Machine Learning Algorithms Highlight tRNA Information Content and Chargaff’s Second Parity Rule Score as Important Features in Discriminating Probiotics from Non-Probiotics

Abstract

Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information content in their tRNA sequences compared to non-probiotics. In conclusion, we prove for the first time that Machine Learning methods can discriminate human probiotic from non-probiotic organisms underlining information within tRNA sequences as the most important genomic feature in distinguishing them.

The Potential of Meta-Proteomics and Artificial Intelligence to Establish the Next Generation of Probiotics for Personalized Healthcare

“…Given this, Bergamini et al proposed that ANN is the most reliable algorithmic approach (90% accuracy) among other ML tools to distinguish human probiotics from nonprobiotic organisms based on their t-RNA sequences. 112 Furthermore, targeting a specific probiotic strain, Josephs-Spaulding and colleagues combined transcriptomic study with ML tools to decode the transcriptional regulatory networks of L. reuteri for the identification of specific gene sets involved in the molecular mechanism, reflecting the functional aspects and presenting the fundamental properties of the probiotic organism. 113 Thus, it can be conceived that a combined approach of in vitro and in silico study is more impactful than any of the individual methods.…”