology, a molecular genetic approach to ligand discovery, is commonly employed to identify peptides as tumor-targeting molecules. The peptide itself may perhaps have functional properties that diminish tumor growth or metastasis. More often, a selected peptide is chemically synthesized, coupled to a radiotracer or fluorescent probe, and utilized in the development of new noninvasive molecular imaging probes. A myriad of peptides that bind cancer cells and cancer-associated antigens have been reported from phage library selections. Phage selections have also been performed in live animals to obtain peptides with optimal stability and targeting properties in vivo. To this point, few in-vitro, in-situ, or in-vivo selected peptides have shown success in the molecular imaging of cancer, the notable exception being vascular targeting peptides identified via in-vivo selections. The success of vasculature targeting peptides, such as those with an RGD motif that bind αvβ3integrin, may be due to the abundance and expression patterns of integrins in tumors and supporting vasculature. The discovery of molecular probes that bind tumor-specific antigens has lagged considerably. One promising means to expedite discovery is through the implementation of selected phage themselves as tumor-imaging agents in animals.

Keywords

Phage Display Phage Clone Phage Display Library Phage Display Peptide Library Phage Peptide 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Department of BiochemistryUniversity of MissouriColumbia

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