Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity.
Sung-Yoon AhnMira KimJi-Eun BaeIel Soo BangSang-Woong LeePublished in: Sensors (Basel, Switzerland) (2022)
Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences.
Keyphrases
- human health
- particulate matter
- air pollution
- risk assessment
- health risk
- infectious diseases
- oxidative stress
- deep learning
- antimicrobial resistance
- heavy metals
- drinking water
- climate change
- genetic diversity
- gram negative
- staphylococcus aureus
- protein protein
- binding protein
- multidrug resistant
- oxide nanoparticles
- amino acid
- convolutional neural network
- biofilm formation
- resistance training