Authors:
Ümmü Söylemez
1
;
Malik Yousef
2
and
Burcu Bakir-Gungor
3
Affiliations:
1
Department of Software Engineering, Faculty of Engineering, Muş Alparslan University, Muş, Turkey
;
2
Department of Information Systems, Zefat Academic College, Zefat, 13206, Israel
;
3
Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey
Keyword(s):
Antimicrobial Peptides, Deep Neural Networks, Physicochemical Properties.
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 Hydro
phobic 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.
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