Abstract
Affinity purification followed by mass spectrometry (AP-MS) has become a commonly used method for the identification of protein-protein interactions and protein complexes. We will start with a review of the most commonly used experimental AP-MS workflows, with an emphasis on the experimental design and data analysis challenges typically encountered in such studies. One of the foremost challenges of interactome mapping is a large number of false positive protein interactions present in unfiltered datasets. We will review computational and informatics strategies for detecting specific protein interaction partners in AP-MS experiments, with a focus on incomplete (as opposite to genome-wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or peptide ion intensities that can be extracted from MS data. We will discuss in more detail the current state of the computational tool SAINT developed in our lab. We will present its extension to intensity-based data, and compare the two quantitative strategies (spectral counts and intensities) in the context of AP-MS studies. We will also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. We then present a new resource – the Contaminant Repository for Affinity Purification – a central repository to store, annotate, statistically analyze and disseminate lists of background contaminants likely to be observed in AP-MS studies. We will show how the contaminant repository, coupled with statistical scoring tools such as SAINT, can significantly improve the ability of individual researchers, especially in small-scale studies, to filter out likely false interactions based on the analysis of protein abundance profiles across multiple control experiments annotated in the repository.