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Moving Profiling Spatial Proteomics Beyond Discrete Classification
Author(s) -
Crook Oliver M.,
Smith Tom,
Elzek Mohamed,
Lilley Kathryn S.
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900392
Subject(s) - proteomics , profiling (computer programming) , computer science , proteome , bayesian probability , computational biology , artificial intelligence , bioinformatics , biology , biochemistry , gene , operating system
The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post‐translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi‐localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi‐localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.