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Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based

Shahid Akbar^1,2, Ali Raza³, Quan Zou^4,5

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.

BMC Bioinformatics

|March 8, 2024

Summary

This study introduces Deepstacked-AVPs, a machine learning model that accurately identifies antiviral peptides (AVPs). The model offers a faster, more cost-effective alternative to traditional drug discovery methods for viral diseases.

Keywords:

Antiviral peptides Feature selection Prediction Stacked ensemble model Tri-segmentation based evolutionary features Word embedding

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Area of Science:

Computational biology
Bioinformatics
Drug discovery

Background:

Viral infections pose a significant global health challenge.
Antiviral peptides (AVPs) show promise as therapeutic agents against viral diseases.
Current drug development is costly and time-consuming, necessitating advanced computational approaches.

Purpose of the Study:

To develop a novel computational model, Deepstacked-AVPs, for accurate discrimination of antiviral peptides (AVPs).
To overcome limitations of existing wet-laboratory methods by leveraging machine learning for AVP identification.
To provide a reliable and efficient tool for accelerating the discovery of novel antiviral peptide therapeutics.

Main Methods:

Numerical encoding of peptide sequences using Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec semantic features.
Integration of physiochemical properties via Composition/Transition/Distribution-Transition (CTDT) descriptors.
Feature selection using Information Gain (IG) and classification with a stacked-ensemble model.

Main Results:

The Deepstacked-AVPs model achieved high predictive accuracy (96.60% on training, 95.15% on independent samples).
Excellent performance metrics including Area Under the Curve (AUC) of 0.98 and Precision-Recall (PR) of 0.97 were obtained.
The model demonstrated superior accuracy compared to existing methods.

Conclusions:

Deepstacked-AVPs offers a reliable and effective computational tool for identifying antiviral peptides.
The model's performance surpasses existing methods, suggesting its utility in pharmaceutical design and research.
This approach can significantly aid in the development of novel antiviral peptide-based drugs.