sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides

Pandey, P and Srivastava, A (2024) sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides. In: Proteins: Structure, Function and Bioinformatics .

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Abstract

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (�30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16. © 2024 Wiley Periodicals LLC.

Item Type:	Journal Article
Publication:	Proteins: Structure, Function and Bioinformatics
Publisher:	John Wiley and Sons Inc
Additional Information:	The copyright for this article belongs to John Wiley and Sons Inc.
Department/Centre:	Division of Biological Sciences > Molecular Biophysics Unit
Date Deposited:	22 May 2024 04:44
Last Modified:	22 May 2024 04:44
URI:	https://eprints.iisc.ac.in/id/eprint/84913

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