DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models.

Area of Science:

• Bioinformatics
• Microbiology
• Computational Biology

Background:

• Virulence factors (VFs) are critical microbial molecules enabling pathogens to evade host immunity.
• Identifying VFs is essential for understanding pathogenesis and advancing drug discovery.
• Accurate VF prediction presents a significant bioinformatics challenge.

Purpose of the Study:

• To develop a novel computational model for accurate prediction of microbial virulence factors.
• To enhance the identification of key molecules involved in pathogen-host interactions.
• To provide a tool for accelerating drug discovery efforts.

Main Methods:

• Proposed a novel Deep Transfer Learning for Virulence Factor Prediction (DTVF) model.
• Integrated ProtT5 protein sequence extraction with a dual-channel deep learning architecture (LSTM and CNN).
• Incorporated an attention mechanism to improve detection accuracy.

Main Results:

• DTVF achieved a high accuracy rate of 84.55% and an Area Under the Receiver Operating Characteristic curve (AUROC) of 92.08% on a benchmark dataset.
• The model demonstrated superior performance compared to existing state-of-the-art methods across multiple metrics.
• Developed an interactive web-based user interface for DTVF using Gradio for accessibility.

Conclusions:

• The DTVF model represents a significant advancement in the accurate prediction of microbial virulence factors.
• This approach offers a powerful new tool for bioinformatics research and infectious disease studies.
• The user-friendly interface facilitates broader application in biological research and drug development.