Abstract
Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed which arise from the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and
species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is an analysis tool to automate the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Our results show fast and accurate identification of protein adducts to aid mass spectrometry analysis.
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BibTeX (Download)
@unpublished{Long2023adducthunter,
title = {AdductHunter: Identifying Protein-Metal Complex Adducts in Mass Spectra},
author = {Derek Long and Liam Eade and Katharina Dost and Samuel M Meier-Menches and David C Goldstone and Matthew P Sullivan and Christian Hartinger and J\"{o}rg Wicker and Katerina Taskova},
url = {https://adducthunter.wickerlab.org},
doi = {10.21203/rs.3.rs-3322854/v1},
year = {2023},
date = {2023-05-29},
urldate = {2023-05-29},
abstract = {Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed which arise from the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and
species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is an analysis tool to automate the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Our results show fast and accurate identification of protein adducts to aid mass spectrometry analysis.},
keywords = {cheminformatics, computational sustainability, data mining, dynamic time warping, machine learning, mass spectrometry},
pubstate = {forthcoming},
tppubtype = {unpublished}
}