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Protein complex analysis: From raw protein lists to protein interaction networks
Author(s) -
Meysman Pieter,
Titeca Kevin,
Eyckerman Sven,
Tavernier Jan,
Goethals Bart,
Martens Lennart,
Valkenborg Dirk,
Laukens Kris
Publication year - 2015
Publication title -
mass spectrometry reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.035
H-Index - 126
eISSN - 1098-2787
pISSN - 0277-7037
DOI - 10.1002/mas.21485
Subject(s) - chemistry , protein–protein interaction , field (mathematics) , filter (signal processing) , selection (genetic algorithm) , computational biology , process (computing) , computer science , biochemical engineering , mass spectrometry , artificial intelligence , chromatography , biochemistry , mathematics , pure mathematics , engineering , computer vision , biology , operating system
The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification—mass spectrometry and other co‐complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull‐down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology‐driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co‐complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600–614, 2017