sAMP‐VGG16: Force‐field assisted image‐based deep neural network prediction model for short antimicrobial peptides.
Saved in:
| Title: | sAMP‐VGG16: Force‐field assisted image‐based deep neural network prediction model for short antimicrobial peptides. |
|---|---|
| Authors: | Pandey, Poonam, Srivastava, Anand |
| Source: | Proteins; Jan2025, Vol. 93 Issue 1, p372-383, 12p |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Proteins is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Be the first to leave a comment!