Deep learning improves antimicrobial peptide recognition

By Daniel Veltri, Uday Kamath, Amarda Shehu. Bioinform., 2018

497 citations recorded by the source corpus.

Abstract Motivation Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. Results In this work, we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive dataset. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino acid types. Availability and implementation Models and datasets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at www.ampscanner.com. Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Microbiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/62aa4ec332264ea1b14ea70ca2feba6c1eeaafef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Microbiology

1. John Muschelli

In Molecular Biology

1. Payel Das
2. Vijil Chenthamarakshan
3. C. D. Santos
4. Tom Sercu
5. John Muschelli
6. Matthew Riemer
7. Inkit Padhi

Citations in the corpus

• Identification of antimicrobial peptides from the human gut microbiome using deep learning (2022, methodology): “The most fundamental model is a convolutional NNM with LSTM layer architecture, which has been used in the current research of Veltri et al. 26 .”
• ROC and AUC with a Binary Predictor: a Potentially Misleading Metric (2019, methodology): “But alas, ROC and AUC analysis has been done on binary predictors and used to inform if one variable is more predictive than the other (Maverakis et al. 2018; Litvin et al. 2017; Glaveckaite et al. 2011; Blumberg et al. 2016; Budwega et al. 2016; Mwipatayi et al. 2016; Xiong et al. 2018; Shterev et ”
• PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences (2018, methodology): “Peptide properties like aromaticity, Eisenberg hydrophobicity, were estimated using the GlobalAnalysis method in modLAMP ?.”