Pattern Recognition and Machine Learning Lab, Department of AI Software, Gachon University, Seongnam 13557, Korea.
Department of Microbiology and Immunology, Chosun University School of Dentistry, Gwangju 61452, Korea.
Sensors (Basel). 2022 Aug 31;22(17):6557. doi: 10.3390/s22176557.
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.
一些通过空气传播的病原体具有高度传染性，在室内条件下，相关传染病更容易通过空气传播，这在 COVID-19 大流行期间就有所观察。被微生物（包括病毒、细菌和真菌）污染的室内空气，或被衍生的致病物质污染的空气，会危害人类健康。因此，识别和分析空气中潜在的病原体对于预防疾病和保持室内空气质量至关重要。在这里，我们应用深度学习技术来分析和预测室内空气中细菌的毒性。我们在 ProtBert 模型上对有毒细菌和毒力因子蛋白进行了训练，并通过分析它们的蛋白质序列来预测一些细菌物种的潜在毒性。结果反映了它们在人类细胞中的体外毒性分析结果。基于计算机模拟的结果表明，在未知的蛋白质序列中找到可能的有毒序列是合理的。
