Bournez, Colin and Riool, Martijn and de Boer, Leonie and Cordfunke, Robert A. and de Best, Leonie and van Leeuwen, Remko and Drijfhout, Jan Wouter and Zaat, Sebastian A. J. and van Westen, Gerard J. P. (2023) CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. ANTIBIOTICS-BASEL, 12 (4): 725. ISSN 2079-6382,
Full text not available from this repository. (Request a copy)Abstract
To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ANTIBACTERIAL PEPTIDES; RESISTANCE; DISCOVERY; DERMCIDIN; DATABASE; antimicrobial peptides; artificial intelligence; bacteria; drug discovery; machine learning; antimicrobial resistance |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Unfallchirurgie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 26 Mar 2024 08:13 |
| Last Modified: | 26 Mar 2024 08:13 |
| URI: | https://pred.uni-regensburg.de/id/eprint/60564 |
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