Nava Lara Rodrigo A, Beltrán Jesús A, Brizuela Carlos A, Del Rio Gabriel
Department of Biochemistry and Structural Biology, Instituto de Fisiologia Celular, UNAM, Mexico City 04510, Mexico.
Department of Computer Science, CICESE Research Center, Ensenada 22860, Mexico.
Pharmaceuticals (Basel). 2020 Aug 21;13(9):204. doi: 10.3390/ph13090204.
Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.
多靶点人类靶向抗菌药物（polyHAM）在治疗微生物群起重要作用的复杂人类疾病（如糖尿病、高血压）方面可能具有潜在用途。我们之前报道了一种机器学习方法，通过异源方法（用肽和非肽化合物进行训练）从美国食品药品监督管理局（FDA）批准的人类靶向药物中识别polyHAM。在此，我们发现polyHAM更有可能在具有广谱抗生素活性的抗菌药物中被发现，并且拓扑特征而非化学特征对于分类这种活性最为重要。用广谱抗菌药物训练一种异源机器学习方法，并用人类代谢物进行测试；基于一种简单的文本挖掘方法，将这些代谢物标记为抗菌药物或非抗菌药物。人类代谢物通常不被视为抗菌药物，但在发现微生物的人体中循环，我们的异源模型能够对具有抗菌活性的代谢物进行分类。这些结果为开发旨在设计人类饮食以有意改变代谢化合物比例从而控制人类微生物群的应用提供了基础。
