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Multidimensional Design of Anticancer Peptides.
Journal article

Multidimensional Design of Anticancer Peptides.

  • Lin YC Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Lim YF Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Russo E Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Schneider P Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Bolliger L Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Edenharter A Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Altmann KH Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Halin C Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Hiss JA Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland).
  • Schneider G Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich (Switzerland). gisbert.schneider@pharma.ethz.ch.
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  • 2015-06-30
Published in:
  • Angewandte Chemie (International ed. in English). - 2015
cancer
drug discovery
lipid membranes
machine learning
molecular design
Antineoplastic Agents
Apoptosis
Breast Neoplasms
Cell Survival
Cell-Penetrating Peptides
Cells, Cultured
Computer-Aided Design
Dermis
Drug Therapy, Combination
Female
Humans
English The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells. The computer-generated sequences featured improved cancer-cell penetration, induced cancer-cell apoptosis, and were enabled a decrease in the cytotoxic concentration of co-administered chemotherapeutic agents in vitro. This study demonstrates the potential of multidimensional machine-learning methods for rapidly obtaining peptides with the desired cellular activities.
Language
  • English
Open access status
closed
Identifiers
Persistent URL
https://sonar.ch/global/documents/68237
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