Affinity-purification coupled to mass spectrometry: basic principles and strategies

Transitioning from concept to application: Comprehensive analytical validation of antibody-based smart sampling

Smart sampling, introduced in 2018, is a technique that integrates crucial sample preparation steps for LC-MS protein analysis directly into the sampling process, improving efficiency. Currently most studies in this area are limited to proof of concept with a brief evaluation. Therefore, our objective is to fully validate the utility of smart samplers for quantitative analysis of bioactive proteins in biological matrices. This study builds on previous work where a paper-based antibody sampler was developed, and human chorionic gonadotropin (hCG) was used as a model protein. hCG is both a biomarker, a drug, and on the doping list for male athletes. Initially, the fabrication of the sampler was optimized, resulting in a reduction of antibody amount per sampler from 50 µg to 5 µg, reducing manufacturing cost while maintaining acceptable performance. It was also shown that removing the sampler before trypsination, but after reduction and alkylation greatly improved signal output while reducing the peptide background, suggesting a cleanser extract was achieved. The optimized smart sampler was validated for hCG in human serum using EMA's ICH M10 guidelines on bioanalytical methods as a guide. Three calibration curves (seven levels) were made in the concentration range of 0.5 ng/mL to 75 ng/mL all displaying excellent linearity (r2 ≥ 0.9955). The accuracy and precision both for the between-run (precision ≤ 10 % CV and accuracy 91-105 % (n = 3)) and within-run (precision ≤ 14 % CV and accuracy 94-105 % (n = 5)) runs were all well within the guideline's acceptance limits. The samplers also showed to be stable for at least ten weeks at room temperature. The optimization and validation of the antibody sampler detailed in this paper marks a notable progression, advancing the concept of "smart sampling" a step closer from the lab to practical application.

Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction

MOTIVATION

Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcity of quality hard negative samples, shortcut learning, and limited generalizability to novel proteins.

RESULTS

In this study, we introduce a novel approach for strategic sampling of protein-protein noninteractions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Next, we introduce Unsupervised Pre-training of Node Attributes tuned for PPI (UPNA-PPI), a high throughput sequence-to-function ML pipeline, integrating unsupervised pre-training in protein representation learning with Topological PPNI (TPPNI) samples, capable of efficiently screening billions of interactions. By using our TPPNI in training the UPNA-PPI model, we improve PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences, strengthening the prioritization of screening assays and facilitating the transferability of ML predictions across protein families and homodimers. UPNA-PPI establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology.

AVAILABILITY AND IMPLEMENTATION

Code and UPNA-PPI predictions are freely available at https://github.com/alxndgb/UPNA-PPI.

A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?

Interpreting variants of uncertain significance (VUS) in mismatch repair (MMR) genes remains a major challenge in managing Lynch syndrome and other hereditary cancer syndromes. This review outlines recommended VUS classification procedures, encompassing foundational and specialized methodologies tailored for MMR genes by expert organizations, including InSiGHT and ClinGen's Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP). Key approaches include: (1) functional data, encompassing direct assays measuring MMR proficiency such as in vitro MMR assays, deep mutational scanning, and MMR cell-based assays, as well as techniques like methylation-tolerant assays, proteomic-based approaches, and RNA sequencing, all of which provide critical functional evidence supporting variant pathogenicity; (2) computational data/tools, including in silico meta-predictors and models, which contribute to robust VUS classification when integrated with experimental evidence; and (3) enhanced variant detection to identify the actual causal variant through whole-genome sequencing and long-read sequencing to detect pathogenic variants missed by traditional methods. These strategies improve diagnostic precision, support clinical decision-making for Lynch syndrome, and establish a flexible framework that can be applied to other OMIM-listed genes.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Navigating the landscape of plant proteomics

In plants, proteins are fundamental to virtually all biological processes, such as photosynthesis, signal transduction, metabolic regulation, and stress responses. Studying protein distribution, function, modifications, and interactions at the cellular and tissue levels is critical for unraveling the complexities of these biological pathways. Protein abundance and localization are highly dynamic and vary widely across the proteome, presenting a challenge for global protein quantification and analysis. Mass spectrometry-based proteomics approaches have proven to be powerful tools for addressing this complex issue. In this review, we summarize recent advancements in proteomics research and their applications in plant biology, with an emphasis on the current state and challenges of studying post-translational modifications, single-cell proteomics, and protein-protein interactions. Additionally, we discuss future prospects for plant proteomics, highlighting potential opportunities that proteomics technologies offer in advancing plant biology research.

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

IP-to-MS: An Unbiased Workflow for Antigen Profiling

Immunoprecipitation is among the most widely utilized methods in biomedical research, with applications that include the identification of antibody targets and associated proteins. The path to identifying these targets is not straightforward, however, and often requires the use of chemical cross-linking and/or gel electrophoresis to separate targets from an overabundance of immunoglobulin protein. Such experiments are labor intensive and often yield long lists of candidate antibody targets. Here, we describe an unbiased immunoprecipitation-to-mass spectrometry (IP-to-MS) method that relies on a novel protein tag to separate low abundance immunoprecipitated proteins from overwhelmingly abundant immunoglobulins. We demonstrate that the IP-to-MS serotyping workflow is highly reproducible and can be used for the identification of novel, patient-specific antigen targets in multiple disease states. Furthermore, we show that IP-to-MS may outperform conventional methods of antibody detection, including enzyme-linked immunosorbent assay, while also enabling patient stratification beyond what is possible with traditional approaches.

Cracking Lysine Crotonylation (Kcr): Enlightening a Promising Post-Translational Modification

Lysine crotonylation (Kcr) is a recently discovered post-translational modification (PTM). Both histone and non-histone Kcr-proteins have been associated with numerous diseases including cancer, acute kidney injury, HIV latency, and cardiovascular disease. Histone Kcr enhances gene expression to a larger extend than the extensively studied lysine acetylation (Kac), suggesting Kcr as a novel potential therapeutic target. Although numerous scientific reports on crotonylation were published in the last years, relevant knowledge gaps concerning this PTM and its regulation still remain. To date, only few selective Kcr-interacting proteins have been identified and selective methods for the enrichment of Kcr-proteins in chemical proteomics analysis are still lacking. The development of new techniques to study this underexplored PTM could then clarify its function in health and disease and hopefully accelerate the development of new therapeutics for Kcr-related disease. Herein we briefly review what is known about the regulation mechanisms of Kcr and the current methods used to identify Kcr-proteins and their interacting partners. This report aims to highlight the significant potential of Kcr as a therapeutic target and to identify the existing scientific gaps that new research must address.

Recent Advances in Mass Spectrometry-Based Protein Interactome Studies

The foundation of all biological processes is the network of diverse and dynamic protein interactions with other molecules in cells known as the interactome. Understanding the interactome is crucial for elucidating molecular mechanisms but has been a longstanding challenge. Recent developments in mass spectrometry (MS)-based techniques, including affinity purification, proximity labeling, cross-linking, and co-fractionation mass spectrometry (MS), have significantly enhanced our abilities to study the interactome. They do so by identifying and quantifying protein interactions yielding profound insights into protein organizations and functions. This review summarizes recent advances in MS-based interactomics, focusing on the development of techniques that capture protein-protein, protein-metabolite, and protein-nucleic acid interactions. Additionally, we discuss how integrated MS-based approaches have been applied to diverse biological samples, focusing on significant discoveries that have leveraged our understanding of cellular functions. Finally, we highlight state-of-the-art bioinformatic approaches for predictions of interactome and complex modeling, as well as strategies for combining experimental interactome data with computation methods, thereby enhancing the ability of MS-based techniques to identify protein interactomes. Indeed, advances in MS technologies and their integrations with computational biology provide new directions and avenues for interactome research, leveraging new insights into mechanisms that govern the molecular architecture of living cells and, thereby, our comprehension of biological processes.

Native MS-guided lipidomics to define endogenous lipid microenvironments of eukaryotic receptors and transporters

The mammalian membrane is composed of various eukaryotic lipids interacting with extensively post-translationally modified proteins. Probing interactions between these mammalian membrane proteins and their diverse and heterogeneous lipid cohort remains challenging. Recently, native mass spectrometry (MS) combined with bottom-up 'omics' approaches has provided valuable information to relate structural and functional lipids to membrane protein assemblies in eukaryotic membranes. Here we provide a step-by-step protocol to identify and provide relative quantification for endogenous lipids bound to mammalian membrane proteins and their complexes. Using native MS to guide our lipidomics strategies, we describe the necessary sample preparation steps, followed by native MS data acquisition, tailored lipidomics and data interpretation. We also highlight considerations for the integration of different levels of information from native MS and lipidomics and how to deal with the various challenges that arise during the experiments. This protocol begins with the preparation of membrane proteins from mammalian cells and tissues for native MS. The results enable not only direct assessment of copurified endogenous lipids but also determination of the apparent affinities of specific lipids. Detailed sample preparation for lipidomics analysis is also covered, along with comprehensive settings for liquid chromatography-MS analysis. This protocol is suitable for the identification and quantification of endogenous lipids, including fatty acids, sterols, glycerolipids, phospholipids and glycolipids and can be used to interrogate proteins from recombinant sources to native membranes.

Isolation and Identification of Nuclear Protein Complexes Using GFP-Tagged Arabidopsis Lines and IP-MS Approach

Immunoprecipitation coupled with mass spectrometry (IP-MS) is a powerful method that enables the identification of protein-protein interactions and isolation of protein complexes. When optimized to work with plant material, it proved to be suitable for the isolation of different types of nuclear complexes from Arabidopsis. Here, we describe a detailed protocol for the isolation of chromatin remodeling and polycomb repressive complexes using Arabidopsis lines that express GFP-tagged proteins as baits, directly from whole-cell extracts or after nuclei enrichment. We describe different experimental variations of the key steps in the protocol and discuss the analysis and interpretation of the IP-MS data.

Metabolic Control of Glycosylation Forms for Establishing Glycan-Dependent Protein Interaction Networks

Protein-protein interactions (PPIs) provide essential insights into the complex molecular mechanisms and signaling pathways within cells that regulate development and disease-related phenotypes. However, for membrane proteins, the impact of various forms of glycosylation has often been overlooked in PPI studies. In this study, we introduce a novel approach, glycan-dependent affinity purification followed by mass spectrometry (GAP-MS), to assess variations in PPIs for any glycoprotein of interest under different glycosylation conditions. As a proof of principle, we selected four glycoproteins-BSG, CD44, EGFR, and SLC3A2-as baits to compare their co-purified partners across five metabolically controlled glycan conditions. The findings demonstrate the capability of GAP-MS to identify PPIs influenced by altered glycosylation states, establishing a foundation for systematically exploring the Glycan-Dependent Protein Interactome (GDPI) for other glycoproteins of interest.

Extraction method combining saponin and trehalose useful for analyzing fragile intermolecular association

Immunoprecipitation (IP) and co-immunoprecipitation (co-IP) are well-established methodologies to analyze protein expression and intermolecular interaction. Composition of extraction and washing buffer for preparing protein is important to accomplish experimental purpose. Various kinds of detergents are included in buffer to adjust extraction efficiency and washing effect. Among them, Triton X-100 (Tx-100), Nonidet P-40 (NP40), deoxycholic acid (DOC) and SDS are generally used according to experimental purpose and characteristic features of protein of interest. In some cases, general detergents disrupt intermolecular interaction and make it impossible to analyze molecular relation of protein of interest with its binding partners. In this study, we propose saponin, a natural detergent, is useful for co-immunoprecipitation when analyzing fragile intermolecular interactions, in which dystrophin and dystroglycan are used as a representative interaction. One of the most notable findings in this report is that intermolecular association between dystrophin and dystroglycan is maintained in saponin buffer whereas general detergents, such as Tx-100, NP40 and DOC, dissociate its binding. Furthermore, supplementation of trehalose, which has been shown to act as a molecular chaperone, facilitates efficient detection of dystrophin-dystroglycan macromolecular complex in co-IP assay. Importantly, the extraction buffer comprising 3 % saponin, 0.5 M trehalose and 0.05 % Tx-100 (we named it STX buffer) is applicable to co-IP for another molecular interaction, N-cadherin and β-catenin, indicating that this methodology can be used for versatile proteins of interest. Thus, STX buffer emerges as an alternative extraction method useful for analyzing fragile intermolecular associations and provides opportunity to identify complex interactomes, which may facilitate proteome-research and functional analysis of proteins of interest.

Answering open questions in biology using spatial genomics and structured methods

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.

Progress in mass spectrometry approaches to profiling protein-protein interactions in the studies of the innate immune system

Understanding protein-protein interactions (PPIs) is pivotal for deciphering the intricacies of biological processes. Dysregulation of PPIs underlies a spectrum of diseases, including cancer, neurodegenerative disorders, and autoimmune conditions, highlighting the imperative of investigating these interactions for therapeutic advancements. This review delves into the realm of mass spectrometry-based techniques for elucidating PPIs and their profound implications in biological research. Mass spectrometry in the PPI research field not only facilitates the evaluation of protein-protein interaction modulators but also discovers unclear molecular mechanisms and sheds light on both on- and off-target effects, thus aiding in drug development. Our discussion navigates through six pivotal techniques: affinity purification mass spectrometry (AP-MS), proximity labeling mass spectrometry (PL-MS), cross-linking mass spectrometry (XL-MS), size exclusion chromatography coupled with mass spectrometry (SEC-MS), limited proteolysis-coupled mass spectrometry (LiP-MS), and thermal proteome profiling (TPP).

Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives

Long non-coding RNAs (lncRNAs) play key roles in various epigenetic and post-transcriptional events in the cell, thereby significantly influencing cellular processes including gene expression, development and diseases such as cancer. Nuclear receptors (NRs) are a family of ligand-regulated transcription factors that typically regulate transcription of genes involved in a broad spectrum of cellular processes, immune responses and in many diseases including cancer. Owing to their many overlapping roles as modulators of gene expression, the paths traversed by lncRNA and NR-mediated signaling often cross each other; these lncRNA-NR cross-talks are being increasingly recognized as important players in many cellular processes and diseases such as cancer. Here, we review the individual roles of lncRNAs and NRs, especially growth factor modulated receptors such as androgen receptors (ARs), in various types of cancers and how the cross-talks between lncRNAs and NRs are involved in cancer progression and metastasis. We discuss the challenges involved in characterizing lncRNA-NR associations and how to overcome them. Furthering our understanding of the mechanisms of lncRNA-NR associations is crucial to realizing their potential as prognostic features, diagnostic biomarkers and therapeutic targets in cancer biology.

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Systematic identification of structure-specific protein-protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

High-Resolution Proteomics Unravel a Native Functional Complex of Cav1.3, SK3, and Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels in Midbrain Dopaminergic Neurons

Pacemaking activity in substantia nigra dopaminergic neurons is generated by the coordinated activity of a variety of distinct somatodendritic voltage- and calcium-gated ion channels. We investigated whether these functional interactions could arise from a common localization in macromolecular complexes where physical proximity would allow for efficient interaction and co-regulations. For that purpose, we immunopurified six ion channel proteins involved in substantia nigra neuron autonomous firing to identify their molecular interactions. The ion channels chosen as bait were Cav1.2, Cav1.3, HCN2, HCN4, Kv4.3, and SK3 channel proteins, and the methods chosen to determine interactions were co-immunoprecipitation analyzed through immunoblot and mass spectrometry as well as proximity ligation assay. A macromolecular complex composed of Cav1.3, HCN, and SK3 channels was unraveled. In addition, novel potential interactions between SK3 channels and sclerosis tuberous complex (Tsc) proteins, inhibitors of mTOR, and between HCN4 channels and the pro-degenerative protein Sarm1 were uncovered. In order to demonstrate the presence of these molecular interactions in situ, we used proximity ligation assay (PLA) imaging on midbrain slices containing the substantia nigra, and we could ascertain the presence of these protein complexes specifically in substantia nigra dopaminergic neurons. Based on the complementary functional role of the ion channels in the macromolecular complex identified, these results suggest that such tight interactions could partly underly the robustness of pacemaking in dopaminergic neurons.

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

Fluorescent Labeling of a Target Protein with an Alkyl Diazirine Photocrosslinker Bearing a Cinnamate Moiety

A novel fluorogenic alkyl diazirine photocrosslinker bearing an o-hydroxycinnamate moiety has been developed for identification of the targets of bioactive molecules. The o-hydroxycinnamate moiety can be converted to the corresponding 7-hydroxycoumarin derivative, which should be created on the interacting site within the photocaptured target protein. The label yield and fluorescence intensity have been immensely improved in comparison with our previous aromatic crosslinkers to facilitate target identification in small quantities.

A Tandem-Affinity Purification Method for Identification of Primary Intracellular Drug-Binding Proteins

In the field of drug discovery, understanding how small molecule drugs interact with cellular components is crucial. Our study introduces a novel methodology to uncover primary drug targets using Tandem Affinity Purification for identification of Drug-Binding Proteins (TAP-DBP). Central to our approach is the generation of a FLAG-hemagglutinin (HA)-tagged chimeric protein featuring the FKBP12(F36V) adaptor protein and the TurboID enzyme. Conjugation of drug molecules with the FKBP12(F36V) ligand allows for the coordinated recruitment of drug-binding partners effectively enabling in-cell TurboID-mediated biotinylation. By employing a tandem affinity purification protocol based on FLAG-immunoprecipitation and streptavidin pulldown, alongside mass spectrometry analysis, TAP-DBP allows for the precise identification of drug-primary binding partners. Overall, this study introduces a systematic, unbiased method for identification of drug-protein interactions, contributing a clear understanding of target engagement and drug selectivity to advance the mode of action of a drug in cells.

Searching for EGF Fragments Recreating the Outer Sphere of the Growth Factor Involved in Receptor Interactions

The aims of this study were to determine whether it is possible to use peptide microarrays obtained using the SPOT technique (immobilized on cellulose) and specific polyclonal antibodies to select fragments that reconstruct the outer sphere of proteins and to ascertain whether the selected peptide fragments can be useful in the study of their protein-protein and/or peptide-protein interactions. Using this approach, epidermal growth factor (EGF) fragments responsible for the interaction with the EGF receptor were searched. A library of EGF fragments immobilized on cellulose was obtained using triazine condensing reagents. Experiments on the interactions with EGFR confirmed the high affinity of the selected peptide fragments. Biological tests on cells showed the lack of cytotoxicity of the EGF fragments. Selected EGF fragments can be used in various areas of medicine.

TurboID-Based Proximity Labeling: A Method to Decipher Protein-Protein Interactions in Plants

Proteins form complex networks through interaction to drive biological processes. Thus, dissecting protein-protein interactions (PPIs) is essential for interpreting cellular processes. To overcome the drawbacks of traditional approaches for analyzing PPIs, enzyme-catalyzed proximity labeling (PL) techniques based on peroxidases or biotin ligases have been developed and successfully utilized in mammalian systems. However, the use of toxic H2O2 in peroxidase-based PL, the requirement of long incubation time (16-24 h), and higher incubation temperature (37 °C) with biotin in BioID-based PL significantly restricted their applications in plants. TurboID-based PL, a recently developed approach, circumvents the limitations of these methods by providing rapid PL of proteins under room temperature. We recently optimized the use of TurboID-based PL in plants and demonstrated that it performs better than BioID in labeling endogenous proteins. Here, we describe a step-by-step protocol for TurboID-based PL in studying PPIs in planta, including Agrobacterium-based transient expression of proteins, biotin treatment, protein extraction, removal of free biotin, quantification, and enrichment of the biotinylated proteins by affinity purification. We describe the PL using plant viral immune receptor N, which belongs to the nucleotide-binding leucine-rich repeat (NLR) class of immune receptors, as a model. The method described could be easily adapted to study PPI networks of other proteins in Nicotiana benthamiana and provides valuable information for future application of TurboID-based PL in other plant species.

Discovery and Characterization of Linear Motif Mediated Protein-Protein Complexes

There are myriads of protein-protein complexes that form within the cell. In addition to classical binding events between globular domains, many protein-protein interactions involve short disordered protein regions. The latter contain so-called linear motifs binding specifically to ordered protein domain surfaces. Linear binding motifs are classified based on their consensus sequence, where only a few amino acids are conserved. In this chapter we will review experimental and in silico techniques that can be used for the discovery and characterization of linear motif mediated protein-protein complexes involved in cellular signaling, protein level and gene expression regulation.

Studying Translesion DNA Synthesis Using Xenopus In Vitro Systems

Cell-free extracts derived from Xenopus eggs have been widely used to decipher molecular pathways involved in several cellular processes including DNA synthesis, the DNA damage response, and genome integrity maintenance. We set out assays using Xenopus cell-free extracts to study translesion DNA synthesis (TLS), a branch of the DNA damage tolerance pathway that allows replication of damaged DNA. Using this system, we were able to recapitulate TLS activities that occur naturally in vivo during early embryogenesis. This chapter describes protocols to detect chromatin-bound TLS factors by western blotting and immunofluorescence microscopy upon induction of DNA damage by UV irradiation, monitor TLS-dependent mutagenesis, and perform proteomic screening.

TurboID-mediated proximity labeling for screening interacting proteins of FIP37 in Arabidopsis

Proximity labeling was recently developed to detect protein-protein interactions and members of subcellular multiprotein structures in living cells. Proximity labeling is conducted by fusing an engineered enzyme with catalytic activity, such as biotin ligase, to a protein of interest (bait protein) to biotinylate adjacent proteins. The biotinylated protein can be purified by streptavidin beads, and identified by mass spectrometry (MS). TurboID is an engineered biotin ligase with high catalytic efficiency, which is used for proximity labeling. Although TurboID-based proximity labeling technology has been successfully established in mammals, its application in plant systems is limited. Here, we report the usage of TurboID for proximity labeling of FIP37, a core member of m6A methyltransferase complex, to identify FIP37 interacting proteins in Arabidopsis thaliana. By analyzing the MS data, we found 214 proteins biotinylated by GFP-TurboID-FIP37 fusion, including five components of m6A methyltransferase complex that have been previously confirmed. Therefore, the identified proteins may include potential proteins directly involved in the m6A pathway or functionally related to m6A-coupled mRNA processing due to spatial proximity. Moreover, we demonstrated the feasibility of proximity labeling technology in plant epitranscriptomics study, thereby expanding the application of this technology to more subjects of plant research.

Higher-Order Structural Organization of the Mitochondrial Proteome Charted by In Situ Cross-Linking Mass Spectrometry

Mitochondria are densely packed with proteins, of which most are involved physically or more transiently in protein-protein interactions (PPIs). Mitochondria host among others all enzymes of the Krebs cycle and the oxidative phosphorylation pathway and are foremost associated with cellular bioenergetics. However, mitochondria are also important contributors to apoptotic cell death and contain their own genome indicating that they play additionally an eminent role in processes beyond bioenergetics. Despite intense efforts in identifying and characterizing mitochondrial protein complexes by structural biology and proteomics techniques, many PPIs have remained elusive. Several of these (membrane embedded) PPIs are less stable in vitro hampering their characterization by most contemporary methods in structural biology. Particularly in these cases, cross-linking mass spectrometry (XL-MS) has proven valuable for the in-depth characterization of mitochondrial protein complexes in situ. Here, we highlight experimental strategies for the analysis of proteome-wide PPIs in mitochondria using XL-MS. We showcase the ability of in situ XL-MS as a tool to map suborganelle interactions and topologies and aid in refining structural models of protein complexes. We describe some of the most recent technological advances in XL-MS that may benefit the in situ characterization of PPIs even further, especially when combined with electron microscopy and structural modeling.

Chemo-proteomics in antimalarial target identification and engagement

Humans have lived in tenuous battle with malaria over millennia. Today, while much of the world is free of the disease, areas of South America, Asia, and Africa still wage this war with substantial impacts on their social and economic development. The threat of widespread resistance to all currently available antimalarial therapies continues to raise concern. Therefore, it is imperative that novel antimalarial chemotypes be developed to populate the pipeline going forward. Phenotypic screening has been responsible for the majority of the new chemotypes emerging in the past few decades. However, this can result in limited information on the molecular target of these compounds which may serve as an unknown variable complicating their progression into clinical development. Target identification and validation is a process that incorporates techniques from a range of different disciplines. Chemical biology and more specifically chemo-proteomics have been heavily utilized for this purpose. This review provides an in-depth summary of the application of chemo-proteomics in antimalarial development. Here we focus particularly on the methodology, practicalities, merits, and limitations of designing these experiments. Together this provides learnings on the future use of chemo-proteomics in antimalarial development.

High-Efficiency Enrichment by Saturating Nanoliters of Protein Affinity Media

Affinity-purification mass spectrometry (AP-MS) is an established technique for identifying protein-protein interactions (PPIs). The basic technology involves immobilizing a high-specificity ligand to a solid-phase support (e.g., an agarose or magnetic bead) to pull down protein(s) of interest from cell lysates. Although these supports are engineered to minimize interactions with background protein, the conventional method recovers mostly nonspecific binders. The law of mass action for dilute solutions has taught us to use an excess of beads to capture all target proteins, especially weakly interacting ones. However, modern microbead technology presents a binding environment that is much different from a dilute solution. We describe a fluidic platform that captures and processes ultralow nanoliter quantities of magnetic particles, simultaneously increasing the efficiency of PPI detection and strongly suppressing nonspecific binding. We demonstrate the concept with synthetic mixtures of tagged protein and illustrate performance with a variety of AP-MS experiment types. These include a BioID experiment targeting lamin-A interactors from HeLa cells and pulldowns using GFP-tagged proteins associated with a double-strand DNA repair mechanism. We show that efficient extraction requires saturation of the solid-phase support and that <10 nL of beads is sufficient to generate comprehensive protein interaction maps.

The development of proximity labeling technology and its applications in mammals, plants, and microorganisms

Protein‒protein, protein‒RNA, and protein‒DNA interaction networks form the basis of cellular regulation and signal transduction, making it crucial to explore these interaction networks to understand complex biological processes. Traditional methods such as affinity purification and yeast two-hybrid assays have been shown to have limitations, as they can only isolate high-affinity molecular interactions under nonphysiological conditions or in vitro. Moreover, these methods have shortcomings for organelle isolation and protein subcellular localization. To address these issues, proximity labeling techniques have been developed. This technology not only overcomes the limitations of traditional methods but also offers unique advantages in studying protein spatial characteristics and molecular interactions within living cells. Currently, this technique not only is indispensable in research on mammalian nucleoprotein interactions but also provides a reliable approach for studying nonmammalian cells, such as plants, parasites and viruses. Given these advantages, this article provides a detailed introduction to the principles of proximity labeling techniques and the development of labeling enzymes. The focus is on summarizing the recent applications of TurboID and miniTurbo in mammals, plants, and microorganisms. Video Abstract.

Smart sampling as the "Spot-on" Method for LC-MS protein analysis from dried blood spots

This perspective explores the feasibility of smart sampling with dried blood spots for the determination of proteins and peptides from human biomatrices using liquid chromatography coupled to mass spectrometry for clinical purposes. The focus is on innovative approaches to transform filter paper from a mere sample carrier to an active element in sample preparation, with the aim of reducing the need for extensive and intensive sample preparation in the conventional sense. Specifically, we discuss the use of modified cellulose to integrate sample preparation at an early stage of sample handling. The use of paper immobilized with either trypsin or monoclonal antibodies for protein digestion and affinity clean-up is discussed as a potential benefit of starting sample preparation instantly at the moment of sampling to optimize time efficiency and enable faster analysis, diagnosis, and follow-up of patients.

In vivo cross-linking-based affinity purification and mass spectrometry for targeting intracellular protein-protein interactions

Comprehensive interactome analysis of targeted proteins is important to understand how proteins work together in regulating functions. Commonly, affinity purification followed by mass spectrometry (AP-MS) has been recognized as the most often used technique for studying protein-protein interactions (PPIs). However, some proteins with weak interactions, which are responsible for key roles in regulation, are easily broken during cell lysis and purification through an AP approach. Herein, we have developed an approach termed in vivo cross-linking-based affinity purification and mass spectrometry (ICAP-MS). By this method, in vivo cross-linking was introduced to covalently fix intracellular PPIs in their functional states to assure all PPIs could be integrally maintained during cell disruption. In addition, the chemically cleavable crosslinkers which were employed enabled unbinding of PPIs for in-depth identification of components within the interactome and biological analysis, while allowing binding of PPIs for cross-linking-mass spectrometry (CXMS)-based direct interaction determination. Multi-level information on targeted PPIs network can be obtained by ICAP-MS, including composition of interacting proteins, as well as direct interacting partners and binding sites. As a proof of concept, the interactome of MAPK3 from 293A cells was profiled with 6.15-fold improvement in identification than by conventional AP-MS. Meanwhile, 184 cross-link site pairs of these PPIs were experimentally identified by CXMS. Furthermore, ICAP-MS was applied in the temporal profiling of MAPK3 interactions under activation by cAMP-mediated pathway. The regulatory manner of MAPK pathways was presented through the quantitative changes of MAPK3 and its interacting proteins at different time points after activation. Therefore, all reported results demonstrated that the ICAP-MS approach may provide comprehensive information on interactome of targeted protein for functional exploration.

A New Cellular Interactome of SARS-CoV-2 Nucleocapsid Protein and Its Biological Implications

There is still much to uncover regarding the molecular details of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. As the most abundant protein, coronavirus nucleocapsid (N) protein encapsidates viral RNAs, serving as the structural component of ribonucleoprotein and virion, and participates in transcription, replication, and host regulations. Virus-host interaction might give clues to better understand how the virus affects or is affected by its host during infection and identify promising therapeutic candidates. Considering the critical roles of N, we here established a new cellular interactome of SARS-CoV-2 N by using a high-specific affinity purification (S-pulldown) assay coupled with quantitative mass spectrometry and immunoblotting validations, uncovering many N-interacting host proteins unreported previously. Bioinformatics analysis revealed that these host factors are mainly involved in translation regulations, viral transcription, RNA processes, stress responses, protein folding and modification, and inflammatory/immune signaling pathways, in line with the supposed actions of N in viral infection. Existing pharmacological cellular targets and the directing drugs were then mined, generating a drug-host protein network. Accordingly, we experimentally identified several small-molecule compounds as novel inhibitors against SARS-CoV-2 replication. Furthermore, a newly identified host factor, DDX1, was verified to interact and colocalize with N mainly by binding to the N-terminal domain of the viral protein. Importantly, loss/gain/reconstitution-of-function experiments showed that DDX1 acts as a potent anti-SARS-CoV-2 host factor, inhibiting the viral replication and protein expression. The N-targeting and anti-SARS-CoV-2 abilities of DDX1 are consistently independent of its ATPase/helicase activity. Further mechanism studies revealed that DDX1 impedes multiple activities of N, including the N-N interaction, N oligomerization, and N-viral RNA binding, thus likely inhibiting viral propagation. These data provide new clues to better depiction of the N-cell interactions and SARS-CoV-2 infection and may help inform the development of new therapeutic candidates.

Proteomic profiling and network biology of colorectal cancer liver metastasis

INTRODUCTION

Tissue-based proteomic studies of colorectal cancer (CRC) metastasis have delivered fragmented results, with very few therapeutic targets and prognostic biomarkers moving beyond the discovery phase. This situation is likely due to the difficulties in obtaining and analyzing large numbers of patient-derived metastatic samples, the own heterogeneity of CRC, and technical limitations in proteomics discovery. As an alternative, metastatic CRC cell lines provide a flexible framework to investigate the underlying mechanisms and network biology of metastasis for target discovery.

AREAS COVERED

In this perspective, we comment on different in-depth proteomic studies of metastatic versus non-metastatic CRC cell lines. Identified metastasis-related proteins are introduced and discussed according to the spatial location in different cellular fractions, with special emphasis on membrane/adhesion proteins, secreted proteins, and nuclear factors, including miRNAs associated with liver metastasis. Moreover, we analyze the biological significance and potential therapeutic applications of the identified liver metastasis-related proteins.

EXPERT OPINION

The combination of protein discovery and functional analysis is the only way to accelerate the progress to clinical translation of the proteomic-derived findings in a relatively fast pace. Patient-derived organoids represent a promising alternative to patient tissues and cell lines, but further optimizations are still required for achieving solid and reproducible results.

Myosin Va, a novel interaction partner of STXBP1, is required to transport Syntaxin1A to the plasma membrane

Syntaxin-binding protein 1 (STXBP1, also known as Munc18-1) regulates exocytosis as a chaperone protein of Syntaxin1A. The haploinsufficiency of STXBP1 causes early infantile-onset developmental and epileptic encephalopathy, known as STXBP1 encephalopathy. Previously, we reported impaired cellular localization of Syntaxin1A in induced pluripotent stem cell-derived neurons from an STXBP1 encephalopathy patient harboring a nonsense mutation. However, the molecular mechanism of abnormal Syntaxin1A localization in the haploinsufficiency of STXBP1 remains unknown. This study aimed to identify the novel interacting partner of STXBP1 involved in transporting Syntaxin1A to the plasma membrane. Affinity purification coupled with mass spectrometry analysis identified a motor protein Myosin Va as a potential binding partner of STXBP1. Co-immunoprecipitation analysis of the synaptosomal fraction from the mouse and tag-fused recombinant proteins revealed that the STXBP1 short splice variant (STXBP1S) interacted with Myosin Va in addition to Syntaxin1A. These proteins colocalized at the tip of the growth cone and axons in primary cultured hippocampal neurons. Furthermore, RNAi-mediated gene silencing in Neuro2a cells showed that STXBP1 and Myosin Va were required for membrane trafficking of Syntaxin1A. In conclusion, this study proposes a potential role of STXBP1 in the trafficking of the presynaptic protein Syntaxin1A to the plasma membrane in conjunction with Myosin Va.

A Combination of Bio-Orthogonal Supramolecular Clicking and Proximity Chemical Tagging as a Supramolecular Tool for Discovery of Putative Proteins Associated with Laminopathic Disease

Protein mutations alter protein-protein interactions that can lead to a number of illnesses. Mutations in lamin A (LMNA) have been reported to cause laminopathies. However, the proteins associated with the LMNA mutation have mostly remained unexplored. Herein, a new chemical tool for proximal proteomics is reported, developed by a combination of proximity chemical tagging and a bio-orthogonal supramolecular latching based on cucurbit[7]uril (CB[7])-based host-guest interactions. As this host-guest interaction acts as a noncovalent clickable motif that can be unclicked on-demand, this new chemical tool is exploited for reliable detection of the proximal proteins of LMNA and its mutant that causes laminopathic dilated cardiomyopathy (DCM). Most importantly, a comparison study reveals, for the first time, mutant-dependent alteration in LMNA proteomic environments, which allows to identify putative laminopathic DCM-linked proteins including FOXJ3 and CELF2. This study demonstrates the feasibility of this chemical tool for reliable proximal proteomics, and its immense potential as a new research platform for discovering biomarkers associated with protein mutation-linked diseases.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

Analysis of affinity purification-related proteomic data for studying protein-protein interaction networks in cells

During intracellular signal transduction, protein-protein interactions (PPIs) facilitate protein complex assembly to regulate protein localization and function, which are critical for numerous cellular events. Over the years, multiple techniques have been developed to characterize PPIs to elucidate roles and regulatory mechanisms of proteins. Among them, the mass spectrometry (MS)-based interactome analysis has been increasing in popularity due to its unbiased and informative manner towards understanding PPI networks. However, with MS instrumentation advancing and yielding more data than ever, the analysis of a large amount of PPI-associated proteomic data to reveal bona fide interacting proteins become challenging. Here, we review the methods and bioinformatic resources that are commonly used in analyzing large interactome-related proteomic data and propose a simple guideline for identifying novel interacting proteins for biological research.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

A toolbox for systematic discovery of stable and transient protein interactors in baker's yeast

Identification of both stable and transient interactions is essential for understanding protein function and regulation. While assessing stable interactions is more straightforward, capturing transient ones is challenging. In recent years, sophisticated tools have emerged to improve transient interactor discovery, with many harnessing the power of evolved biotin ligases for proximity labelling. However, biotinylation-based methods have lagged behind in the model eukaryote, Saccharomyces cerevisiae, possibly due to the presence of several abundant, endogenously biotinylated proteins. In this study, we optimised robust biotin-ligation methodologies in yeast and increased their sensitivity by creating a bespoke technique for downregulating endogenous biotinylation, which we term ABOLISH (Auxin-induced BiOtin LIgase diminiSHing). We used the endoplasmic reticulum insertase complex (EMC) to demonstrate our approaches and uncover new substrates. To make these tools available for systematic probing of both stable and transient interactions, we generated five full-genome collections of strains in which every yeast protein is tagged with each of the tested biotinylation machineries, some on the background of the ABOLISH system. This comprehensive toolkit enables functional interactomics of the entire yeast proteome.

Rapid isolation of respiring skeletal muscle mitochondria using nitrogen cavitation

Methods of isolating mitochondria commonly utilise mechanical force and shear stress to homogenize tissue followed by purification by multiple rounds of ultracentrifugation. Existing protocols can be time-consuming with some physically impairing integrity of the sensitive mitochondrial double membrane. Here, we describe a method for the recovery of intact, respiring mitochondria from murine skeletal muscle tissue and cell lines using nitrogen cavitation. This protocol results in high-yield, pure and respiring mitochondria without the need for purification gradients or ultracentrifugation. The protocol takes under an hour and requires limited specialised equipment. Our methodology is successful in extracting mitochondria of both cell extracts and skeletal muscle tissue. This represents an improved yield in comparison to many of the existing methods. Western blotting and electron microscopy demonstrate the enrichment of mitochondria with their ultrastructure well-preserved and an absence of contamination from cytoplasmic or nuclear fractions. Using respirometry analysis we show that mitochondria extracted from murine skeletal muscle cell lines (C2C12) and tibialis anterior tissue have an appropriate respiratory control ratio. These measures are indicative of healthy coupled mitochondria. Our method successfully demonstrates the rapid isolation of functional mitochondria and will benefit researchers studying mitochondrial bioenergetics as well as providing greater throughput and application for time-sensitive assays.

Studying Cellular Dynamics Using Proximity-Dependent Biotinylation: Somatic Cell Reprogramming

Assessing the reorganization of proteins and organelles following the induction of reprogramming and differentiation programs is crucial to understand the mechanistic underpinning of morphological and fate changes associated with these processes. The advent of proximity-dependent biotinylation (PDB) methods has overcome some of the limitations of biochemical purification methods, enabling proteomic characterization of most subcellular compartments. The first-generation PDB enzyme, the biotin ligase BirA* used in BioID, has now been used in multiple studies determining the cellular context in which proteins reside, typically under standard growth conditions and using long labeling (usually 8-24 h) times. Capitalizing on the generation of more active PDB enzymes such as miniTurbo that can generate strong biotinylation signals in minutes rather than hours, as well as the development of an inducible lentiviral toolkit for BioID, we define here protocols for time-resolved PDB in primary cells. Here, we report the optimization and application of lentivirally delivered miniTurbo constructs to a mouse fibroblast model of somatic cell reprogramming, allowing the study of this dynamic process. This detailed protocol also provides a baseline reference for researchers who wish to adapt these techniques to other dynamic cellular processes.

Tandem Affinity Purification (TAP) of Interacting Prey Proteins with FLAG- and HA-Tagged Bait Proteins

Proteins often interact with each other to form complexes and play functional roles in almost all cellular processes. The study of protein-protein interactions is therefore critical to understand protein function and biological pathways. Affinity Purification coupled with Mass Spectrometry (AP-MS) is an invaluable technique for identifying the interaction partners in protein complexes. In this approach, the protein of interest is fused to an affinity tag, followed by the expression and purification of the fusion protein. The affinity-purified sample is then analyzed by mass spectrometry to identify the interaction partners of the bait proteins. In this chapter, we detail the protocol for tandem affinity purification (TAP) based on the use of the FLAG (a fusion tag with peptide sequence DYKDDDDK) and hemagglutinin (HA) peptide epitopes. The immunoprecipitation using dual-affinity tags offers the advantage of increasing the specificity of the purification with lower nonspecific-background interactions.

Combination of Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and Substrate Trapping for the Detection of Transient Protein Interactions

Antibody-based affinity purification is a recognized method for use in studying protein-protein interactions. There are four different classes of proteins that are typically identified with such affinity purification workflows: bait protein, proteins that specifically interact with the bait protein, proteins nonspecifically associated with the antibody, and proteins that cross-react with the antibody. Mass spectrometry can be used to differentiate these classes of proteins in affinity-purified mixtures. Here we describe the use of stable isotope labeling by amino acids in cell culture, substrate trapping, and mass spectrometry to enable the objective identification of the components of affinity-purified protein complexes.

Identification and Quantification of Affinity-Purified Proteins with MaxQuant, Followed by the Discrimination of Nonspecific Interactions with the CRAPome Interface

Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the "background contaminants" that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.

Protein-Protein Interaction Network Exploration Using Cytoscape

As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.

Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts

Immunoprecipitation mass spectrometry (IP-MS) is a popular method for the identification of protein-protein interactions. This approach is particularly powerful when information is collected without a priori knowledge and has been successively used as a first key step for the elucidation of many complex protein networks. IP-MS consists in the affinity purification of a protein of interest and of its interacting proteins followed by protein identification and quantification by mass spectrometry analysis. We developed an R package, named IPinquiry, dedicated to IP-MS analysis and based on the spectral count quantification method. The main purpose of this package is to provide a simple R pipeline with a limited number of processing steps to facilitate data exploration for biologists. This package allows to perform differential analysis of protein accumulation between two groups of IP experiments, to retrieve protein annotations, to export results, and to create different types of graphics. Here we describe the step-by-step procedure for an interactome analysis using IPinquiry from data loading to result export and plot production.

Affinity-Based Interactome Analysis of Endogenous LINE-1 Macromolecules

During their proliferation and the host's concomitant attempts to suppress it, LINE-1 (L1) retrotransposons give rise to a collection of heterogeneous ribonucleoproteins (RNPs); their protein and RNA compositions remain poorly defined. The constituents of L1-associated macromolecules can differ depending on numerous factors, including, for example, position within the L1 life cycle, whether the macromolecule is productive or under suppression, and the cell type within which the proliferation is occurring. This chapter describes techniques that aid the capture and characterization of protein and RNA components of L1 macromolecules from tissues that natively express them. The protocols described have been applied to embryonal carcinoma cell lines that are popular model systems for L1 molecular biology (e.g., N2102Ep, NTERA-2, and PA-1 cells), as well as colorectal cancer tissues. N2102Ep cells are given as the use case for this chapter; the protocols should be applicable to essentially any tissue exhibiting endogenous L1 expression with minor modifications.