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Human Virome

Human Virome

The human body harbors a vast and diverse viral community known as the human virome. The virome includes bacteriophages that infect bacteria, and eukaryotic viruses that infect human cells. Transient dietary and environmental viruses also contribute to this dynamic ecosystem. Estimates suggest the human body may contain on the order of 10¹³ viral particles, though abundance varies widely by body site and detection method.Comprehensive characterization of the virome has become possible...

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

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Published on: December 1, 2020

Accelerating Prediction of Antiviral Peptides Using Genetic Algorithm-Based Weighted Multiperspective Descriptors

Shahid Akbar^1,2, Ali Raza^3,4,5, Quan Zou^1,6

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

Journal of Chemical Information and Modeling

|September 3, 2025

Summary

This summary is machine-generated.

This study introduces TargetAVP-DeepCaps, a novel deep learning model for accurately predicting antiviral peptides (AVPs). The model achieves 97.36% accuracy, significantly advancing peptide-based antiviral drug discovery.

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Area of Science:

Computational Biology
Bioinformatics
Drug Discovery

Background:

Viral diseases pose a significant global health challenge despite existing antiviral medications.
Antiviral peptides (AVPs) show promise as novel therapeutic agents.
Traditional methods for identifying AVPs are inefficient and lack deep sequence insights.

Purpose of the Study:

To develop a precise and efficient computational model for predicting antiviral peptides (AVPs).
To overcome the limitations of traditional labor-intensive and expensive AVP identification methods.
To enhance the understanding of peptide mechanisms in antiviral drug development.

Main Methods:

Utilized ProtGPT2 for contextual peptide embeddings and sequence-to-image transformations (SMR, RECM).
Applied CLBP for local image decomposition and differential evolution for feature vector formation.
Employed a hybrid MRMD + SFLA approach for optimal feature selection.
Developed a novel self-normalized capsule network (Sn-CapsNet) for prediction.

Main Results:

Achieved a superior predictive accuracy of 97.36% for AVPs.
Outperformed existing predictors by approximately 12% with an AUC of 0.98.
Demonstrated robust generalization on an independent dataset with an 8% improvement over previous models.

Conclusions:

The TargetAVP-DeepCaps model offers a highly accurate and efficient tool for AVP prediction.
This computational approach accelerates the discovery and development of peptide-based antiviral therapeutics.
Provides a valuable resource for understanding peptide mechanisms and their applications in drug discovery.