Abstract
Cell-penetrating peptides (CPPs) have proven their potential as versatile drug delivery vehicles. Last decade has witnessed an unprecedented growth in CPP-based research, demonstrating the potential of CPPs as therapeutic candidates. In the past, many in silico algorithms have been developed for the prediction and screening of CPPs, which expedites the CPP-based research. In silico screening/prediction of CPPs followed by experimental validation seems to be a reliable, less time-consuming, and cost-effective approach. This chapter describes the prediction, screening, and designing of novel efficient CPPs using "CellPPD," an in silico tool.
| Original language | English |
|---|---|
| Title of host publication | Cell-Penetrating Peptides |
| Subtitle of host publication | Methods and Protocols |
| Publisher | Springer New York |
| Pages | 59-69 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781493928064 |
| ISBN (Print) | 9781493928057 |
| DOIs | |
| State | Published - 22 Jul 2015 |
| Externally published | Yes |
Keywords
- Cell-penetrating peptides
- Drug delivery system
- Machine learning approach
- Prediction
- Support vector machine
- Virtual screening
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