Introduction

Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds.

Objective

The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries.

Methods

We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study.

Results

We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag.

Conclusion

MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

中文翻译：

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MetFID：用于代谢物注释的基于人工神经网络的化合物指纹预测

介绍

代谢物注释是基于质谱的代谢组学分析中一个关键且具有挑战性的步骤。在典型的基于非靶向 MS/MS 的代谢组学研究中，实验 MS/MS 光谱与光谱库中的光谱进行匹配，以进行代谢物注释。然而，现有的光谱库仅包含一小部分已知化合物。

客观的

目标是开发一种方法，帮助对质谱库中不存在参考 MS/MS 光谱的分析物的推定代谢物 ID 进行排序。

方法

我们介绍了 MetFID，它使用经过训练的人工神经网络 (ANN)，用于根据实验 MS/MS 数据预测分子指纹。为了缩小搜索空间，MetFID 使用分子式或分析物母离子的 m/z 值从代谢物数据库中检索候选物。指纹与预测指纹最相似的候选者用于代谢物注释。通过使用 MoNA 存储库和 NIST 库中的 MS/MS 谱图训练 MetFID，并使用 NIST 库中的结构不相交 MS/MS 谱图、CASMI 2016 数据集和来自的内部 MS/MS 数据进行了综合评估。癌症生物标志物发现研究。

结果

我们观察到，与涵盖广泛碰撞能量的单个模型相比，针对不同范围的碰撞能量训练单独的模型可以提高模型性能。使用 MetaboQuest 检索候选人，MetFID 在大约 50% 的测试用例中将正确的推定 ID 排在首位。通过独立的测试数据集，我们证明与 ChemDistiller、CSI:FingerID 和 MetFrag 等其他工具相比，MetFID 具有将推定代谢物 ID 排序的准确性提高 5% 以上的潜力。

结论

MetFID 为通过使用 ANN 进行分子指纹预测来提高代谢物注释的准确性提供了有希望的机会。