Abstract
The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.
| Language | English |
|---|---|
| Pages | 2006-2015 |
| Number of pages | 10 |
| Journal | Journal of Medicinal Chemistry |
| Volume | 52 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 9 Apr 2009 |
ASJC Scopus subject areas
- Molecular Medicine
- Drug Discovery
Cite this
}
Identification of novel antibacterial peptides by chemoinformatics and machine learning. / Fjell, Christopher D.; Jenssen, Håvard; Hilpert, Kai; Cheung, Warren A.; Panté, Nelly; Hancock, Robert; Cherkasov, Artem.
In: Journal of Medicinal Chemistry, Vol. 52, No. 7, 09.04.2009, p. 2006-2015.Research output: Contribution to journal › Article
TY - JOUR
T1 - Identification of novel antibacterial peptides by chemoinformatics and machine learning
AU - Fjell, Christopher D.
AU - Jenssen, Håvard
AU - Hilpert, Kai
AU - Cheung, Warren A.
AU - Panté, Nelly
AU - Hancock, Robert
AU - Cherkasov, Artem
PY - 2009/4/9
Y1 - 2009/4/9
N2 - The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.
AB - The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.
UR - http://www.scopus.com/inward/record.url?scp=64549125571&partnerID=8YFLogxK
U2 - 10.1021/jm8015365
DO - 10.1021/jm8015365
M3 - Article
VL - 52
SP - 2006
EP - 2015
JO - Journal of Medicinal Chemistry
T2 - Journal of Medicinal Chemistry
JF - Journal of Medicinal Chemistry
SN - 0022-2623
IS - 7
ER -