期刊
CHEMBIOCHEM
卷 15, 期 15, 页码 2225-2231出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/cbic.201402231
关键词
drug discovery; machine learning; membrane interaction; peptide design; vesicles
资金
- ETH Zurich
- Swiss National Science Foundation (SNF) [205321-134783, 206021-133768]
- Swiss National Science Foundation (SNF) [206021_133768] Funding Source: Swiss National Science Foundation (SNF)
Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low-nanomolar membranolytic activities. Thermal analysis revealed an entropy-driven mechanism of action. The study demonstrates sustained potential of advanced computational methods for designing peptides with the desired activity.
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