Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes

Abstract

Co-fractionation mass spectrometry (CF-MS) is a technique with potential to characterise endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS datasets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending ‘Guilt-by-Association’ by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution dataset, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies – Spearman and Kendall correlations – and the recently introduced Co-apex metric frequently maximise recall, while a popular metric – Euclidean distance – delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying non-model organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimise false discovery. These assessments are summarised in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.

Closing the protein gap in plant chronobiology

“…Recent developments, however, have made extensive CF‐MS experimentation more practical. This includes the development of analytical guidelines (Pang et al ., 2020) and a new complex‐centric experimental approach (Bludau et al ., 2020; Heusel et al ., 2019; Ting et al ., 2015).…”

Native Size-Exclusion Chromatography–Based Mass Spectrometry Reveals New Components of the Early Heat Shock Protein 90 Inhibition Response Among Limited Global Changes

“…With more studies employing SEC-MS and other cofractionation-based MS approaches, we expect our dataset ( e.g. , supplemental Table S2 ) to continue being of use as newer analysis pipelines, software, and best-practices are developed ( 121 , 122 , 123 ). Another useful future comparison would be SEC-MS with other clinically pursued HSP90 inhibitors—especially those that bind HSP90 outside of its N-terminal ATPase pocket, such as C-terminal domain binders of the novobiocin family ( 124 ) or covalent inhibitors targeting cysteine residues ( 125 , 126 ).…”

Native size exclusion chromatography-based mass spectrometry (SEC-MS) identifies novel components of the Heat Shock Protein 90-dependent proteome

“…With more studies employing SEC-MS and other cofractionation-based MS approaches, we expect our dataset (e.g., Table S2) to continue being of use as newer analysis pipelines, software, and best-practices are developed (Schlossarek et al, 2021; Pang et al, 2020; Serwetnyk and Blagg, 2021). We also hope our study lays the foundation for other cuttingedge mass spectrometric approaches that increase resolution of treatment-induced changes to protein complexes or organellar proteomes, including SEC-SWATH-MS (Heusel et al, 2020), the various LOPIT techniques (Geladaki et al, 2019; Mulvey et al, 2017), and Dynamic Organellar Maps (Itzhak et al, 2016).…”