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PhyteByte: identification of foods containing compounds with specific pharmacological properties.
BMC Bioinformatics ( IF 3 ) Pub Date : 2020-06-10 , DOI: 10.1186/s12859-020-03582-7
Kenneth E Westerman 1, 2 , Sean Harrington 3 , Jose M Ordovas 1 , Laurence D Parnell 4
Affiliation  

Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

中文翻译:

PhyteByte:鉴定含有具有特定药理特性的化合物的食品。

食品中的植物化学物质和其他分子通常通过不完善的机制或未知的机制可带来积极的健康益处。尽管有大量有关药物和治疗性化合物的生物学和生物物理特性的数据,但对于食品中常见的化合物却缺乏类似的数据。高通量鉴定具有特定生物学效应的食物化合物的计算方法,尤其是在伴随相关食物成分数据的情况下,可以使更有效,更个性化的饮食计划成为可能。我们创建了一个基于机器学习的工具(PhyteByte),以利用现有的药理数据来预测整个食品和食品化合物分子数据库中的生物活性。PhyteByte使用化学信息学方法进行基于结构的活性预测,并将其应用于发现食品化合物的假定生物活性。该工具以输入蛋白质为目标,并使用从ChEMBL数据库获得的结构和活性数据,基于其分子指纹,开发了一个随机森林分类器来预测输入分子的效果。然后,它可以从FooDB数据库中预测具有已知分子结构的食物化合物库的相关生物活性。输出是具有引起相关生物效应的高可信度的食物化合物的清单,以及它们的来源食物和这些食物中的相关数量(如果有)。将PhyteByte应用于人类PPARG基因,我们确定了irigenin,芝麻素,fargesin和delta-sanshool是PPARG的假定激动剂,以及先前确定的这种重要代谢调节剂的激动剂。PhyteByte可识别预计与特定蛋白质靶标相互作用的基于食物的化合物。所确定的关系可用于为实验或流行病学随访确定食物化合物的优先顺序,并有助于快速开发新的保健食品的精确方法以及个性化的饮食计划。
更新日期:2020-06-10
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