Novel computational pipeline to conceptually change the paradigm of protein complex prediction and differential analysis using proteomic data

Two of the co-leads on this research (L-R) Prof Ruedi Aebersold, ETH Zürich and Dr Chen Li, Monash BDI. (Photo by Silas Krämer at ETH Zürich)

An international research team co-led by Dr Chen Li from the Biomedicine Discovery Institute (BDI) at Monash University has published a landmark study in prediction and computational analysis of protein complexes using co-fractionation mass spectrometry (MS) data.

The computational framework, termed PCprophet, has been published in Nature Methods and is the result of a close international collaboration co-led by Monash University (Australia) and ETH Zürich (Switzerland), and collaborators from seven other international research institutes.

Proteins usually function as large assemblies to perform critical and fundamental biological functions. Recent research advances have highlighted the significance of MS techniques in quantification and identification of protein complexes. Specifically, analytical techniques such as size exclusion chromatography (SEC) and ion exchange chromatography (IEX) have demonstrated their abilities for protein complex identification. A key challenge in fractionation-based approaches is to accurately assign the protein subunits to complexes based on proteins’ co-elution/co-fractionation patterns and other relevant biological information. In light of this, a computational pipeline, PCprophet, has been proposed to accurately predict protein complexes and conduct differential analysis in terms of complex abundance and composition across different conditions.

“For the first time, PCprophet is able to perform complex-centric prediction directly from any co-fractionation dataset. Compared to traditional methods which conclude protein complexes from protein-protein interaction (PPI) MS data and the resulting networks, PCprophet learns directly from the carefully designed features depicting complex coelution profiles and is therefore able to avoid the potential amplified PPI network noise induced by clustering algorithms. In addition, a Bayesian factor-based analytical method was proposed to capture the abundance and compositional alterations of predicted protein complexes across different conditions, highlighting its potential to identify both protein- and complex-level disruptions in disease and healthy samples,” says Dr Chen Li, who co-led the study with Prof Ruedi Aebersold and Dr Matthias Gstaiger from ETH Zürich.

“This study demonstrates an excellent example of how international collaboration and effort led to the construction of this pipeline and experimental validation for novel PPIs predicted, which has conceptually changed the traditional paradigm of predicting and analysing protein complexes using MS proteomic data,” says Dr Li.

Collaborators

  • Dr Chen Li, A/Prof Jiangning Song, Prof Anthony Purcell from the Monash BDI
  • Dr Andrea Fossati from ETH Zürich, Switzerland and University of California, San Francisco, USA
  • Prof Ruedi Aebersold, Dr Matthias Gstaiger, Dr Federico Uliana, Dr Fabian Wendt, Dr Fabian Frommelt and Prof Bernd Wollscheid from ETH Zürich, Switzerland.
  • Dr Peter Sykacek from University of Natural Resources and Life Sciences, Austria
  • Dr Moritz Heusel from Lund University, Sweden
  • Dr Mahmoud Hallal from University of Bern, Switzerland
  • Dr Isabell Bludau from Max Planck Institute of Biochemistry, Germany
  • Tümay Capraz from European Molecular Biology Laboratory, Germany
  • Dr Peng Xue from Chinese Academy of Sciences, China

This work was supported by the National Health and Medicine Research Council (NHMRC) CJ Martin Early Career Fellowship for Dr Chen Li.

PCprophet package is freely accessible at https://github.com/anfoss/PCprophet.

Read the full paper in Nature Methods titled: PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

DOI: 10.1038/s41592-021-01107-5


About the Monash Biomedicine Discovery Institute at Monash University

Committed to making the discoveries that will relieve the future burden of disease, the newly established Monash Biomedicine Discovery Institute at Monash University brings together more than 120 internationally-renowned research teams. Spanning six discovery programs across Cancer, Cardiovascular Disease, Development and Stem Cells, Infection and Immunity, Metabolism, Diabetes and Obesity, and Neuroscience, Monash BDI is one of the largest biomedical research institutes in Australia.  Our researchers are supported by world-class technology and infrastructure, and partner with industry, clinicians and researchers internationally to enhance lives through discovery.

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