4. A protein complex database is imported, and it is used for
protein complex decoy generation and as prior knowledge for
complex inference.
5. Complex hypothesis from the database are scored using a target
- decoy approach which results in the statistical assessment of
the likelihood of identification for a particular macromolecular
assembly.
complexFeatures <- findComplexFeatures(traces=protTraces,
complex_hypothesis = corumTargetsPlusDecoys)
6. The resulting complexes can be visualized using the functions
from CCprofiler. CCprofiler provides a fast pipeline for reproducible analysis of protein complexes (see Fig. 3).
plotFeatures(feature_table = complexFeaturesFiltered, traces
= protTraces, feature_id = "193", annotation_label="Entry_name", calibration = calibration, peak_area = TRUE)
Example of the entire R code with additional instructions
used to perform all of the steps described in this section can be
found
at
https://github.com/fossatiA/
MethodsInMolecularBiology/.
4 Expected Outcome and Applications
The proposed procedure facilitates the analysis of large sample
cohorts from native fractionation and enables the acquisition of
proteome-wide protein complex information at an enhanced
throughput of approximately one sample per day. The increased
efficiency and associated reduction in machine time are important
steps toward a differential analysis of proteome organization following different perturbations or to obtain protein complex organization in clinical samples across different patients. Recent studies
in the field of protein co-elution profiling, applying shotgun proteomics or DIA approaches, made a clear case, how profoundly this
method improves our understanding of protein complex organization, especially if applied to study the behavior of protein ensembles
across cellular states [18]. To name only a few recent studies,
SEC-SWATH allowed proteome-wide level profiling of complexes
in different mitotic states [21], and a DDA-based workflow termed
differential fractionation (DIF-FRAC) the identification of RNA
ribonucleoprotein complexes [33].
System-Wide Profiling of Protein Complexes Via Size Exclusion. . .
287
protein complex decoy generation and as prior knowledge for
complex inference.
5. Complex hypothesis from the database are scored using a target
- decoy approach which results in the statistical assessment of
the likelihood of identification for a particular macromolecular
assembly.
complexFeatures <- findComplexFeatures(traces=protTraces,
complex_hypothesis = corumTargetsPlusDecoys)
6. The resulting complexes can be visualized using the functions
from CCprofiler. CCprofiler provides a fast pipeline for reproducible analysis of protein complexes (see Fig. 3).
plotFeatures(feature_table = complexFeaturesFiltered, traces
= protTraces, feature_id = "193", annotation_label="Entry_name", calibration = calibration, peak_area = TRUE)
Example of the entire R code with additional instructions
used to perform all of the steps described in this section can be
found
at
https://github.com/fossatiA/
MethodsInMolecularBiology/.
4 Expected Outcome and Applications
The proposed procedure facilitates the analysis of large sample
cohorts from native fractionation and enables the acquisition of
proteome-wide protein complex information at an enhanced
throughput of approximately one sample per day. The increased
efficiency and associated reduction in machine time are important
steps toward a differential analysis of proteome organization following different perturbations or to obtain protein complex organization in clinical samples across different patients. Recent studies
in the field of protein co-elution profiling, applying shotgun proteomics or DIA approaches, made a clear case, how profoundly this
method improves our understanding of protein complex organization, especially if applied to study the behavior of protein ensembles
across cellular states [18]. To name only a few recent studies,
SEC-SWATH allowed proteome-wide level profiling of complexes
in different mitotic states [21], and a DDA-based workflow termed
differential fractionation (DIF-FRAC) the identification of RNA
ribonucleoprotein complexes [33].
System-Wide Profiling of Protein Complexes Via Size Exclusion. . .
287