Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS

Citations Over TimeTop 10% of 2019 papers

Abstract

Untargeted metabolomic measurements using mass spectrometry are a powerful tool for uncovering new small molecules with environmental and biological importance. The small molecule identification step, however, still remains an enormous challenge due to fragmentation difficulties or unspecific fragment ion information. Current methods to address this challenge are often dependent on databases or require the use of nuclear magnetic resonance (NMR), which have their own difficulties. The use of the gas-phase collision cross section (CCS) values obtained from ion mobility spectrometry (IMS) measurements were recently demonstrated to reduce the number of false positive metabolite identifications. While promising, the amount of empirical CCS information currently available is limited, thus predictive CCS methods need to be developed. In this article, we expand upon current experimental IMS capabilities by predicting the CCS values using a deep learning algorithm. We successfully developed and trained a prediction model for CCS values requiring only information about a compound's SMILES notation and ion type. The use of data from five different laboratories using different instruments allowed the algorithm to be trained and tested on more than 2400 molecules. The resulting CCS predictions were found to achieve a coefficient of determination of 0.97 and median relative error of 2.7% for a wide range of molecules. Furthermore, the method requires only a small amount of processing power to predict CCS values. Considering the performance, time, and resources necessary, as well as its applicability to a variety of molecules, this model was able to outperform all currently available CCS prediction algorithms.

Related Papers

• → Ion mobility–mass spectrometry(2008)1,150 cited
• → Adding a new separation dimension to MS and LC–MS: What is the utility of ion mobility spectrometry?(2017)180 cited
• → Enhancement of mass spectrometry performance for proteomic analyses using high‐field asymmetric waveform ion mobility spectrometry (FAIMS)(2015)42 cited
• → Ambient surface mass spectrometry–ion mobility spectrometry of intact proteins(2018)16 cited
• An evaluation of miniaturised field asymmetric waveform ion mobility spectrometry hyphenated with time-of-flight mass spectrometry(2014)