Interactomes of SARS‐CoV‐2 and human coronaviruses reveal host factors potentially affecting pathogenesis

Host–virus protein–protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity‐labeling strategies and identified 437 human proteins as the high‐confidence interacting proteins. Further characterization of these interactions and comparison to other large‐scale study of cellular responses to SARS‐CoV‐2 infection elucidated how distinct SARS‐CoV‐2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID‐19. The interactomes of two key SARS‐CoV‐2‐encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein–protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS‐CoV‐2 provides valuable resources for the understanding and treating of this disease.     

SARS‐CoV‐2–host proteome interactions for antiviral drug discovery

Treatment options for COVID‐19, caused by SARS‐CoV‐2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS‐CoV‐2–host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS‐CoV‐2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP‐MS) and the complementary proximity‐based labeling MS method (BioID‐MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS‐CoV‐2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image‐based drug screen with infectious SARS‐CoV‐2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein–protein interactions.     

Computational design of small molecular modulators of protein–protein interactions with a novel thermodynamic cycle: Allosteric inhibitors of HIV‐1 integrase

Targeting protein–protein interactions for therapeutic development involves designing small molecules to either disrupt or enhance a known PPI. For this purpose, it is necessary to compute reliably the effect of chemical modifications of small molecules on the protein–protein association free energy. Here we present results obtained using a novel thermodynamic free energy cycle, for the rational design of allosteric inhibitors of HIV‐1 integrase (ALLINI) that act specifically in the early stage of the infection cycle. The new compounds can serve as molecular probes to dissect the multifunctional mechanisms of ALLINIs to inform the discovery of new allosteric inhibitors. The free energy protocol developed here can be more broadly applied to study quantitatively the effects of small molecules on modulating the strengths of protein–protein interactions.     

CFTR interactome mapping using the mammalian membrane two‐hybrid high‐throughput screening system

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a chloride and bicarbonate channel in secretory epithelia with a critical role in maintaining fluid homeostasis. Mutations in CFTR are associated with Cystic Fibrosis (CF), the most common lethal autosomal recessive disorder in Caucasians. While remarkable treatment advances have been made recently in the form of modulator drugs directly rescuing CFTR dysfunction, there is still considerable scope for improvement of therapeutic effectiveness. Here, we report the application of a high‐throughput screening variant of the Mammalian Membrane Two‐Hybrid (MaMTH‐HTS) to map the protein–protein interactions of wild‐type (wt) and mutant CFTR (F508del), in an effort to better understand CF cellular effects and identify new drug targets for patient‐specific treatments. Combined with functional validation in multiple disease models, we have uncovered candidate proteins with potential roles in CFTR function/CF pathophysiology, including Fibrinogen Like 2 (FGL2), which we demonstrate in patient‐derived intestinal organoids has a significant effect on CFTR functional expression.     

High‐throughput metabolomics predicts drug–target relationships for eukaryotic proteins

Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high‐throughput metabolomics and overexpression to predict drug–target interactions. The metabolome profiles of yeast treated with 1,280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high‐throughput metabolomics.     

Purification of MAP–kinase protein complexes and identification of candidate components by XL–TAP–MS

The purification of low-abundance protein complexes and detection of in vivo protein–protein interactions in complex biological samples remains a challenging task. Here, we devised crosslinking and tandem affinity purification coupled to mass spectrometry (XL–TAP–MS), a quantitative proteomics approach for analyzing tandem affinity-purified, crosslinked protein complexes from plant tissues. We exemplarily applied XL–TAP–MS to study the MKK2–Mitogen-activated protein kinase (MPK4) signaling module in Arabidopsis thaliana. A tandem affinity tag consisting of an in vivo-biotinylated protein domain flanked by two hexahistidine sequences was adopted to allow for the affinity-based isolation of formaldehyde–crosslinked protein complexes under fully denaturing conditions. Combined with ¹⁵N stable isotopic labeling and tandem MS we captured and identified a total of 107 MKK2–MPK4 module-interacting proteins. Consistent with the role of the MPK signaling module in plant immunity, many of the module-interacting proteins are involved in the biotic and abiotic stress response of Arabidopsis. Validation of binary protein–protein interactions by in planta split-luciferase assays and in vitro kinase assays disclosed several direct phosphorylation targets of MPK4. Together, the XL–TAP–MS approach purifies low abundance protein complexes from biological samples and discovers previously unknown protein–protein interactions.

XL–TAP–MS: a novel technique that allows purification of crosslinked, low abundant protein complexes from plant tissues under denatured conditions and detection of in vivo protein–protein interactions.     

Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID‐19

ObjectivesCoronavirus disease 2019 (COVID‐19) is rapidly spreading worldwide. Lianhua Qingwen capsule (LQC) has shown therapeutic effects in patients with COVID‐19. This study is aimed to discover its molecular mechanism and provide potential drug targets.Materials and MethodsAn LQC target and COVID‐19–related gene set was established using the Traditional Chinese Medicine Systems Pharmacology database and seven disease‐gene databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‐protein interaction (PPI) network were performed to discover the potential mechanism. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein.ResultsA gene set of 65 genes was generated. We then constructed a compound‐target network that contained 234 nodes of active compounds and 916 edges of compound‐target pairs. The GO and KEGG indicated that LQC can act by regulating immune response, apoptosis and virus infection. PPI network and subnetworks identified nine hub genes. The molecular docking was conducted on the most significant gene Akt1, which is involved in lung injury, lung fibrogenesis and virus infection. Six active compounds of LQC can enter the active pocket of Akt1, namely beta‐carotene, kaempferol, luteolin, naringenin, quercetin and wogonin, thereby exerting potential therapeutic effects in COVID‐19.ConclusionsThe network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of LQC. Akt1 is a promising drug target to reduce tissue damage and help eliminate virus infection.     

An ultra‐high affinity protein–protein interface displaying sequence‐robustness

Protein–protein interactions are crucial in biology and play roles in for example, the immune system, signaling pathways, and enzyme regulation. Ultra‐high affinity interactions (K _d <0.1 nM) occur in these systems, however, structures and energetics behind stability of ultra‐high affinity protein–protein complexes are not well understood. Regulation of the starch debranching barley limit dextrinase (LD) and its endogenous cereal type inhibitor (LDI) exemplifies an ultra‐high affinity complex (K _d of 42 pM). In this study the LD–LDI complex is investigated to unveil how robust the ultra‐high affinity is to LDI sequence variation at the protein–protein interface and whether alternative sequences can retain the ultra‐high binding affinity. The interface of LD–LDI was engineered using computational protein redesign aiming at identifying LDI variants predicted to retain ultra‐high binding affinity. These variants present a very diverse set of mutations going beyond conservative and alanine substitutions typically used to probe interfaces. Surface plasmon resonance analysis of the LDI variants revealed that high affinity of LD–LDI requires interactions of several residues at the rim of the protein interface, unlike the classical hotspot arrangement where key residues are found at the center of the interface. Notably, substitution of interface residues in LDI, including amino acids with functional groups different from the wild‐type, could occur without loss of affinity. This demonstrates that ultra‐high binding affinity can be conferred without hotspot residues, thus making complexes more robust to mutational drift in evolution. The present mutational analysis also demonstrates how energetic coupling can emerge between residues at large distances at the interface.     

Direct visualization of Bcl-2 family protein interactions using live cell fluorescent protein redistribution assays

Bcl-2 family proteins have important roles in tumor initiation, progression and resistance to therapy. Pro-survival Bcl-2 proteins are regulated by their interactions with pro-death BH3-only proteins making these protein–protein interactions attractive therapeutic targets. Although these interactions have been extensively characterized biochemically, there is a paucity of tools to assess these interactions in cells. Here, we address this limitation by developing quantitative, high throughput microscopy assays to characterize Bcl-2 and BH3-only protein interactions in live cells. We use fluorescent proteins to label the interacting proteins of interest, enabling visualization and quantification of their mitochondria-localized interactions. Using tool compounds, we demonstrate the suitability of our assays to characterize the cellular activity of putative therapeutic molecules that target the interaction between pro-survival Bcl-2 and pro-death BH3-only proteins. In addition to the relevance of our assays for drug discovery, we anticipate that our work will contribute to an improved understanding of the mechanisms that regulate these important protein–protein interactions within the cell.     

Tracking exogenous intracellular casp‐3 using split GFP

Cytosolic protein delivery promises diverse applications from therapeutics, to genetic modification and precision research tools. To achieve effective cellular and subcellular delivery, approaches that allow protein visualization and accurate localization with greater sensitivity are essential. Fluorescently tagging proteins allows detection, tracking and visualization in cellulo. However, undesired consequences from fluorophores or fluorescent protein tags, such as nonspecific interactions and high background or perturbation to native protein''s size and structure, are frequently observed, or more troublingly, overlooked. Distinguishing cytosolically released molecules from those that are endosomally entrapped upon cellular uptake is particularly challenging and is often complicated by the inherent pH‐sensitive and hydrophobic properties of the fluorophore. Monitoring localization is more complex in delivery of proteins with inherent protein‐modifying activities like proteases, transacetylases, kinases, etc. Proteases are among the toughest cargos due to their inherent propensity for self‐proteolysis. To implement a reliable, but functionally silent, tagging technology in a protease, we have developed a caspase‐3 variant tagged with the 11th strand of GFP that retains both enzymatic activity and structural characteristics of wild‐type caspase‐3. Only in the presence of cytosolic GFP strands 1–10 will the tagged caspase‐3 generate fluorescence to signal a non‐endosomal location. This methodology facilitates easy screening of cytosolic vs. endosomally‐entrapped proteins due to low probabilities for false positive results, and further, allows tracking of the resultant cargo''s translocation. The development of this tagged casp‐3 cytosolic reporter lays the foundation to probe caspase therapeutic properties, charge–property relationships governing successful escape, and the precise number of caspases required for apoptotic cell death.     

Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases

Mitogen‐activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less‐characterized disordered regions. We used a structurally consistent model on kinase‐docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under‐explored part of the human proteome and applied experimental tools specifically tailored to detect low‐affinity protein–protein interactions for their validation in vitro and in cell‐based assays. The combined computational and experimental approach enabled the identification of many novel MAPK‐docking motifs that were elusive for other large‐scale protein–protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase‐mediated partnerships evolved over time. The analysis suggests that most human MAPK‐binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK‐binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles.     

Long‐term nitrogen enrichment mediates the effects of nitrogen supply and co‐inoculation on a viral pathogen

Host nutrient supply can mediate host–pathogen and pathogen–pathogen interactions. In terrestrial systems, plant nutrient supply is mediated by soil microbes, suggesting a potential role of soil microbes in plant diseases beyond soil‐borne pathogens and induced plant defenses. Long‐term nitrogen (N) enrichment can shift pathogenic and nonpathogenic soil microbial community composition and function, but it is unclear if these shifts affect plant–pathogen and pathogen–pathogen interactions. In a growth chamber experiment, we tested the effect of long‐term N enrichment on infection by Barley Yellow Dwarf Virus (BYDV‐PAV) and Cereal Yellow Dwarf Virus (CYDV‐RPV), aphid‐vectored RNA viruses, in a grass host. We inoculated sterilized growing medium with soil collected from a long‐term N enrichment experiment (ambient, low, and high N soil treatments) to isolate effects mediated by the soil microbial community. We crossed soil treatments with a N supply treatment (low, high) and virus inoculation treatment (mock‐, singly‐, and co‐inoculated) to evaluate the effects of long‐term N enrichment on plant–pathogen and pathogen–pathogen interactions, as mediated by N availability. We measured the proportion of plants infected (i.e., incidence), plant biomass, and leaf chlorophyll content. BYDV‐PAV incidence (0.96) declined with low N soil (to 0.46), high N supply (to 0.61), and co‐inoculation (to 0.32). Low N soil mediated the effect of N supply on BYDV‐PAV: instead of N supply reducing BYDV‐PAV incidence, the incidence increased. Additionally, ambient and low N soil ameliorated the negative effect of co‐inoculation on BYDV‐PAV incidence. BYDV‐PAV infection only reduced chlorophyll when plants were grown with low N supply and ambient N soil. There were no significant effects of long‐term N soil on CYDV‐RPV incidence. Soil inoculant with different levels of long‐term N enrichment had different effects on host–pathogen and pathogen–pathogen interactions, suggesting that shifts in soil microbial communities with long‐term N enrichment may mediate disease dynamics.     

Adenosine triphosphate energy‐independently controls protein homeostasis with unique structure and diverse mechanisms

Proteins function in the crowded cellular environments with high salt concentrations, thus facing tremendous challenges of misfolding/aggregation which represents a pathological hallmark of aging and an increasing spectrum of human diseases. Recently, intrinsically disordered regions (IDRs) were recognized to drive liquid–liquid phase separation (LLPS), a common principle for organizing cellular membraneless organelles (MLOs). ATP, the universal energy currency for all living cells, mysteriously has concentrations of 2–12 mM, much higher than required for its previously‐known functions. Only recently, ATP was decoded to behave as a biological hydrotrope to inhibit protein LLPS and aggregation at mM. We further revealed that ATP also acts as a bivalent binder, which not only biphasically modulates LLPS driven by IDRs of human and viral proteins, but also bind to the conserved nucleic‐acid‐binding surfaces of the folded proteins. Most unexpectedly, ATP appears to act as a hydration mediator to antagonize the crowding‐induced destabilization as well as to enhance folding of proteins without significant binding. Here, this review focuses on summarizing the results of these biophysical studies and discussing their implications in an evolutionary context. By linking triphosphate with unique hydration property to adenosine, ATP appears to couple the ability for establishing hydrophobic, π‐π, π‐cation and electrostatic interactions to the capacity in mediating hydration of proteins, which is at the heart of folding, dynamics, stability, phase separation and aggregation. Consequently, ATP acquired a category of functions at ~mM to energy‐independently control protein homeostasis with diverse mechanisms, thus implying a link between cellular ATP concentrations and protein‐aggregation diseases.     

Tensor‐structured decomposition improves systems serology analysis

Systems serology provides a broad view of humoral immunity by profiling both the antigen‐binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease‐relevant antigen targets, alongside additional measurements made for single antigens or in an antigen‐generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix–tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV‐ and SARS‐CoV‐2‐infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen‐binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.