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ComplexFinder: A software package for the analysis of native protein complex fractionation experiments
Hendrik Nolte1, Thomas Langer2
1Max-Planck-Institute for Biology of Ageing, Joseph-Stelzmann Str. 9b, 50931 Cologne, Germany.
Biochimica Et Biophysica Acta. Bioenergetics
|May 3, 2021
Summary
ComplexFinder is a new computational pipeline that uses machine learning to predict protein-protein interactions from complexome profiling data, improving the identification of protein complexes. This method enables peak-centric analysis for better understanding of cellular mechanisms.
Area of Science:
- Proteomics
- Computational Biology
- Biochemistry
Background:
- Protein complex identification is crucial for understanding cellular functions.
- Complexome profiling, combining fractionation and mass spectrometry, offers unbiased protein complex analysis.
- Advances in mass spectrometry necessitate improved computational tools for analyzing large datasets.
Purpose of the Study:
- To develop a novel computational pipeline, ComplexFinder, for analyzing complexome profiling data.
- To enable machine-learning based prediction of protein-protein interactions and protein complex assembly.
- To facilitate peak-centric analysis of complexome profiling data across various quantification strategies.
Main Methods:
- ComplexFinder utilizes a python-based pipeline for machine-learning based prediction of protein-protein interactions.
- Signal profiles are modeled using peak-like ensembles to calculate local similarities.
- Protein connectivity networks are constructed from predicted interactions to assemble protein complexes.
Main Results:
- ComplexFinder enables accurate prediction of protein-protein interactions by incorporating diverse signal profile distance measures.
- The pipeline allows for peak-centric comparison between biological conditions.
- It facilitates the estimation of specific protein complex compositions.
Conclusions:
- ComplexFinder provides a robust computational framework for analyzing complexome profiling data.
- The tool enhances the identification and characterization of protein complexes.
- It supports various quantitative mass spectrometry techniques, including label-free, SILAC, TMT, and pulseSILAC.
