Mass spectrometry-based protein-protein interaction networks for the study of human diseases

Comparison of two peroxidases with high potential for biotechnology applications - HRP vs. APEX2

Peroxidases are essential elements in many biotechnological applications. An especially interesting concept involves split enzymes, where the enzyme is separated into two smaller and inactive proteins that can dimerize into a fully active enzyme. Such split forms were developed for the horseradish peroxidase (HRP) and ascorbate peroxidase (APX) already. Both peroxidases have a high potential for biotechnology applications. In the present study, we performed biophysical comparisons of these two peroxidases and their split analogues. The active site availability is similar for all four structures. The split enzymes are comparable in stability with their native analogues, meaning that they can be used for further biotechnology applications. Also, the tertiary structures of the two peroxidases are similar. However, differences that might help in choosing one system over another for biotechnology applications were noticed. The main difference between the two systems is glycosylation which is not present in the case of APX/sAPEX2, while it has a high impact on the HRP/sHRP stability. Further differences are calcium ions and cysteine bridges that are present only in the case of HRP/sHRP. Finally, computational results identified sAPEX2 as the systems with the smallest structural variations during molecular dynamics simulations showing its dominant stability comparing to other simulated proteins. Taken all together, the sAPEX2 system has a high potential for biotechnological applications due to the lack of glycans and cysteines, as well as due to high stability.

A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies

Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

Protein mimetic 2D FAST rescues alpha synuclein aggregation mediated early and post disease Parkinson's phenotypes

Abberent protein-protein interactions potentiate many diseases and one example is the toxic, self-assembly of α-Synuclein in the dopaminergic neurons of patients with Parkinson's disease; therefore, a potential therapeutic strategy is the small molecule modulation of α-Synuclein aggregation. In this work, we develop an Oligopyridylamide based 2-dimensional Fragment-Assisted Structure-based Technique to identify antagonists of α-Synuclein aggregation. The technique utilizes a fragment-based screening of an extensive array of non-proteinogenic side chains in Oligopyridylamides, leading to the identification of NS132 as an antagonist of the multiple facets of α-Synuclein aggregation. We further identify a more cell permeable analog (NS163) without sacrificing activity. Oligopyridylamides rescue α-Synuclein aggregation mediated Parkinson's disease phenotypes in dopaminergic neurons in early and post disease Caenorhabditis elegans models. We forsee tremendous potential in our technique to identify lead therapeutics for Parkinson's disease and other diseases as it is expandable to other oligoamide scaffolds and a larger array of side chains.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Generation and Characterization of Engineered Ubiquitin Variants to Modulate the Ubiquitin Signaling Cascade

The ubiquitin signaling cascade plays a crucial role in human cells. Consistent with this, malfunction of ubiquitination and deubiquitination is implicated in the initiation and progression of numerous human diseases, including cancer. Therefore, the development of potent and specific modulators of ubiquitin signal transduction has been at the forefront of drug development. In the past decade, a structure-based combinatorial protein-engineering approach has been used to generate ubiquitin variants (UbVs) as protein-based modulators of multiple components in the ubiquitin-proteasome system. Here, we review the design and generation of phage-displayed UbV libraries, including the processes of binder selection and library improvement. We also provide a comprehensive overview of the general in vitro and cellular methodologies involved in characterizing UbV binders. Finally, we describe two recent applications of UbVs for developing molecules with therapeutic potential.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in cancer. The model identifies eight core assemblies that integrate rare and common alterations across 90 genes to stratify palbociclib-sensitive versus palbociclib-resistant cell lines. Predictions translate to patients and patient-derived xenografts, whereas single-gene biomarkers do not. Most predictive assemblies can be shown by CRISPR-Cas9 genetic disruption to regulate the CDK4/6i response. Validated assemblies relate to cell-cycle control, growth factor signaling and a histone regulatory complex that we show promotes S-phase entry through the activation of the histone modifiers KAT6A and TBL1XR1 and the transcription factor RUNX1. This study enables an integrated assessment of how a tumor's genetic profile modulates CDK4/6i resistance.

Rapid Profiling of the Glycosylation Effects on the Binding of SARS-CoV-2 Spike Protein to Angiotensin-Converting Enzyme 2 Using MALDI-MS with High Mass Detection

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 binds directly to angiotensin-converting enzyme 2 (ACE2), mediating the host cell entry of SARS-CoV-2. Both spike protein and ACE2 are highly glycosylated, which can regulate the binding. Here, we utilized high-mass MALDI-MS with chemical cross-linking for profiling the glycosylation effects on the binding between RBD and ACE2. Overall, it was found that ACE2 glycosylation affects the binding more strongly than does RBD glycosylation. The binding affinity was improved after desialylation or partial deglycosylation (N690) of ACE2, while it decreased after degalactosylation. ACE2 can form dimers in solution, which bind more tightly to the RBD than the ACE2 monomers. The ACE2 dimerization and the binding of RBD to dimeric ACE2 can also be improved by the desialylation or deglycosylation of ACE2. Partial deglycosylation of ACE2 increased the dimerization of ACE2 and the binding affinity of RBD and ACE2 by more than a factor of 2, suggesting its high potential for neutralizing SARS-CoV-2. The method described in the work provided a simple way to analyze the protein-protein interaction without sample purification. It can be widely used for rapid profiling of glycosylation effects on protein-protein interaction for glycosylation-related diseases and the study of multiple interactions between protein and protein aggregates in a single system.

A review of nuclear Dbf2-related kinase 1 (NDR1) protein interaction as promising new target for cancer therapy

Nuclear Dbf2-related kinase 1 (NDR1) is a nuclear Dbf2-related (NDR) protein kinase family member, which regulates cell functions and participates in cell proliferation and differentiation through kinase activity. NDR1 regulates physiological functions by interacting with different proteins. Protein-protein interactions (PPIs) are crucial for regulating biological processes and controlling cell fate, and as a result, it is beneficial to study the actions of PPIs to elucidate the pathological mechanism of diseases. The previous studies also show that the expression of NDR1 is deregulated in numerous human cancer samples and it needs the context-specific targeting strategies for NDR1. Thus, a comprehensive understanding of the direct interaction between NDR1 and varieties of proteins may provide new insights into cancer therapies. In this review, we summarize recent studies of NDR1 in solid tumors, such as prostate cancer and breast cancer, and explore the mechanism of action of PPIs of NDR1 in tumors.

Photoproximity Labeling from Single Catalyst Sites Allows Calibration and Increased Resolution for Carbene Labeling of Protein Partners In Vitro and on Cells

The cell surface proteome (surfaceome) plays a pivotal role in virtually all extracellular biology, and yet we are only beginning to understand the protein complexes formed in this crowded environment. Recently, a high-resolution approach (μMap) was described that utilizes multiple iridium-photocatalysts attached to a secondary antibody, directed to a primary antibody of a protein of interest, to identify proximal neighbors by light-activated conversion of a biotin-diazirine to a highly reactive carbene followed by LC/MS (Geri, J. B.; Oakley, J. V.; Reyes-Robles, T.; Wang, T.; McCarver, S. J.; White, C. H.; Rodriguez-Rivera, F. P.; Parker, D. L.; Hett, E. C.; Fadeyi, O. O.; Oslund, R. C.; MacMillan, D. W. C. Science2020, 367, 1091-1097). Here we calibrated the spatial resolution for carbene labeling using site-specific conjugation of a single photocatalyst to a primary antibody drug, trastuzumab (Traz), in complex with its structurally well-characterized oncogene target, HER2. We observed relatively uniform carbene labeling across all amino acids, and a maximum distance of ∼110 Å from the fixed photocatalyst. When targeting HER2 overexpression cells, we identified 20 highly enriched HER2 neighbors, compared to a nonspecific membrane tethered catalyst. These studies identify new HER2 interactors and calibrate the radius of carbene photoprobe labeling for the surfaceome.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.

MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network

MOTIVATION

Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space.

RESULTS

In response to this gap, this article proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and E(n) equivariant graph neural network (EGNN). There are two channels in MEG-PPIS: the original graph and the subgraph obtained by graph pooling. The model can iteratively update the features of the original graph and subgraph through the weight-sharing EGNN. Subsequently, the max-pooling operation aggregates the updated features of the original graph and subgraph. Ultimately, the model feeds node features into the prediction layer to obtain prediction results. Comparative assessments against other methods on benchmark datasets reveal that MEG-PPIS achieves optimal performance across all evaluation metrics and gets the fastest runtime. Furthermore, specific case studies demonstrate that our method can predict more true positive and true negative sites than the current best method, proving that our model achieves better performance in the PPIS prediction task.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/dhz234/MEG-PPIS.git.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

Assessing Trans-Inhibition of OATP1B1 and OATP1B3 by Calcineurin and/or PPIase Inhibitors and Global Identification of OATP1B1/3-Associated Proteins

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 are key determinants of drug-drug interactions (DDIs). Various drugs including the calcineurin inhibitor (CNI) cyclosporine A (CsA) exert preincubation-induced trans-inhibitory effects upon OATP1B1 and/or OATP1B3 (abbreviated as OATP1B1/3) by unknown mechanism(s). OATP1B1/3 are phosphoproteins; calcineurin, which dephosphorylates and regulates numerous phosphoproteins, has not previously been investigated in the context of preincubation-induced trans-inhibition of OATP1B1/3. Herein, we compare the trans-inhibitory effects exerted on OATP1B1 and OATP1B3 by CsA, the non-analogous CNI tacrolimus, and the non-CNI CsA analogue SCY-635 in transporter-overexpressing human embryonic kidney (HEK) 293 stable cell lines. Preincubation (10-60 min) with tacrolimus (1-10 µM) rapidly and significantly reduces OATP1B1- and OATP1B3-mediated transport up to 0.18 ± 0.03- and 0.20 ± 0.02-fold compared to the control, respectively. Both CsA and SCY-635 can trans-inhibit OATP1B1, with the inhibitory effects progressively increasing over a 60 min preincubation time. At each equivalent preincubation time, CsA has greater trans-inhibitory effects toward OATP1B1 than SCY-635. Preincubation with SCY-635 for 60 min yielded IC50 of 2.2 ± 1.4 µM against OATP1B1, which is ~18 fold greater than that of CsA (0.12 ± 0.04 µM). Furthermore, a proteomics-based screening for protein interactors was used to examine possible proteins and processes contributing to OATP1B1/3 regulation and preincubation-induced inhibition by CNIs and other drugs. A total of 861 and 357 proteins were identified as specifically associated with OATP1B1 and OATP1B3, respectively, including various protein kinases, ubiquitin-related enzymes, the tacrolimus (FK506)-binding proteins FKBP5 and FKBP8, and several known regulatory targets of calcineurin. The current study reports several novel findings that expand our understanding of impaired OATP1B1/3 function; these include preincubation-induced trans-inhibition of OATP1B1/3 by the CNI tacrolimus, greater preincubation-induced inhibition by CsA compared to its non-CNI analogue SCY-635, and association of OATP1B1/3 with various proteins relevant to established and candidate OATP1B1/3 regulatory processes.

A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry

Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.

A biotin targeting chimera (BioTAC) system to map small molecule interactomes in situ

Understanding how small molecules bind to specific protein complexes in living cells is critical to understanding their mechanism-of-action. Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules would provide advantages over target-focused approaches, including the ability to detect previously unknown ligand targets and complexes. However, there are few current methods for unbiased profiling of small molecule interactomes. To address this, we envisioned a technology that would combine the sensitivity and live-cell compatibility of proximity labelling coupled to mass spectrometry, with the specificity and unbiased nature of chemoproteomics. In this manuscript, we describe the BioTAC system, a small-molecule guided proximity labelling platform that can rapidly identify both direct and complexed small molecule binding proteins. We benchmark the system against µMap, photoaffinity labelling, affinity purification coupled to mass spectrometry and proximity labelling coupled to mass spectrometry datasets. We also apply the BioTAC system to provide interactome maps of Trametinib and analogues. The BioTAC system overcomes a limitation of current approaches and supports identification of both inhibitor bound and molecular glue bound complexes.

Site-specific crosslinking reveals Phosphofructokinase-L inhibition drives self-assembly and attenuation of protein interactions

Phosphofructokinase is the central enzyme in glycolysis and constitutes a highly regulated step. The liver isoform (PFKL) compartmentalizes during activation and inhibition in vitro and in vivo, respectively. Compartmentalized PFKL is hypothesized to modulate metabolic flux consistent with its central role as the rate limiting step in glycolysis. PFKL tetramers self-assemble at two interfaces in the monomer (interface 1 and 2), yet how these interfaces contribute to PFKL compartmentalization and drive protein interactions remains unclear. Here, we used site-specific incorporation of noncanonical photocrosslinking amino acids to identify PFKL interactors at interface 1, 2, and the active site. Tandem mass tag-based quantitative interactomics reveals interface 2 as a hotspot for PFKL interactions, particularly with cytoskeletal, glycolytic, and carbohydrate derivative metabolic proteins. Furthermore, PFKL compartmentalization into puncta was observed in human cells using citrate inhibition. Puncta formation attenuated crosslinked protein-protein interactions with the cytoskeleton at interface 2. This result suggests that PFKL compartmentalization sequesters interface 2, but not interface 1, and may modulate associated protein assemblies with the cytoskeleton.

Proteomic approaches advancing targeted protein degradation

Targeted protein degradation (TPD) is an emerging modality for research and therapeutics. Most TPD approaches harness cellular ubiquitin-dependent proteolytic pathways. Proteolysis-targeting chimeras (PROTACs) and molecular glue (MG) degraders (MGDs) represent the most advanced TPD approaches, with some already used in clinical settings. Despite these advances, TPD still faces many challenges, pertaining to both the development of effective, selective, and tissue-penetrant degraders and understanding their mode of action. In this review, we focus on progress made in addressing these challenges. In particular, we discuss the utility and application of recent proteomic approaches as indispensable tools to enable insights into degrader development, including target engagement, degradation selectivity, efficacy, safety, and mode of action.

Elucidating Structures of Protein Complexes by Collision-Induced Dissociation at Elevated Gas Pressures

Ion activation methods carried out at gas pressures compatible with ion mobility separations are not yet widely established. This limits the analytical utility of emerging tandem-ion mobility spectrometers that conduct multiple ion mobility separations in series. The present work investigates the applicability of collision-induced dissociation (CID) at 1 to 3 mbar in a tandem-trapped ion mobility spectrometer (tandem-TIMS) to study the architecture of protein complexes. We show that CID of the homotetrameric protein complexes streptavidin (53 kDa), neutravidin (60 kDa), and concanavalin A (110 kDa) provides access to all subunits of the investigated protein complexes, including structurally informative dimers. We report on an "atypical" dissociation pathway, which for concanavalin A proceeds via symmetric partitioning of the precursor charges and produces dimers with the same charge states that were previously reported from surface induced dissociation. Our data suggest a correlation between the formation of subunits by CID in tandem-TIMS/MS, their binding strengths in the native tetramer structures, and the applied activation voltage. Ion mobility spectra of in situ-generated subunits reveal a marked structural heterogeneity inconsistent with annealing into their most stable gas phase structures. Structural transitions are observed for in situ-generated subunits that resemble the transitions reported from collision-induced unfolding of natively folded proteins. These observations indicate that some aspects of the native precursor structure is preserved in the subunits generated from disassembly of the precursor complex. We rationalize our observations by an approximately 100-fold shorter activation time scale in comparison to traditional CID in a collision cell. Finally, the approach discussed here to conduct CID at elevated pressures appears generally applicable also for other types of tandem-ion mobility spectrometers.

Salivary Cystatin D Interactome in Patients with Systemic Mastocytosis: An Exploratory Study

Mastocytosis, a rare blood disorder characterized by the proliferation of clonal abnormal mast cells, has a variegated clinical spectrum and diagnosis is often difficult and delayed. Recently we proposed the cathepsin inhibitor cystatin D-R26 as a salivary candidate biomarker of systemic mastocytosis (SM). Its C26 variant is able to form multiprotein complexes (mPCs) and since protein-protein interactions (PPIs) are crucial for studying disease pathogenesis, potential markers, and therapeutic targets, we aimed to define the protein composition of the salivary cystatin D-C26 interactome associated with SM. An exploratory affinity purification-mass spectrometry method was applied on pooled salivary samples from SM patients, SM patient subgroups with and without cutaneous symptoms (SM+C and SM-C), and healthy controls (Ctrls). Interactors specifically detected in Ctrls were found to be implicated in networks associated with cell and tissue homeostasis, innate system, endopeptidase regulation, and antimicrobial protection. Interactors distinctive of SM-C patients participate to PPI networks related to glucose metabolism, protein S-nitrosylation, antibacterial humoral response, and neutrophil degranulation, while interactors specific to SM+C were mainly associated with epithelial and keratinocyte differentiation, cytoskeleton rearrangement, and immune response pathways. Proteins sensitive to redox changes, as well as proteins with immunomodulatory properties and activating mast cells, were identified in patients; many of them were involved directly in cytoskeleton rearrangement, a process crucial for mast cell activation. Although preliminary, these results demonstrate that PPI alterations of the cystatin D-C26 interactome are associated with SM and provide a basis for future investigations based on quantitative proteomic analysis and immune validation.

Site-Specific Crosslinking Reveals Phosphofructokinase-L Inhibition Drives Self-Assembly and Attenuation of Protein Interactions

Phosphofructokinase is the central enzyme in glycolysis and constitutes a highly regulated step. The liver isoform (PFKL) compartmentalizes during activation and inhibition in vitro and in vivo respectively. Compartmentalized PFKL is hypothesized to modulate metabolic flux consistent with its central role as the rate limiting step in glycolysis. PFKL tetramers self-assemble at two interfaces in the monomer (interface 1 and 2), yet how these interfaces contribute to PFKL compartmentalization and drive protein interactions remains unclear. Here, we used site-specific incorporation of noncanonical photocrosslinking amino acids to identify PFKL interactors at interface 1, 2, and the active site. Tandem mass tag-based quantitative interactomics reveals interface 2 as a hotspot for PFKL interactions, particularly with cytoskeletal, glycolytic, and carbohydrate derivative metabolic proteins. Furthermore, PFKL compartmentalization into puncta was observed in human cells using citrate inhibition. Puncta formation attenuated crosslinked protein-protein interactions with the cytoskeleton at interface 2. This result suggests that PFKL compartmentalization sequesters interface 2, but not interface 1, and may modulate associated protein assemblies with the cytoskeleton.

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

DeepSG2PPI: A Protein-Protein Interaction Prediction Method Based on Deep Learning

Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. First, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Second, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.

MS Annika 2.0 Identifies Cross-Linked Peptides in MS2-MS3-Based Workflows at High Sensitivity and Specificity

Cross-linking mass spectrometry has become a powerful tool for the identification of protein-protein interactions and for gaining insight into the structures of proteins. We previously published MS Annika, a cross-linking search engine which can accurately identify cross-linked peptides in MS2 spectra from a variety of different MS-cleavable cross-linkers. In this publication, we present MS Annika 2.0, an updated version implementing a new search algorithm that, in addition to MS2 level, only supports the processing of data from MS2-MS3-based approaches for the identification of peptides from MS3 spectra, and introduces a novel scoring function for peptides identified across multiple MS stages. Detected cross-links are validated by estimating the false discovery rate (FDR) using a target-decoy approach. We evaluated the MS3-search-capabilities of MS Annika 2.0 on five different datasets covering a variety of experimental approaches and compared it to XlinkX and MaXLinker, two other cross-linking search engines. We show that MS Annika detects up to 4 times more true unique cross-links while simultaneously yielding less false positive hits and therefore a more accurate FDR estimation than the other two search engines. All mass spectrometry proteomics data along with result files have been deposited to the ProteomeXchange consortium via the PRIDE partner repository with the dataset identifier PXD041955.

Association analysis of hepatocellular carcinoma-related hub proteins and hub genes

PURPOSE

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. The occurrence and development of HCC are closely related to epigenetic modifications. Epigenetic modifications can regulate gene expression and related functions through DNA methylation. This paper presents an association analysis method of HCC-related hub proteins and hub genes.

EXPERIMENTAL DESIGN

Bioinformatics analysis of HCC-related DNA methylation data is carried out to clarify the molecular mechanism of HCC-related genes and to find hub genes (genes with more connections in the network) by constructing in the gene interaction network. This paper proposes an accurate prediction method of protein-protein interaction (PPI) based on deep learning model DeepSG2PPI. The trained DeepSG2PPI model predicts the interaction relationship between the synthetic proteins regulated by HCC-related genes.

RESULTS

This paper finds that four genes are the intersection of hub genes and hub proteins. The four genes are: FBL, CCNB2, ALDH18A1, and RPLP0. The association of RPLP0 gene with HCC is a new finding of this study. RPLP0 is expected to become a new biomarker for the treatment, diagnosis, and prognosis of HCC. The four proteins corresponding to the four genes are: ENSP00000221801, ENSP00000288207, ENSP00000360268, and ENSP00000449328.

CONCLUSIONS AND CLINICAL RELEVANCE

The association between the hub genes with the hub proteins is analyzed. The mutual verification of the hub genes and the hub proteins can obtain more credible HCC-related genes and proteins, which is helpful for the diagnosis, treatment, and drug development of HCC.

A Biotin Targeting Chimera (BioTAC) System to Map Small Molecule Interactomes in situ

Unbiased chemical biology strategies for direct readout of protein interactome remodelling by small molecules provide advantages over target-focused approaches, including the ability to detect previously unknown targets, and the inclusion of chemical off-compete controls leading to high-confidence identifications. We describe the BioTAC system, a small-molecule guided proximity labelling platform, to rapidly identify both direct and complexed small molecule binding proteins. The BioTAC system overcomes a limitation of current approaches, and supports identification of both inhibitor bound and molecular glue bound complexes.

Exploring Single-Cell Exposomics by Mass Spectrometry

Single-cell exposomics, a revolutionary approach that investigates cell-environment interactions at cellular and subcellular levels, stands distinct from conventional bulk exposomics. Leveraging advancements in mass spectrometry, it provides a detailed perspective on cellular dynamics, interactions, and responses to environmental stimuli and their impacts on human health. This work delves into this innovative realm, highlighting the nuanced interplay between environmental stressors and biological responses at cellular and subcellular levels. The application of spatial mass spectrometry in single-cell exposomics is discussed, revealing the intricate spatial organization and molecular composition within individual cells. Cell-type-specific exposomics, shedding light on distinct susceptibilities and adaptive strategies of various cell types to environmental exposures, is also examined. The Perspective further emphasizes the integration with molecular and cellular biology approaches to validate hypotheses derived from single-cell exposomics in a comprehensive biological context. Looking toward the future, we anticipate continued technological advancements and convergence with other -omics approaches and discuss implications for environmental health research, disease progression studies, and precision medicine. The final emphasis is on the need for robust computational tools and interdisciplinary collaboration to fully leverage the potential of single-cell exposomics, acknowledging the complexities inherent to this paradigm.

Molecular complex detection in protein interaction networks through reinforcement learning

BACKGROUND

Proteins often assemble into higher-order complexes to perform their biological functions. Such protein-protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein-protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.

RESULTS

The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.

CONCLUSIONS

Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

Site-specific proximity labeling at single residue resolution for identification of protein partners in vitro and on cells

The cell surface proteome, or surfaceome, is encoded by more than 4000 genes, but we are only beginning to understand the complexes they form. Rapid proximity labeling around specific membrane targets allows for capturing weak and transient interactions expected in the crowded and dynamic environment of the surfaceome. Recently, a high-resolution approach called μMap has been described (Geri, J. B., Oakley, J. V., Reyes-Robles, T., Wang, T., McCarver, S. J., White, C. H., Rodriguez-Rivera, F. P., Parker, D. L., Hett, E. C., Fadeyi, O. O., Oslund, R. C., and MacMillan, D. W. C. (2020) Science 367 , 1091-1097) in which an iridium (Ir)-based photocatalyst is attached to a specific antibody to target labeling of neighbors utilizing light-activated generation of carbenes from diazirine compounds via Dexter Energy Transfer (DET). Here we studied and optimized the spatial resolution for the method using an oncoprotein complex between the antibody drug, trastuzumab (Traz), and its target HER2. A set of eight single site-specific Ir-catalytic centers were engineered into Traz to study intra- and inter-molecular labeling in vitro and on cells by mass spectrometry. From this structurally well-characterized complex we observed a maximum distance of ∼110 Å for labeling. Labeling occurred almost uniformly over the full range of amino acids, unlike the residue specific labeling of other techniques. To examine on cell labeling that is specific to HER2 as opposed to simply being on the membrane, we compared the labeling patterns for the eight Traz-catalyst species to random labeling of membrane proteins using a metabolically integrated fatty acid catalyst. Our results identified 20 high confidence HER2 neighbors, many novel, that were more than 6-fold enriched compared to the non-specific membrane tethered catalyst. These studies define distance labeling parameters from single-site catalysts placed directly on the membrane target of interest, and more accurately compare to non-specific labeling to identify membrane complexes with higher confidence.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

"Common-Precursor" Protein Mimetic Approach to Rescue Aβ Aggregation-Mediated Alzheimer's Phenotypes

Abberent protein-protein interactions (aPPIs) are associated with an array of pathological conditions, which make them important therapeutic targets. The aPPIs are mediated via specific chemical interactions that spread over a large and hydrophobic surface. Therefore, ligands that can complement the surface topography and chemical fingerprints could manipulate aPPIs. Oligopyridylamides (OPs) are synthetic protein mimetics that have been shown to manipulate aPPIs. However, the previous OP library used to disrupt these aPPIs was moderate in number (∼30 OPs) with very limited chemical diversity. The onus is on the laborious and time-consuming synthetic pathways with multiple chromatography steps. We have developed a novel chromatography-free technique to synthesize a highly diverse chemical library of OPs using a "common-precursor" approach. We significantly expanded the chemical diversity of OPs using a chromatography-free high-yielding method. To validate our novel approach, we have synthesized an OP with identical chemical diversity to a pre-existing OP-based potent inhibitor of Aβ aggregation, a process central to Alzheimer's disease (AD). The newly synthesized OP ligand (RD242) was very potent in inhibiting Aβ aggregation and rescuing AD phenotypes in an in vivo model. Moreover, RD242 was very effective in rescuing AD phenotypes in a post-disease onset AD model. We envision that our "common-precursor" synthetic approach will have tremendous potential as it is expandable for other oligoamide scaffolds to enhance affinity for disease-relevant targets.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually exclusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity purification (AP)-mass spectrometry (MS) experiments of exogenously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

CyanoMapDB: a database integrating experimentally validated protein-protein interactions in cyanobacteria

As one of the essential life forms in the biosphere, research on cyanobacteria has been growing remarkably for decades. Biological functions in organisms are often accomplished through protein-protein interactions (PPIs), which help to regulate interacting proteins or organize them into an integral machine. However, the study of PPIs in cyanobacteria falls far behind that in mammals and has not been integrated for ease of use. Thus, we built CyanoMapDB (http://www.cyanomapdb.msbio.pro/), a database providing cyanobacterial PPIs with experimental evidence, consisting of 52,304 PPIs among 6,789 proteins from 23 cyanobacterial species. We collected available data in UniProt, STRING, and IntAct, and mined numerous PPIs from co-fractionation MS data in cyanobacteria. The integrated data are accessible in CyanoMapDB (http://www.cyanomapdb.msbio.pro/), enabling users to easily query proteins of interest, investigate interacting proteins with evidence from different sources, and acquire a visual network of the target protein. We believe that CyanoMapDB will promote research involved with cyanobacteria and plants.

10 years of extracellular matrix proteomics: Accomplishments, challenges, and future perspectives

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics, have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely post-translationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome", of tissues. This review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Last, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. MS-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Characterization of Cystatin B Interactome in Saliva from Healthy Elderly and Alzheimer’s Disease Patients

Cystatin B is a small, multifunctional protein involved in the regulation of inflammation, innate immune response, and neuronal protection and found highly abundant in the brains of patients with Alzheimer’s disease (AD). Recently, our study demonstrated a significant association between the level of salivary cystatin B and AD. Since the protein is able to establish protein-protein interaction (PPI) in different contexts and aggregation-prone proteins and the PPI networks are relevant for AD pathogenesis, and due to the relevance of finding new AD markers in peripheral biofluids, we thought it was interesting to study the possible involvement of cystatin B in PPIs in saliva and to evaluate differences and similarities between AD and age-matched elderly healthy controls (HC). For this purpose, we applied a co-immunoprecipitation procedure and a bottom-up proteomics analysis to purify, identify, and quantify cystatin B interactors. Results demonstrated for the first time the existence of a salivary cystatin B-linked multi-protein complex composed by 82 interactors and largely expressed in the body. Interactors are involved in neutrophil activation, antimicrobial activity, modulation of the cytoskeleton and extra-cellular matrix (ECM), and glucose metabolism. Preliminary quantitative data showed significantly lower levels of triosophosphate isomerase 1 and higher levels of mucin 7, BPI, and matrix Gla protein in AD with respect to HC, suggesting implications associated with AD of altered glucose metabolism, antibacterial activities, and calcification-associated processes. Data are available via ProteomeXchange with identifiers PXD039286 and PXD030679.

Multiplexed kinase interactome profiling quantifies cellular network activity and plasticity

Dynamic changes in protein-protein interaction (PPI) networks underlie all physiological cellular functions and drive devastating human diseases. Profiling PPI networks can, therefore, provide critical insight into disease mechanisms and identify new drug targets. Kinases are regulatory nodes in many PPI networks; yet, facile methods to systematically study kinase interactome dynamics are lacking. We describe kinobead competition and correlation analysis (kiCCA), a quantitative mass spectrometry-based chemoproteomic method for rapid and highly multiplexed profiling of endogenous kinase interactomes. Using kiCCA, we identified 1,154 PPIs of 238 kinases across 18 diverse cancer lines, quantifying context-dependent kinase interactome changes linked to cancer type, plasticity, and signaling states, thereby assembling an extensive knowledgebase for cell signaling research. We discovered drug target candidates, including an endocytic adapter-associated kinase (AAK1) complex that promotes cancer cell epithelial-mesenchymal plasticity and drug resistance. Our data demonstrate the importance of kinase interactome dynamics for cellular signaling in health and disease.

New insights into extracellular and intracellular redox status in COVID-19 patients

BACKGROUND

The imbalance of redox homeostasis induces hyper-inflammation in viral infections. In this study, we explored the redox system signature in response to SARS-COV-2 infection and examined the status of these extracellular and intracellular signatures in COVID-19 patients.

METHOD

The multi-level network was constructed using multi-level data of oxidative stress-related biological processes, protein-protein interactions, transcription factors, and co-expression coefficients obtained from GSE164805, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and healthy controls. Top genes were designated based on the degree and closeness centralities. The expression of high-ranked genes was evaluated in PBMCs and nasopharyngeal (NP) samples of 30 COVID-19 patients and 30 healthy controls. The intracellular levels of GSH and ROS/O₂^• - and extracellular oxidative stress markers were assayed in PBMCs and plasma samples by flow cytometry and ELISA. ELISA results were applied to construct a classification model using logistic regression to differentiate COVID-19 patients from healthy controls.

RESULTS

CAT, NFE2L2, SOD1, SOD2 and CYBB were 5 top genes in the network analysis. The expression of these genes and intracellular levels of ROS/O₂^• - were increased in PBMCs of COVID-19 patients while the GSH level decreased. The expression of high-ranked genes was lower in NP samples of COVID-19 patients compared to control group. The activity of extracellular enzymes CAT and SOD, and the total oxidant status (TOS) level were increased in plasma samples of COVID-19 patients. Also, the 2-marker panel of CAT and TOS and 3-marker panel showed the best performance.

CONCLUSION

SARS-COV-2 disrupts the redox equilibrium in immune cells and the upper respiratory tract, leading to exacerbated inflammation and increased replication and entrance of SARS-COV-2 into host cells. Furthermore, utilizing markers of oxidative stress as a complementary validation to discriminate COVID-19 from healthy controls, seems promising.

Perspective on the potential of tandem-ion mobility /mass spectrometry methods for structural proteomics applications

Cellular processes are usually carried out collectively by the entirety of all proteins present in a biological cell, i.e. the proteome. Mass spectrometry-based methods have proven particularly successful in identifying and quantifying the constituent proteins of proteomes, including different molecular forms of a protein. Nevertheless, protein sequences alone do not reveal the function or dysfunction of the identified proteins. A straightforward way to assign function or dysfunction to proteins is characterization of their structures and dynamics. However, a method capable to characterize detailed structures of proteins and protein complexes in a large-scale, systematic manner within the context of cellular processes does not yet exist. Here, we discuss the potential of tandem-ion mobility / mass spectrometry (tandem-IM/MS) methods to provide such ability. We highlight the capability of these methods using two case studies on the protein systems ubiquitin and avidin using the tandem-TIMS/MS technology developed in our laboratory and discuss these results in the context of other developments in the broader field of tandem-IM/MS.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

Hepatocyte-derived MANF mitigates ethanol-induced liver steatosis in mice via enhancing ASS1 activity and activating AMPK pathway

Hepatic steatosis plays a detrimental role in the onset and progression of alcohol-associated liver disease (ALD). Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an evolutionarily conserved protein related to the unfolded protein response. Recent studies have demonstrated that MANF plays an important role in liver diseases. In this study, we investigated the role of MANF in ethanol-induced steatosis and the underlying mechanisms. We showed that the hepatic MANF expression was markedly upregulated in mouse model of ALD by chronic-plus-single-binge ethanol feeding. Moreover, after chronic-plus-binge ethanol feeding, hepatocyte-specific MANF knockout (HKO) mice displayed more severe hepatic steatosis and liver injury than wild-type (WT) control mice. Immunoprecipitation-coupled MS proteomic analysis revealed that arginosuccinate synthase 1 (ASS1), a rate-limiting enzyme in the urea cycle, resided in the same immunoprecipitated complex with MANF. Hepatocyte-specific MANF knockout led to decreased ASS1 activity, whereas overexpression of MANF contributed to enhanced ASS1 activity in vitro. In addition, HKO mice displayed unique urea cycle metabolite patterns in the liver with elevated ammonia accumulation after ethanol feeding. ASS1 is known to activate AMPK by generating an intracellular pool of AMP from the urea cycle. We also found that MANF supplementation significantly ameliorated ethanol-induced steatosis in vivo and in vitro by activating the AMPK signaling pathway, which was partly ASS1 dependent. This study demonstrates a new mechanism in which MANF acts as a key molecule in maintaining hepatic lipid homeostasis by enhancing ASS1 activity and uncovers an interesting link between lipid metabolism and the hepatic urea cycle under excessive alcohol exposure.

Native holdup (nHU) to measure binding affinities from cell extracts

Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.