Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Advances in crosslinking chemistry and proximity-enabled strategies: deciphering protein complexes and interactions

Mass spectrometry, coupled with innovative crosslinking techniques to decode protein conformations and interactions through uninterrupted signal connections, has undergone remarkable progress in recent years. It is crucial to develop selective crosslinking reagents that minimally disrupt protein structure and dynamics, providing insights into protein network regulation and biological functions. Compared to traditional crosslinkers, new bifunctional chemical crosslinkers exhibit high selectivity and specificity in connecting proximal amino acid residues, resulting in stable molecular crosslinked products. The conjugation with specific amino acid residues like lysine, cysteine, arginine and tyrosine expands the XL-MS toolbox, enabling more precise modeling of target substrates and leading to improved data quality and reliability. Another emerging crosslinking method utilizes unnatural amino acids (UAAs) derived from proximity-enabled reactivity with specific amino acids or sulfur-fluoride exchange (SuFEx) reactions with nucleophilic residues. These UAAs are genetically encoded into proteins for the formation of specific covalent bonds. This technique combines the benefits of genetic encoding for live cell compatibility with chemical crosslinking, providing a valuable method for capturing transient and weak protein-protein interactions (PPIs) for mapping PPI coordinates and improving the pharmacological properties of proteins. With continued advancements in technology and applications, crosslinking mass spectrometry is poised to play an increasingly significant role in guiding our understanding of protein dynamics and function in the future.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

A Multiplexed SEC-MS Approach to Systematically Study the Interplay Between Protein Assembly-States and Phosphorylation Events

Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Global Interactome Mapping Reveals Pro-tumorigenic Interactions of NF-κB in Breast Cancer

NF-κB pathway is involved in inflammation; however, recent data shows its role also in cancer development and progression, including metastasis. To understand the role of NF-κB interactome dynamics in cancer, we study the complexity of breast cancer interactome in luminal A breast cancer model and its rearrangement associated with NF-κB modulation. Liquid chromatography-mass spectrometry measurement of 160 size-exclusion chromatography fractions identifies 5460 protein groups. Seven thousand five hundred sixty eight interactions among these proteins have been reconstructed by PrInCE algorithm, of which 2564 have been validated in independent datasets. NF-κB modulation leads to rearrangement of protein complexes involved in NF-κB signaling and immune response, cell cycle regulation, and DNA replication. Central NF-κB transcription regulator RELA co-elutes with interactors of NF-κB activator PRMT5, and these complexes are confirmed by AlphaPulldown prediction. A complementary immunoprecipitation experiment recapitulates RELA interactions with other NF-κB factors, associating NF-κB inhibition with lower binding of NF-κB activators to RELA. This study describes a network of pro-tumorigenic protein interactions and their rearrangement upon NF-κB inhibition with potential therapeutic implications in tumors with high NF-κB activity.

DIP-MS: ultra-deep interaction proteomics for the deconvolution of protein complexes

Most proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing most cellular processes. Affinity purification of the protein of interest combined with liquid chromatography coupled to tandem mass spectrometry (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can, however, not be resolved from a single AP-MS experiment but requires computational inference from multiple time- and resource-intensive reciprocal AP-MS experiments. Here we introduce deep interactome profiling by mass spectrometry (DIP-MS), which combines AP with blue-native-PAGE separation, data-independent acquisition with mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organization of the human prefoldin family of complexes, resolving distinct prefoldin holo- and subcomplex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution.

Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts

Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

Next-generation proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions are pivotal in regulating establishment, progression, and outcome of an infection. While affinity-purification mass spectrometry has become instrumental in characterizing such interactions, it suffers from limitations in scalability and biological authenticity. Here we present the use of co-fractionation mass spectrometry for high throughput analysis of host-pathogen interactions from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This approach enabled the detection of > 6000 unique host-pathogen interactions for each phage, encompassing > 50% of their respective proteomes. This deep coverage provided evidence for interactions between KZ-like phage proteins and the host ribosome, and revealed protein complexes for previously undescribed phage ORFs, including a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ non-virion RNA polymerase. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of host-pathogen interactomes and protein complex dynamics upon infection.

A feature extraction free approach for protein interactome inference from co-elution data

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Proteomic applications in identifying protein-protein interactions

There are many things that can be used to characterize a protein. Size, isoelectric point, hydrophobicity, structure (primary to quaternary), and subcellular location are just a few parameters that are used. The most important feature of a protein, however, is its function. While there are many experiments that can indicate a protein's role, identifying the molecules it interacts with is probably the most definitive way of determining its function. Owing to technology limitations, protein interactions have historically been identified on a one molecule per experiment basis. The advent of high throughput multiplexed proteomic technologies in the 1990s, however, made identifying hundreds and thousands of proteins interactions within single experiments feasible. These proteomic technologies have dramatically increased the rate at which protein-protein interactions (PPIs) are discovered. While the improvement in mass spectrometry technology was an early driving force in the rapid pace of identifying PPIs, advances in sample preparation and chromatography have recently been propelling the field. In this chapter, we will discuss the importance of identifying PPIs and describe current state-of-the-art technologies that demonstrate what is currently possible in this important area of biological research.

Proteomic discovery of chemical probes that perturb protein complexes in human cells

Most human proteins lack chemical probes, and several large-scale and generalizable small-molecule binding assays have been introduced to address this problem. How compounds discovered in such "binding-first" assays affect protein function, nonetheless, often remains unclear. Here, we describe a "function-first" proteomic strategy that uses size exclusion chromatography (SEC) to assess the global impact of electrophilic compounds on protein complexes in human cells. Integrating the SEC data with cysteine-directed activity-based protein profiling identifies changes in protein-protein interactions that are caused by site-specific liganding events, including the stereoselective engagement of cysteines in PSME1 and SF3B1 that disrupt the PA28 proteasome regulatory complex and stabilize a dynamic state of the spliceosome, respectively. Our findings thus show how multidimensional proteomic analysis of focused libraries of electrophilic compounds can expedite the discovery of chemical probes with site-specific functional effects on protein complexes in human cells.

Meta-analysis of RNA-Seq datasets highlights novel genes/pathways involved in fat deposition in fat-tail of sheep

Introduction

Fat-tail in sheep is considered as an important energy reservoir to provide energy as a survival buffer during harsh challenges. However, fat-tail is losing its importance in modern sheep industry systems and thin-tailed breeds are more desirable. Using comparative transcriptome analysis to compare fat-tail tissue between fat- and thin-tailed sheep breeds provides a valuable approach to study the complex genetic factors associated with fat-tail development. However, transcriptomic studies often suffer from issues with reproducibility, which can be improved by integrating multiple studies based on a meta-analysis.

Methods

Hence, for the first time, an RNA-Seq meta-analysis on sheep fat-tail transcriptomes was performed using six publicly available datasets.

Results and discussion

A total of 500 genes (221 up-regulated, 279 down-regulated) were identified as differentially expressed genes (DEGs). A jackknife sensitivity analysis confirmed the robustness of the DEGs. Moreover, QTL and functional enrichment analysis reinforced the importance of the DEGs in the underlying molecular mechanisms of fat deposition. Protein-protein interactions (PPIs) network analysis revealed the functional interactions among the DEGs and the subsequent sub-network analysis led to identify six functional sub-networks. According to the results of the network analysis, down-regulated DEGs in green and pink sub-networks (like collagen subunits IV, V, and VI, integrins 1 and 2, SCD, SCD5, ELOVL6, ACLY, SLC27A2, and LPIN1) may impair lipolysis or fatty acid oxidation and cause fat accumulation in tail. On the other hand, up-regulated DEGs, especially those are presented in green and pink sub-networks (like IL6, RBP4, LEPR, PAI-1, EPHX1, HSD11B1, and FMO2), might contribute to a network controlling fat accumulation in the tail of sheep breed through mediating adipogenesis and fatty acid biosynthesis. Our results highlighted a set of known and novel genes/pathways associated with fat-tail development, which could improve the understanding of molecular mechanisms behind fat deposition in sheep fat-tail.

Network-centric analysis of co-fractionated protein complex profiles using SECAT

The Size-Exclusion Chromatography Analysis Toolkit (SECAT) elucidates protein complex dynamics using co-fractionated bottom-up mass spectrometry (CF-MS) data. Here, we present a protocol for the network-centric analysis and interpretation of CF-MS profiles using SECAT. We describe the technical steps for preprocessing, scoring, semi-supervised machine learning, and quantification, including common pitfalls and their solutions. We further provide guidance for data export, visualization, and the interpretation of SECAT results to discover dysregulated proteins and interactions, supporting new hypotheses and biological insights. For complete details on the use and execution of this protocol, please refer to Rosenberger et al. (2020).1.

Protein complexes in cells by AI-assisted structural proteomics

Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use a combination of in-cell crosslinking mass spectrometry and co-fractionation mass spectrometry (CoFrac-MS) to identify protein-protein interactions in the model Gram-positive bacterium Bacillus subtilis. We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the SubtiWiki database with AlphaFold-Multimer and, after controlling for the false-positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein-protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ( ϕ KZ and ϕ PA3) in Pseudomonas aeruginosa . This enabled the detection > 6000 unique host-pathogen and > 200 pathogen-pathogen interactions for each phage, encompassing > 50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕ PA3 complex showing strong structural and sequence similarity to ϕ KZ nvRNAp, suggesting ϕ PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

Discovering Protein-Protein Interactions using Co-Fractionation-Mass Spectrometry with Label-Free Quantitation

Proteins generally achieve their functions through interactions with other proteins, so being able to determine which proteins interact with which other proteins underlies much of molecular biology. Co-fractionation (CF) is a mass spectrometry-based method for detecting proteome-wide protein-protein interactions. An attractive feature of CF is that it is not necessary to label or otherwise alter samples. Although we have previously published a widely used protocol for a label-incorporated CF methodology, no published protocols currently exist for the label-free variation. In this chapter, we describe a label-free CF-MS protocol. This protocol takes a minimum of a week, excluding the time for cell/tissue culture. It begins with cell/tissue lysis under non-denaturing conditions, after which intact protein complexes are isolated using size exclusion chromatography (SEC) where they are fractionated according to size. The proteins in each fraction are then prepared for mass spectrometry analysis where the constituent proteins are identified and quantified. Finally, we describe an in-house bioinformatics pipeline, PrInCE, to accurately predict protein complexes. Taken together, co-fractionation methodologies combined with mass spectrometry can identify and quantify thousands of protein-protein interactions in biological systems.

Quantitative fragmentomics allow affinity mapping of interactomes

Human protein networks have been widely explored but most binding affinities remain unknown, hindering quantitative interactome-function studies. Yet interactomes rely on minimal interacting fragments displaying quantifiable affinities. Here, we measure the affinities of 65,000 interactions involving PDZ domains and their target PDZ-binding motifs (PBM) within a human interactome region particularly relevant for viral infection and cancer. We calculate interactomic distances, identify hot spots for viral interference, generate binding profiles and specificity logos, and explain selected cases by crystallographic studies. Mass spectrometry experiments on cell extracts and literature surveys show that quantitative fragmentomics effectively complements protein interactomics by providing affinities and completeness of coverage, putting a full human interactome affinity survey within reach. Finally, we show that interactome hijacking by the viral PBM of human papillomavirus E6 oncoprotein substantially impacts the host cell proteome beyond immediate E6 binders, illustrating the complex system-wide relationship between interactome and function.

Protein networks have been widely explored but most binding affinities remain unknown, limiting the quantitative interpretation of interactomes. Here the authors measure affinities of 65,000 interactions involving human PDZ domains and target sequence motifs relevant for viral infection and cancer.

The protein organization of a red blood cell

Red blood cells (RBCs) (erythrocytes) are the simplest primary human cells, lacking nuclei and major organelles and instead employing about a thousand proteins to dynamically control cellular function and morphology in response to physiological cues. In this study, we define a canonical RBC proteome and interactome using quantitative mass spectrometry and machine learning. Our data reveal an RBC interactome dominated by protein homeostasis, redox biology, cytoskeletal dynamics, and carbon metabolism. We validate protein complexes through electron microscopy and chemical crosslinking and, with these data, build 3D structural models of the ankyrin/Band 3/Band 4.2 complex that bridges the spectrin cytoskeleton to the RBC membrane. The model suggests spring-like compression of ankyrin may contribute to the characteristic RBC cell shape and flexibility. Taken together, our study provides an in-depth view of the global protein organization of human RBCs and serves as a comprehensive resource for future research.

Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery

Co-fractionation/mass spectrometry (CF/MS) enables the mapping of endogenous macromolecular networks on a proteome scale, but current methods are experimentally laborious, resource intensive and afford lesser quantitative accuracy. Here, we present a technically efficient, cost-effective and reproducible multiplex CF/MS (mCF/MS) platform for measuring and comparing, simultaneously, multi-protein assemblies across different experimental samples at a rate that is up to an order of magnitude faster than previous approaches. We apply mCF/MS to map the protein interaction landscape of non-transformed mammary epithelia versus breast cancer cells in parallel, revealing large-scale differences in protein-protein interactions and the relative abundance of associated macromolecules connected with cancer-related pathways and altered cellular processes. The integration of multiplexing capability within an optimized workflow renders mCF/MS as a powerful tool for systematically exploring physical interaction networks in a comparative manner.

Co-fractionation/mass spectrometry (CF/MS) allows mapping protein interactomes but efficiency and quantitative accuracy are limited. Here, the authors develop a reproducible multiplexed CF/MS method and apply it to characterize interactome rewiring in breast cancer cells.

PCfun: a hybrid computational framework for systematic characterization of protein complex function

In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively.

In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.

Decoding the cellular effects of genetic variation through interaction proteomics

It is often unclear how genetic variation translates into cellular phenotypes, including how much of the coding variation can be recovered in the proteome. Proteogenomic analyses of heterogenous cell lines revealed that the genetic differences impact mostly the abundance and stoichiometry of protein complexes, with the effects propagating post-transcriptionally via protein interactions onto other subunits. Conversely, large scale binary interaction analyses of missense variants revealed that loss of interaction is widespread and caused by about 50% disease-associated mutations, while deep scanning mutagenesis of binary interactions identified thousands of interaction-deficient variants per interaction. The idea that phenotypes arise from genetic variation through protein-protein interaction is therefore substantiated by both forward and reverse interaction proteomics. With improved methodologies, these two approaches combined can close the knowledge gap between nucleotide sequence variation and its functional consequences on the cellular proteome.

ATAD3A has a scaffolding role regulating mitochondria inner membrane structure and protein assembly

The ATPase Family AAA Domain Containing 3A (ATAD3A), is a mitochondrial inner membrane protein conserved in metazoans. ATAD3A has been associated with several mitochondrial functions, including nucleoid organization, cholesterol metabolism, and mitochondrial translation. To address its primary role, we generated a neuronal-specific conditional knockout (Atad3 nKO) mouse model, which developed a severe encephalopathy by 5 months of age. Pre-symptomatic mice showed aberrant mitochondrial cristae morphogenesis in the cortex as early as 2 months. Using a multi-omics approach in the CNS of 2-to-3-month-old mice, we found early alterations in the organelle membrane structure. We also show that human ATAD3A associates with different components of the inner membrane, including OXPHOS complex I, Letm1, and prohibitin complexes. Stochastic Optical Reconstruction Microscopy (STORM) shows that ATAD3A is regularly distributed along the inner mitochondrial membrane, suggesting a critical structural role in inner mitochondrial membrane and its organization, most likely in an ATPase-dependent manner.

Arguello et al. show that deletion of the mitochondrial protein ATAD3 in neurons leads to neuronal loss and death. The earliest phenotype is disruption of the mitochondrial inner membrane structure; OXPHOS complexes are affected later. ATAD3 is regularly spaced and has several interactors at the inner membrane, including CI subunits.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.