Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

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.

Phosphorylation-linked complex profiling identifies assemblies required for Hippo signal integration.

While several computational methods have been developed to predict the functional relevance of phosphorylation sites, experimental analysis of the interdependency between protein phosphorylation and Protein-Protein Interactions (PPIs) remains challenging. Here, we describe an experimental strategy to establish interdependencies between protein phosphorylation and complex formation. This strategy is based on three main steps: (i) systematically charting the phosphorylation landscape of a target protein; (ii) assigning distinct proteoforms of the target protein to different protein complexes by native complex separation (AP-BNPAGE) and protein correlation profiling; and (iii) analyzing proteoforms and complexes in cells lacking regulators of the target protein. We applied this strategy to YAP1, a transcriptional co-activator for the control of organ size and tissue homeostasis that is highly phosphorylated and among the most connected proteins in human cells. We identified multiple YAP1 phosphosites associated with distinct complexes and inferred how both are controlled by Hippo pathway members. We detected a PTPN14/LATS1/YAP1 complex and suggest a model how PTPN14 inhibits YAP1 via augmenting WW domain-dependent complex formation and phosphorylation by LATS1/2.

Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics.

Protein-protein interactions (PPIs) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components but mostly lack the context of cellular states. However, the generation of context-dependent PPI networks is essential for structural and systems-level modeling of biological processes-a goal that remains an unsolved challenge. Here we describe an experimental/computational strategy to achieve a modeling of PPIs that considers contextual information. This strategy defines the composition, stoichiometry, temporal organization, and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

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.

Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen.

Proteases are among the largest protein families and critical regulators of biochemical processes like apoptosis and blood coagulation. Knowledge of proteases has been expanded by the development of proteomic approaches, however, technology for multiplexed screening of proteases within native environments is currently lacking behind. Here we introduce a simple method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter, their analysis in a mass spectrometer and a custom data analysis pipeline. The method is significantly faster, cheaper, technically less demanding, easy to multiplex and produces accurate protease fingerprints. Using the blood cascade proteases as a case study, we obtain protease substrate profiles that can be used to map specificity, cleavage entropy and allosteric effects and to design protease probes. The data further show that protease substrate predictions enable the selection of potential physiological substrates for targeted validation in biochemical assays.

Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes.

Rapidly increasing availability of genomic data and ensuing identification of disease associated mutations allows for an unbiased insight into genetic drivers of disease development. However, determination of molecular mechanisms by which individual genomic changes affect biochemical processes remains a major challenge. Here, we develop a multilayered proteomic workflow to explore how genetic lesions modulate the proteome and are translated into molecular phenotypes. Using this workflow we determine how expression of a panel of disease-associated mutations in the Dyrk2 protein kinase alter the composition, topology and activity of this kinase complex as well as the phosphoproteomic state of the cell. The data show that altered protein-protein interactions caused by the mutations are associated with topological changes and affected phosphorylation of known cancer driver proteins, thus linking Dyrk2 mutations with cancer-related biochemical processes. Overall, we discover multiple mutation-specific functionally relevant changes, thus highlighting the extensive plasticity of molecular responses to genetic lesions.

Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases.

Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.

A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model.

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1hep-/-), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.

Contribution of Mass Spectrometry-Based Proteomics to the Understanding of TNF-α Signaling.

NF-κB is a family of ubiquitous dimeric transcription factors that play a role in a myriad of cellular processes, ranging from differentiation to stress response and immunity. In inflammation, activation of NF-κB is mediated by pro-inflammatory cytokines, in particular the prototypic cytokines IL-1ß and TNF-α, which trigger the activation of complex signaling cascades. In spite of decades of research, the system level understanding of TNF-α signaling is still incomplete. This is partially due to the limited knowledge at the proteome level. The objective of this review is to summarize and critically evaluate the current status of the proteomic research on TNF-α signaling. We will discuss the merits and flaws of the existing studies as well as the insights that they have generated into the proteomic landscape and architecture connected to this signaling pathway. Besides delineating past and current trends in TNF-α proteomic research, we will identify research directions and new methodologies that can further contribute to characterize the TNF-α associated proteome in space and time.

The selective autophagy receptor p62 forms a flexible filamentous helical scaffold.

The scaffold protein p62/SQSTM1 is involved in protein turnover and signaling and is commonly found in dense protein bodies in eukaryotic cells. In autophagy, p62 acts as a selective autophagy receptor that recognizes and shuttles ubiquitinated proteins to the autophagosome for degradation. The structural organization of p62 in cellular bodies and the interplay of these assemblies with ubiquitin and the autophagic marker LC3 remain to be elucidated. Here, we present a cryo-EM structural analysis of p62. Together with structures of assemblies from the PB1 domain, we show that p62 is organized in flexible polymers with the PB1 domain constituting a helical scaffold. Filamentous p62 is capable of binding LC3 and addition of long ubiquitin chains induces disassembly and shortening of filaments. These studies explain how p62 assemblies provide a large molecular scaffold for the nascent autophagosome and reveal how they can bind ubiquitinated cargo.

SPRING - an image processing package for single-particle based helical reconstruction from electron cryomicrographs.

Helical reconstruction from electron cryomicrographs has become a routine technique for macromolecular structure determination of helical assemblies since the first days of Fourier-based three-dimensional image reconstruction. In the past decade, the single-particle technique has had an important impact on the advancement of helical reconstruction. Here, we present the software package SPRING that combines Fourier based symmetry analysis and real-space helical processing into a single workflow. One of the most time-consuming steps in helical reconstruction is the determination of the initial symmetry parameters. First, we propose a class-based helical reconstruction approach that enables the simultaneous exploration and evaluation of many symmetry combinations at low resolution. Second, multiple symmetry solutions can be further assessed and refined by single-particle based helical reconstruction using the correlation of simulated and experimental power spectra. Finally, the 3D structure can be determined to high resolution. In order to validate the procedure, we use the reference specimen Tobacco Mosaic Virus (TMV). After refinement of the helical symmetry, a total of 50,000 asymmetric units from two micrographs are sufficient to reconstruct a subnanometer 3D structure of TMV at 6.4Å resolution. Furthermore, we introduce the individual programs of the software and discuss enhancements of the helical reconstruction workflow. Thanks to its user-friendly interface and documentation, SPRING can be utilized by the novice as well as the expert user. In addition to the study of well-ordered helical structures, the development of a streamlined workflow for single-particle based helical reconstruction opens new possibilities to analyze specimens that are heterogeneous in symmetries.

Molecular basis for coupling the plasma membrane to the actin cytoskeleton during clathrin-mediated endocytosis.

Dynamic actin filaments are a crucial component of clathrin-mediated endocytosis when endocytic proteins cannot supply enough energy for vesicle budding. Actin cytoskeleton is thought to provide force for membrane invagination or vesicle scission, but how this force is transmitted to the plasma membrane is not understood. Here we describe the molecular mechanism of plasma membrane-actin cytoskeleton coupling mediated by cooperative action of epsin Ent1 and the HIP1R homolog Sla2 in yeast Saccharomyces cerevisiae. Sla2 anchors Ent1 to a stable endocytic coat by an unforeseen interaction between Sla2's ANTH and Ent1's ENTH lipid-binding domains. The ANTH and ENTH domains bind each other in a ligand-dependent manner to provide critical anchoring of both proteins to the membrane. The C-terminal parts of Ent1 and Sla2 bind redundantly to actin filaments via a previously unknown phospho-regulated actin-binding domain in Ent1 and the THATCH domain in Sla2. By the synergistic binding to the membrane and redundant interaction with actin, Ent1 and Sla2 form an essential molecular linker that transmits the force generated by the actin cytoskeleton to the plasma membrane, leading to membrane invagination and vesicle budding.