Affinity Purification Coupled To Mass Spectrometry Research Articles

A Novel Immuno-Competitive Capture Mass Spectrometry Strategy for Protein–Protein Interaction Profiling Reveals That LATS Kinases Regulate HCV Replication Through NS5A Phosphorylation*

Mapping protein-protein interactions is essential to fully characterize the biological function of a protein and improve our understanding of diseases. Affinity purification coupled to mass spectrometry (AP-MS) using selective antibodies against a target protein has been commonly applied to study protein complexes. However, one major limitation is a lack of specificity as a substantial part of the proposed binders is due to nonspecific interactions. Here, we describe an innovative immuno-competitive capture mass spectrometry (ICC-MS) method to allow systematic investigation of protein-protein interactions. ICC-MS markedly increases the specificity of classical immunoprecipitation (IP) by introducing a competition step between free and capturing antibody prior to IP. Instead of comparing only one experimental sample with a control, the methodology generates a 12-concentration antibody competition profile. Label-free quantitation followed by a robust statistical analysis of the data is then used to extract the cellular interactome of a protein of interest and to filter out background proteins. We applied this new approach to specifically map the interactome of hepatitis C virus (HCV) nonstructural protein 5A (NS5A) in a cellular HCV replication system and uncovered eight new NS5A-interacting protein candidates along with two previously validated binding partners. Follow-up biological validation experiments revealed that large tumor suppressor homolog 1 and 2 (LATS1 and LATS2, respectively), two closely related human protein kinases, are novel host kinases responsible for NS5A phosphorylation at a highly conserved position required for optimal HCV genome replication. These results are the first illustration of the value of ICC-MS for the analysis of endogenous protein complexes to identify biologically relevant protein-protein interactions with high specificity.

CIG-P: Circular Interaction Graph for Proteomics.

BackgroundA typical affinity purification coupled to mass spectrometry (AP-MS) experiment includes the purification of a target protein (bait) using an antibody and subsequent mass spectrometry analysis of all proteins co-purifying with the bait (aka prey proteins). Like any other systems biology approach, AP-MS experiments generate a lot of data and visualization has been challenging, especially when integrating AP-MS experiments with orthogonal datasets.ResultsWe present Circular Interaction Graph for Proteomics (CIG-P), which generates circular diagrams for visually appealing final representation of AP-MS data. Through a Java based GUI, the user inputs experimental and reference data as file in csv format. The resulting circular representation can be manipulated live within the GUI before exporting the diagram as vector graphic in pdf format. The strength of CIG-P is the ability to integrate orthogonal datasets with each other, e.g. affinity purification data of kinase PRPF4B in relation to the functional components of the spliceosome. Further, various AP-MS experiments can be compared to each other.ConclusionsCIG-P aids to present AP-MS data to a wider audience and we envision that the tool finds other applications too, e.g. kinase – substrate relationships as a function of perturbation. CIG-P is available under: http://sourceforge.net/projects/cig-p/Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-344) contains supplementary material, which is available to authorized users.

Incorporating DNA shearing in standard affinity purification allows simultaneous identification of both soluble and chromatin-bound interaction partners

Affinity purification coupled to mass spectrometry (AP-MS) is an effective means of identifying protein-protein interactions to better understand biological functions. However, issues associated with sample preparation still limit the success of AP-MS for specific classes of proteins, including those associated with chromatin that exhibit overall poor solubility in the protocols normally used for AP-MS analysis. Here, we wanted to provide a generally applicable method to simultaneously identify interactors for the chromatin-bound and the soluble fractions of a given bait protein. Using four FLAG-tagged canonical histone proteins (H2A, H2B, H3.1 and H4) we demonstrate that the chromatin solubility issue can be robustly alleviated by fragmenting DNA prior to AP-MS using a combination of sonication and nuclease treatment. We show that - in comparison to a commonly used AP-MS method - our optimized protocol greatly improves the recovery of chromatin-associated interactors for core histones. Critically, this is achieved while preserving the interaction partners associated with the soluble portion of the histones. Detailed protocols amenable to the study of both histone and non-histone baits are presented here. This manuscript describes workflow improvements to enable the recovery of chromatin-bound interactors by affinity purification coupled to mass spectrometry (AP-MS). This is significant, as most of the high-throughput studies to date can only monitor protein-protein interactions for soluble (not bound to chromatin) components. By consequence, we still poorly understand how protein complexes form on chromatin, which greatly hampers our understanding of gene expression. Using core histones as test cases, we show here a simple and universally applicable workflow that permits the identification of chromatin-bound protein-protein interactions. As exemplified in our manuscript, this revised protocol should result in a much deeper understanding of chromatin biology. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Defining dynamic protein interactions using SILAC-based quantitative mass spectrometry.

Protein-protein interactions are essential to various physiological processes in living cells. A full characterization of protein interactions is critical to our understanding of their roles in the regulation of protein functions. Affinity purification coupled with mass spectrometry (AP-MS) has become one of the most effective approaches to systematically study protein-protein interactions. In combination with quantitative mass spectrometry, specific interacting proteins can be efficiently distinguished from nonspecific background proteins. Based on interaction affinity and kinetics, protein interactions can be classified into different categories such as stable and dynamic interactions. Standard biochemical methods are effective in capturing and identifying stable protein interactions but are not sufficient enough to identify dynamic interactors. In this chapter, we describe integrated strategies to allow the identification of dynamic interactors of protein complexes by incorporating new sample preparation methods with SILAC-based quantitation.

The CRAPome: a contaminant repository for affinity purification-mass spectrometry data.

Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.

Mass spectrometry supported determination of protein complex structure

Virtually all the biological processes are controlled and catalyzed by proteins which are, in many cases, in complexes with other proteins. Therefore, understanding the architecture and structure of protein complexes is critical to understanding their biological role and function. Traditionally, high-resolution data for structural analysis of proteins or protein complexes have been generated by the powerful methods of X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. More recently, mass spectrometry (MS)-based methods have been developed that provide low-resolution structural information, which contributes to the determination of the native structure of protein complexes that have remained refractory to the high-resolution methods. Native MS and affinity purification coupled with MS (AP-MS) have been used to characterize the composition, stoichiometry and connectivity of protein complexes. Chemical cross-linking MS (CX-MS) provides protein-protein interaction data supplemented with distance information that indicates residues that are in close spatial proximity in the native protein structure. Hydrogen-deuterium exchange combined with MS has been used to map protein-protein binding sites. Here, we focus on recent developments in CX-MS and native MS and their application to challenging problems in structural biology.

Using ProHits to store, annotate, and analyze affinity purification-mass spectrometry (AP-MS) data.

Affinity purification coupled with mass spectrometry (AP-MS) is a robust technique used to identify protein-protein interactions. With recent improvements in sample preparation, and dramatic advances in MS instrumentation speed and sensitivity, this technique is becoming more widely used throughout the scientific community. To meet the needs of research groups both large and small, we have developed software solutions for tracking, scoring and analyzing AP-MS data. Here, we provide details for the installation and utilization of ProHits, a Laboratory Information Management System designed specifically for AP-MS interaction proteomics. This protocol explains: (i) how to install the complete ProHits system, including modules for the management of mass spectrometry files and the analysis of interaction data, and (ii) alternative options for the use of pre-existing search results in simpler versions of ProHits, including a virtual machine implementation of our ProHits Lite software. We also describe how to use the main features of the software to analyze AP-MS data.

AN INTEGRATED EXPERIMENTAL WORKFLOW TO INCREASE THROUGHPUT AND DATA ROBUSTNESS FOR ANALYSIS OF MAMMALIAN PROTEIN INTERACTION NETWORKS

To understand the organization of protein complexes and how biological information is propagated via protein‐protein interaction is becoming increasingly important to get new functional insights on cellular processes. Affinity purification coupled to mass spectrometry (AP‐MS) has been successfully used in the past to characterize protein complexes. However, mainly due to significant experimental limitations the success and power of the AP‐MS strategy has been underrated to date. Here, we describe an integrated workflow for characterization of mammalian protein complexes. The experimental pipeline comprises rapid generation of isogenic mammalian cell lines, protein complex analysis using an efficient double affinity purification strategy followed by state‐of‐the‐art nano‐LC‐Orbitrap‐MS analysis.We use this pipeline to study interactions associated with BMP signaling systems. In particular we focus on ACTR‐1/Alk2 and FKBP12; two proteins intimately linked to Fibrodysplasia Ossificans Progressiva (FOP) a catastrophic human disorder leading to heterotopic ossification throughout the body.The high performance of our strategy is well suited to identifying protein interactions partners that can subsequently be used as (1) entry points for focused research projects and (2) to more globally map out whole signaling systems linked to important biological processes.

A cost–benefit analysis of multidimensional fractionation of affinity purification‐mass spectrometry samples

Affinity purification coupled to mass spectrometry (AP-MS) is gaining widespread use for the identification of protein-protein interactions. It is unclear, however, whether typical AP sample complexity is limiting for the identification of all protein components using standard one-dimensional LC-MS/MS. Multidimensional sample separation is useful for reducing sample complexity prior to MS analysis and increases peptide and protein coverage of complex samples. Here, we monitored the effects of upstream protein or peptide separation techniques on typical mammalian AP-MS samples, generated by FLAG affinity purification of four baits with different biological functions and/or subcellular distribution. As a first separation step, we employed SDS-PAGE, strong cation exchange LC, or reversed-phase LC at basic pH. We also analyzed the benefits of using an instrument with a faster scan rate, the new TripleTOF 5600 mass spectrometer. While all multidimensional approaches yielded a clear increase in spectral counts, the increase in unique peptides and additional protein identification was modest and came at the cost of increased instrument and handling time. The use of a high duty-cycle instrument achieved similar benefits without these drawbacks. An increase in spectral counts is beneficial when data analysis methods relying on spectral counts, including Significance Analysis of INTeractome (SAINT), are used.

AN INTEGRATED EXPERIMENTAL WORKFLOW TO INCREASE THROUGHPUT AND DATA ROBUSTNESS FOR ANALYSIS OF MAMMALIAN PROTEIN INTERACTION NETWORKS

The analysis of protein complexes and how biological information is propagated via protein‐protein interactions is becoming increasingly important to obtain new insights into cellular signaling processes. Affinity purification coupled with mass spectrometry (AP‐MS) has been successfully used in the past to characterize protein complexes. However, mainly due to experimental limitations, the success of the AP‐MS strategy has been somewhat limited to date. Here, we describe an integrated workflow for characterizing mammalian protein complexes. The experimental pipeline comprises rapid generation of isogenic mammalian cell lines expressing a bait protein of interest, protein complex analysis using an efficient double affinity purification strategy and state‐of‐the‐art nano‐LC‐Orbitrap‐MS analysis. We have used this workflow to study interactions associated with various signaling pathways, including the NF‐kB and BMPR pathways. We demonstrate that the improved AP‐MS workflow identifies hitherto unknown protein interactions and can be used to dissect interactions involving membrane bound receptors and associated signaling proteins. The high performance of our strategy is well suited to identifying protein interaction partners that can subsequently be used as entry points for focused research projects and to globally map whole signaling systems linked to important biological processes.

Label‐free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5

Affinity purification coupled to mass spectrometry (AP-MS) represents a powerful and proven approach for the analysis of protein-protein interactions. However, the detection of true interactions for proteins that are commonly considered background contaminants is currently a limitation of AP-MS. Here using spectral counts and the new statistical tool, Significance Analysis of INTeractome (SAINT), true interaction between the serine/threonine protein phosphatase 5 (PP5) and a chaperonin, heat shock protein 90 (Hsp90), is discerned. Furthermore, we report and validate a new interaction between PP5 and an Hsp90 adaptor protein, stress-induced phosphoprotein 1 (STIP1; HOP). Mutation of PP5, replacing key basic amino acids (K97A and R101A) in the tetratricopeptide repeat (TPR) region known to be necessary for the interactions with Hsp90, abolished both the known interaction of PP5 with cell division cycle 37 homolog and the novel interaction of PP5 with stress-induced phosphoprotein 1. Taken together, the results presented demonstrate the usefulness of label-free quantitative proteomics and statistical tools to discriminate between noise and true interactions, even for proteins normally considered as background contaminants.

SAINT: probabilistic scoring of affinity purification-mass spectrometry data.

We present SAINT (Significance Analysis of INTeractome), a computational tool that assigns confidence scores to protein-protein interaction data generated using affinity-purification coupled to mass spectrometry (AP-MS). The method utilizes label-free quantitative data and constructs separate distributions for true and false interactions to derive the probability of a bona fide protein-protein interaction. We demonstrate that SAINT is applicable to data of different scales and protein connectivity and allows for the transparent analysis of AP-MS data.

The Protein Interaction Network of the Human Transcription Machinery Reveals a Role for the Conserved GTPase RPAP4/GPN1 and Microtubule Assembly in Nuclear Import and Biogenesis of RNA Polymerase II

RNA polymerase II (RNAPII), the 12-subunit enzyme that synthesizes all mRNAs and several non-coding RNAs in eukaryotes, plays a central role in cell function. Although multiple proteins are known to regulate the activity of RNAPII during transcription, little is known about the machinery that controls the fate of the enzyme before or after transcription. We used systematic protein affinity purification coupled to mass spectrometry (AP-MS) to characterize the high resolution network of protein interactions of RNAPII in the soluble fraction of human cell extracts. Our analysis revealed that many components of this network participate in RNAPII biogenesis. We show here that RNAPII-associated protein 4 (RPAP4/GPN1) shuttles between the nucleus and the cytoplasm and regulates nuclear import of POLR2A/RPB1 and POLR2B/RPB2, the two largest subunits of RNAPII. RPAP4/GPN1 is a member of a newly discovered GTPase family that contains a unique and highly conserved GPN loop motif that we show is essential, in conjunction with its GTP-binding motifs, for nuclear localization of POLR2A/RPB1 in a process that also requires microtubule assembly. A model for RNAPII biogenesis is presented.

AN INTEGRATED EXPERIMENTAL WORKFLOW TO INCREASE THROUGHPUT AND DATA ROBUSTNESS FOR ANALYSIS OF MAMMALIAN PROTEIN INTERACTION NETWORKS

To understand the organization of protein complexes and how biological information is propagated via protein‐protein interaction is becoming increasingly important to get new functional insights on cellular processes. Affinity purification coupled to mass spectrometry (AP‐MS) has been successfully used in the past to characterize protein complexes. However, mainly due to significant experimental limitations the success and power of the AP‐MS strategy has been underrated to date. Here, we describe an integrated workflow for characterization of mammalian protein complexes. The experimental pipeline comprises rapid generation of isogenic mammalian cell lines, protein complex analysis using an efficient double affinity purification strategy followed by state‐of‐the‐art nano‐LC‐Orbitrap‐MS analysis. We have used the system to study interactions associated with the human protein phosphatase 2A and NF‐kB systems. Analysis of these systems revealed new components of protein network structures that are linked to a number of important biological processes such as transcription, the cell proliferation, apoptosis and inflammation. The high performance of our strategy is well suited to identifying protein interactions partners that can subsequently be used as (1) entry points for focused research projects and (2) to more globally map out whole signaling systems linked to important biological processes.

High-resolution mapping of the protein interaction network for the human transcription machinery and affinity purification of RNA polymerase II-associated complexes

Thirty years of research on gene transcription has uncovered a myriad of factors that regulate, directly or indirectly, the activity of RNA polymerase II (RNAPII) during mRNA synthesis. Yet many regulatory factors remain to be discovered. Using protein affinity purification coupled to mass spectrometry (AP-MS), we recently unraveled a high-density interaction network formed by RNAPII and its accessory factors from the soluble fraction of human cell extracts. Validation of the dataset using a machine learning approach trained to minimize the rate of false positives and false negatives yielded a high-confidence dataset and uncovered novel interactors that regulate the RNAPII transcription machinery, including a new protein assembly we named the RNAPII-Associated Protein 3 (RPAP3) complex.

A PP2A Phosphatase High Density Interaction Network Identifies a Novel Striatin-interacting Phosphatase and Kinase Complex Linked to the Cerebral Cavernous Malformation 3 (CCM3) Protein

The serine/threonine protein phosphatases are targeted to specific subcellular locations and substrates in part via interactions with a wide variety of regulatory proteins. Understanding these interactions is thus critical to understanding phosphatase function. Using an iterative affinity purification/mass spectrometry approach, we generated a high density interaction map surrounding the protein phosphatase 2A catalytic subunit. This approach recapitulated the assembly of the PP2A catalytic subunit into many different trimeric complexes but also revealed several new protein-protein interactions. Here we define a novel large multiprotein assembly, referred to as the striatin-interacting phosphatase and kinase (STRIPAK) complex. STRIPAK contains the PP2A catalytic (PP2Ac) and scaffolding (PP2A A) subunits, the striatins (PP2A regulatory B''' subunits), the striatin-associated protein Mob3, the novel proteins STRIP1 and STRIP2 (formerly FAM40A and FAM40B), the cerebral cavernous malformation 3 (CCM3) protein, and members of the germinal center kinase III family of Ste20 kinases. Although the function of the CCM3 protein is unknown, the CCM3 gene is mutated in familial cerebral cavernous malformations, a condition associated with seizures and strokes. Our proteomics survey indicates that a large portion of the CCM3 protein resides within the STRIPAK complex, opening the way for further studies of CCM3 biology. The STRIPAK assembly establishes mutually exclusive interactions with either the CTTNBP2 proteins (which interact with the cytoskeletal protein cortactin) or a second subcomplex consisting of the sarcolemmal membrane-associated protein (SLMAP) and the related coiled-coil proteins suppressor of IKKepsilon (SIKE) and FGFR1OP2. We have thus identified several novel PP2A-containing protein complexes, including a large assembly linking kinases and phosphatases to a gene mutated in human disease.

An integrated workflow for charting the human interaction proteome: insights into the PP2A system

Protein complexes represent major functional units for the execution of biological processes. Systematic affinity purification coupled with mass spectrometry (AP-MS) yielded a wealth of information on the compendium of protein complexes expressed in Saccharomyces cerevisiae. However, global AP-MS analysis of human protein complexes is hampered by the low throughput, sensitivity and data robustness of existing procedures, which limit its application for systems biology research. Here, we address these limitations by a novel integrated method, which we applied and benchmarked for the human protein phosphatase 2A system. We identified a total of 197 protein interactions with high reproducibility, showing the coexistence of distinct classes of phosphatase complexes that are linked to proteins implicated in mitosis, cell signalling, DNA damage control and more. These results show that the presented analytical process will substantially advance throughput and reproducibility in future systematic AP-MS studies on human protein complexes.

Predicting co-complexed protein pairs from heterogeneous data.

Proteins do not carry out their functions alone. Instead, they often act by participating in macromolecular complexes and play different functional roles depending on the other members of the complex. It is therefore interesting to identify co-complex relationships. Although protein complexes can be identified in a high-throughput manner by experimental technologies such as affinity purification coupled with mass spectrometry (APMS), these large-scale datasets often suffer from high false positive and false negative rates. Here, we present a computational method that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources. We show that a diffusion kernel based on random walks on the full network topology yields good performance in predicting CCPPs from protein interaction networks. In the setting of direct ranking, a diffusion kernel performs much better than the mutual clustering coefficient. In the setting of SVM classifiers, a diffusion kernel performs much better than a linear kernel. We also show that combination of complementary information improves the performance of our CCPP recognizer. A summation of three diffusion kernels based on two-hybrid, APMS, and genetic interaction networks and three sequence kernels achieves better performance than the sequence kernels or diffusion kernels alone. Inclusion of additional features achieves a still better ROC50 of 0.937. Assuming a negative-to-positive ratio of 600∶1, the final classifier achieves 89.3% coverage at an estimated false discovery rate of 10%. Finally, we applied our prediction method to two recently described APMS datasets. We find that our predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that our method successfully identifies true CCPPs. An SVM classifier trained from heterogeneous data sources provides accurate predictions of CCPPs in yeast. This computational method thereby provides an inexpensive method for identifying protein complexes that extends and complements high-throughput experimental data.

Predicting co-complexed protein pairs using genomic and proteomic data integration

BackgroundIdentifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship.ResultsUsing a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP) of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue), a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database), and the remaining predictions may potentially represent unknown CCPs.ConclusionsWe demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP) pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.