论文标题
通过簇预测新代谢物的途径
Predicting pathways for old and new metabolites through clustering
论文作者
论文摘要
多样化的代谢途径是所有生物体的基础，因为它们收获能量，合成生物量成分，产生分子与微环境相互作用并中和毒素。尽管发现新的代谢产物和途径的发现仍在继续，但对新代谢产物的途径的预测可能具有挑战性。阐明新代谢产物的途径可能需要大量时间。因此，根据HMDB，只有60％的代谢物被分配到途径。在这里，我们提出了一种基于代谢物结构的途径的方法。我们从微笑注释中提取了201个功能，并从PubMed摘要和HMDB中确定了新的代谢物。在将聚类算法应用于两组特征之后，我们量化了代谢物之间的相关性，发现簇将92％的已知代谢产物与各自的途径相关。因此，这种方法对于预测新代谢产物的代谢途径可能是有价值的。
The diverse metabolic pathways are fundamental to all living organisms, as they harvest energy, synthesize biomass components, produce molecules to interact with the microenvironment, and neutralize toxins. While discovery of new metabolites and pathways continues, the prediction of pathways for new metabolites can be challenging. It can take vast amounts of time to elucidate pathways for new metabolites; thus, according to HMDB only 60% of metabolites get assigned to pathways. Here, we present an approach to identify pathways based on metabolite structure. We extracted 201 features from SMILES annotations, and identified new metabolites from PubMed abstracts and HMDB. After applying clustering algorithms to both groups of features, we quantified correlations between metabolites, and found the clusters accurately linked 92% of known metabolites to their respective pathways. Thus, this approach could be valuable for predicting metabolic pathways for new metabolites.
