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Metabolite collision cross section prediction without energy-minimized structures.

ABSTRACT: Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 Å2 (median error of ?2%) using linear methods accesible to most researchers. A comparison on the performance of 2D vs. 3D molecular descriptors for the purposes of CCS prediction is also presented for the first time, enabling CCS prediction without a priori knowledge of the metabolite's energy-minimized structure.

SUBMITTER: Soper-Hopper MT

PROVIDER: S-EPMC7423765 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Metabolite collision cross section prediction without energy-minimized structures.

Soper-Hopper M T MT Vandegrift J J Baker E S ES Fernández F M FM

The Analyst 20200625 16

Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 Å<sup>2</sup> (median error of ∼2%) using linear methods accesible to most researchers. A comparison on the performance ...[more]

PMID: 32583823

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