Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity

Area of Science:

• Environmental microbiology
• Computational biology
• Infectious disease epidemiology

Background:

• Airborne pathogens pose significant risks to human health, particularly in indoor environments, as highlighted by the COVID-19 pandemic.
• Contaminated indoor air with viruses, bacteria, and fungi can lead to various health issues.
• Effective identification of airborne pathogens is essential for disease prevention and maintaining healthy indoor air quality.

Purpose of the Study:

• To develop and apply deep learning technology for analyzing and predicting the toxicity of bacteria present in indoor air.
• To assess the potential of computational methods in identifying pathogenic bacteria based on their protein sequences.
• To enhance strategies for preventing infectious diseases transmitted through indoor air.

Main Methods:

• Utilized deep learning, specifically the ProtBert model, trained on toxic bacterial and virulence factor proteins.
• Applied the trained model to predict the toxicity of bacterial species by analyzing their protein sequences.
• Validated the in silico predictions against in vitro analyses of bacterial toxicity in human cells.

Main Results:

• The deep learning model successfully predicted bacterial toxicity, with results aligning with in vitro experimental findings.
• The study demonstrated the plausibility of identifying potential toxic protein sequences within unknown bacterial samples.
• The computational approach proved effective in assessing the pathogenic potential of airborne bacteria.

Conclusions:

• Deep learning offers a powerful tool for predicting airborne bacterial toxicity and identifying potential health threats in indoor environments.
• In silico analysis of protein sequences can reliably predict bacterial pathogenicity, complementing traditional laboratory methods.
• This research contributes to improved methods for monitoring indoor air quality and preventing the spread of airborne infectious diseases.