A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds. Issue 8 (15th February 2022)

• Record Type: Journal Article
• Authors: Li, Zhijun; Jin, Kun; Chen, Hong; Zhang, Liyuan; Zhang, Guitao; Jiang, Yizhou; Zou, Haixia; Wang, Wentao; Qi, Guangpei; Qu, Xiangmeng
• Is Part Of: Nanoscale. Volume 14:Issue 8(2022)
• Page Start: 3087
• Page End: 3096
• Publication Date: 2022-02-15
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d1nr07452k
• Languages: English
• ISSNs: 2040-3364

Abstract

Abstract : We present a machine learning approach-based array sensor for high-accuracy profiling of mechanisms of action (MoAs) by sensing the physicochemical changes on surfaces of bacteria. We successfully predict the MoAs of 4 antimicrobial compounds and a novel small molecule AMP. Abstract : Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds have been developed in recent years. In particular, approaches combining machine learning technology enable precisely recognizing the MoA of antibacterial compounds. However, these methods heavily rely on the big data resulting from multiplexed experiments. As such, these approaches tend to produce minimal throughput and are not comprehensive enough to be adapted to widespread industrial applications. Here, we present a machine learning approach based on a customized array sensor for directly identifying the MoAs of antibacterial compounds. The array sensor consists of different two-dimensional nanomaterial fluorescence quenchers with different fluorescence-labeled single-stranded DNAs (ssDNAs). By mapping the subtle difference of the physicochemical properties on the bacterial surface treated with different antibacterial compound stimuli, the array sensor ensures visualizing the recognition process. Moreover, the customized array sensor produces a high volume of the MoA database, overcoming the dependence on big data. We further use the array sensor to build a chemical-response unique "fingerprint" database of MoAs. By combining a neural network-based genetic algorithm (NNGA), we rapidly discriminate the MoAs of four antibiotics with an overall accuracy of 100%. Furthermore, a new screening antibacterial peptide has been discovered and evaluated by our approach for determining the MoA with high accuracy proven by other techniques.