Prediction of Antimicrobial Peptides Using Deep Neural Networks

Ümmü Söylemez, Malik Yousef, Burcu Bakir-Gungor

2023

Abstract

Antimicrobial peptides (AMPs) are crucial elements of the innate immune system; and they are effective against bacteria that cause several diseases. These peptides are investigated as potential alternatives of antibiotics to treat infections. Since wet lab experiments are expensive and time-consuming, computational methods become crucial in this field. In this study, we suggest a computational technique for AMP prediction using deep neural networks (DNN). We trained a DNN classifier using physicochemical features that include a sequential model; and evaluated the model with 10-fold cross-validation on a benchmark dataset. We compared our method with other machine learning approaches and demonstrated that the method we developed generates higher performance (accuracy: 92%, precision: 92%, recall: 93%, f1: 93%, AUC: 98%). In our experiments, we have realized that there is a strong positive correlation between the ‘Normalized Hydrophobic Moment’ feature and ‘Angle Subtended by the Hydrophobic Residues’ feature; and strong negative correlations between ‘Normalized Hydrophobicity’ feature and ‘Disordered Conformation Propensity’ feature, and between ‘Amphilicity Index’ - ‘Disordered Conformation Propensity’ features. We believe that the approach we proposed could guide further experimental studies and could facilitate the prediction of other types of AMPs having anticancer, antivirus, antiparasitic activities.

Paper Citation

in Harvard Style

Söylemez Ü., Yousef M. and Bakir-Gungor B. (2023). Prediction of Antimicrobial Peptides Using Deep Neural Networks. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-631-6, SciTePress, pages 188-194. DOI: 10.5220/0011690000003414

in Bibtex Style

@conference{bioinformatics23,
author={Ümmü Söylemez and Malik Yousef and Burcu Bakir-Gungor},
title={Prediction of Antimicrobial Peptides Using Deep Neural Networks},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS},
year={2023},
pages={188-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011690000003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS
TI - Prediction of Antimicrobial Peptides Using Deep Neural Networks
SN - 978-989-758-631-6
AU - Söylemez Ü.
AU - Yousef M.
AU - Bakir-Gungor B.
PY - 2023
SP - 188
EP - 194
DO - 10.5220/0011690000003414
PB - SciTePress