Showing papers on "Protein–protein interaction published in 2022"

A comprehensive SARS-CoV-2–human protein–protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets

Abstract: Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Human transcription factor protein interaction networks

Abstract: Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein-protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.

Improving peptide-protein docking with AlphaFold-Multimer using forced sampling

Abstract: Protein interactions are key in vital biological process. In many cases, particularly often in regulation, this interaction is between a protein and a shorter peptide fragment. Such peptides are often part of larger disordered regions of other proteins. The flexible nature of peptides enable rapid, yet specific, regulation of important functions in the cell, such as the cell life-cycle. Because of this, understanding the molecular details of these interactions are crucial to understand and alter their function, and many specialized computational methods have been developed to study them. The recent release of AlphaFold and AlphaFold-Multimer has caused a leap in accuracy for computational modeling of proteins. In this study, the ability of AlphaFold to predict which peptides and proteins interact as well as its accuracy in modeling the resulting interaction complexes are benchmarked against established methods in the fields of peptide-protein interaction prediction and modeling. We find that AlphaFold-Multimer consistently produces predicted interaction complexes with a median DockQ of 0.47 for all 112 complexes investigated. Additionally, it can be used to separate interacting from non-interacting pairs of peptides and proteins with ROC-AUC and PR-AUC of 0.78 and 0.61, respectively, best among the method benchmarked. However, the most interestingly result is the possibility to improve AlphaFold by enabling dropout at inference to sample a wider part of the conformational space. This improves the median DockQ from 0.47 to 0.56 for rank 1 and the median best DockQ improves from 0.58 to 0.72. This scheme of generating more structures with AlphaFold should be generally useful for many application involving multiple states, flexible regions and disorder.

Ultrasound: A reliable method for regulating food component interactions in protein-based food matrices

Abstract: Protein-rich foods supply the human body with energy and nutrients. Several methods have been applied for modifying the characteristics of various protein-based food matrices by altering food component interactions. Among these methods, ultrasound has received a great deal of interest since it is considered to be a promising and environmentally friendly technology. The effects of ultrasound on the covalent and noncovalent interactions of proteins with other dietary components in protein-based food matrices were discussed. The techniques for analyzing these interaction transformations and their effects on matrix characteristics were also summarized. Finally, this review examined the benefits and drawbacks of the latest literature. The response to ultrasound varied between proteins separated from food and those found in the food system, as well as different food types. Along with protein structure, the importance of food component interactions should also be emphasized, which are closely related to matrix properties. The review revealed that the sonophysical and sonochemical properties of ultrasound enhanced noncovalent and covalent interactions of proteins with other dietary components. Ultrasound is often applied before the production of final products. In this regard, the addition order of various components will also alter the effect of ultrasound on the food-ingredient interactions. Thus, it is critical to examine these changes using advanced technologies, especially large amplitude oscillatory shear. Further study regarding the sonochemical activity impact on the covalent forces of complex protein matrixes is needed along with building large-scale manufacturing equipment.

Protein–protein interaction prediction with deep learning: A comprehensive review

Abstract: Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Lipid-Protein Interactions in Plasma Membrane Organization and Function.

Abstract: Lipid-protein interactions in cells are involved in various biological processes, including metabolism, trafficking, signaling, host-pathogen interactions, and transmembrane transport. At the plasma membrane, lipid-protein interactions play major roles in membrane organization and function. Several membrane proteins have motifs for specific lipid binding, which modulate protein conformation and consequent function. In addition to such specific lipid-protein interactions, protein function can be regulated by the dynamic, collective behavior of lipids in membranes. Emerging analytical, biochemical, and computational technologies allow us to study the influence of specific lipid-protein interactions, as well as the collective behavior of membranes on protein function. In this article, we review the recent literature on lipid-protein interactions with a specific focus on the current state-of-the-art technologies that enable novel insights into these interactions. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The USP7 protein interaction network and its roles in tumorigenesis

Abstract: Ubiquitin-specific protease (USP7), also known as Herpesvirus-associated ubiquitin-specific protease (HAUSP), is a deubiquitinase. There has been significant recent attention on USP7 following the discovery that USP7 is a key regulator of the p53-MDM2 pathway. The USP7 protein is 130 kDa in size and has multiple domains which bind to a diverse set of proteins. These interactions mediate key developmental and homeostatic processes including the cell cycle, immune response, and modulation of transcription factor and epigenetic regulator activity and localization. USP7 also promotes carcinogenesis through aberrant activation of the Wnt signalling pathway and stabilization of HIF-1α. These findings have shown that USP7 may induce tumour progression and be a therapeutic target. Together with interest in developing USP7 as a target, several studies have defined new protein interactions and the regulatory networks within which USP7 functions. In this review, we focus on the protein interactions of USP7 that are most important for its cancer-associated roles.

Interrelationship between 2019-nCov receptor DPP4 and diabetes mellitus targets based on protein interaction network

Abstract: Patients with diabetes are more likely to be infected with Coronavirus disease 2019 (COVID-19), and the risk of death is significantly higher than ordinary patients. Dipeptidyl peptidase-4 (DPP4) is one of the functional receptor of human coronavirus. Exploring the relationship between diabetes mellitus targets and DPP4 is particularly important for the management of patients with diabetes and COVID-19. We intend to study the protein interaction through the protein interaction network in order to find a new clue for the management of patients with diabetes with COVID-19. Diabetes mellitus targets were obtained from GeneCards database. Targets with a relevance score exceeding 20 were included, and DPP4 protein was added manually. The initial protein interaction network was obtained through String. The targets directly related to DPP4 were selected as the final analysis targets. Importing them into String again to obtain the protein interaction network. Module identification, gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were carried out respectively. The impact of DPP4 on the whole network was analyzed by scoring the module where it located. 43 DPP4-related proteins were finally selected from the diabetes mellitus targets and three functional modules were found by the cluster analysis. Module 1 was involved in insulin secretion and glucagon signaling pathway, module 2 and module 3 were involved in signaling receptor binding. The scoring results showed that LEP and apoB in module 1 were the highest, and the scores of INS, IL6 and ALB of cross module associated proteins of module 1 were the highest. DPP4 is widely associated with key proteins in diabetes mellitus. COVID-19 may affect DPP4 in patients with diabetes mellitus, leading to high mortality of diabetes mellitus combined with COVID-19. DPP4 inhibitors and IL-6 antagonists can be considered to reduce the effect of COVID-19 infection on patients with diabetes.

Quantitative fragmentomics allow affinity mapping of interactomes

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

Efficacy and specificity of inhibitors of BCL-2 family protein interactions assessed by affinity measurements in live cells

Abstract: Cytoplasmic and membrane-bound BCL-2 family proteins regulate apoptosis, a form of programmed cell death, via dozens of binary protein interactions confounding measurement of the effects of inhibitors in live cells. In cancer, apoptosis is frequently dysregulated, and cell survival depends on antiapoptotic proteins binding to and inhibiting proapoptotic BH3 proteins. The clinical success of BH3 mimetic inhibitors of antiapoptotic proteins has spawned major efforts by the pharmaceutical industry to develop molecules with different specificities and higher affinities. Here, quantitative fast fluorescence lifetime imaging microscopy enabled comparison of BH3 mimetic drugs in trials and preclinical development by measuring drug effects on binding affinities of interacting protein pairs in live cells. Both selectivity and efficacy were assessed for 15 inhibitors of four antiapoptotic proteins for each of six BH3 protein ligands. While many drugs target the designed interaction, most also have unexpected selectivity and poor efficacy in cells.

OUP accepted manuscript

Abstract: Biogenesis of spliceosomal small nuclear ribonucleoproteins (snRNPs) and their recycling after splicing require numerous assembly/recycling factors whose modes of action are often poorly understood. The intrinsically disordered TSSC4 protein has been identified as a nuclear-localized U5 snRNP and U4/U6-U5 tri-snRNP assembly/recycling factor, but how TSSC4's intrinsic disorder supports TSSC4 functions remains unknown. Using diverse interaction assays and cryogenic electron microscopy-based structural analysis, we show that TSSC4 employs four conserved, non-contiguous regions to bind the PRPF8 Jab1/MPN domain and the SNRNP200 helicase at functionally important sites. It thereby inhibits SNRNP200 helicase activity, spatially aligns the proteins, coordinates formation of a U5 sub-module and transiently blocks premature interaction of SNRNP200 with at least three other spliceosomal factors. Guided by the structure, we designed a TSSC4 variant that lacks stable binding to the PRPF8 Jab1/MPN domain or SNRNP200 in vitro. Comparative immunoprecipitation/mass spectrometry from HEK293 nuclear extract revealed distinct interaction profiles of wild type TSSC4 and the variant deficient in PRPF8/SNRNP200 binding with snRNP proteins, other spliceosomal proteins as well as snRNP assembly/recycling factors and chaperones. Our findings elucidate molecular strategies employed by an intrinsically disordered protein to promote snRNP assembly, and suggest multiple TSSC4-dependent stages during snRNP assembly/recycling.

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

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

Multiplexed Isobaric Quantitative Cross-Linking Reveals Drug-Induced Interactome Changes in Breast Cancer Cells.

Abstract: The study of protein structures and interactions is critical to understand their function. Chemical cross-linking of proteins with mass spectrometry (XL-MS) is a rapidly developing structural biology technique able to provide valuable insight into protein conformations and interactions, even as they exist within their native cellular environment. Quantitative analysis of cross-links can reveal protein conformational and interaction changes that occur as a result of altered biological states, environmental conditions, or pharmacological perturbations. Our laboratory recently developed an isobaric quantitative protein interaction reporter (iqPIR) cross-linking strategy for comparative interactome studies. This strategy relies on isotope encoded chemical cross-linkers that have the same molecular mass yet produce unique and specific isotope signatures upon fragmentation in the mass spectrometer which can be used for quantitative analysis of cross-linked peptides. The initial set of iqPIR molecules allowed for binary comparisons. Here, we describe the in vivo application of an extended set of six iqPIR reagents (6-plex iqPIR), allowing multiplexed quantitative interactome analysis of up to six biological samples in a single LC-MS acquisition. Multiplexed iqPIR is demonstrated on MCF-7 breast cancer cells treated with five different Hsp90 inhibitors revealing large scale protein conformational and interaction changes specific to the molecular class of the inhibitors.

Signaling Pathways and Protein-Protein Interaction of Vimentin in Invasive and Migration Cells: A Review.

Abstract: The vimentin (encoded by VIM) is one of the 70 human intermediate filaments (IFs), building highly dynamic and cell-type-specific web networks in the cytoplasm. Vim-/- mice exhibit process defects associated with cell differentiation, which can have implications for understanding cancer and disease. This review showed recent reports from studies that unveiled vimentin intermediate filaments (VIFs) as an essential component of the cytoskeleton, followed by a description of vimentin's physiological functions and process reports in VIF signaling pathway and gene network studies. The main focus of the discussion is on vital signaling pathways associated with how VIF coordinates invasion cells and migration. The current research will open up multiple processes to research the function of VIF and other IF proteins in cellular and molecular biology, and they will lead to essential insights into different VIF levels for the invasive metastatic cancer cells. Enrich GO databases used Gene Ontology and Pathway Enrichment Analysis. Estimation with STRING online was to predict the functional and molecular interactions of proteins-protein with Cytoscape analysis to search and select the master genes. Using Cytoscape and STRING analysis, we presented eight genes, RhoA, Smad3, Akt1, Cdk2, Rock1, Rock2, Mapk1, and Mapk8, as the essential protein-protein interaction with vimentin involved in the invasion.

Dynamic spatiotemporal determinants modulate GPCR:G protein coupling selectivity and promiscuity

Abstract: Abstract Recent studies have shown that G protein coupled receptors (GPCRs) show selective and promiscuous coupling to different Gα protein subfamilies and yet the mechanisms of the range of coupling preferences remain unclear. Here, we use Molecular Dynamics (MD) simulations on ten GPCR:G protein complexes and show that the location (spatial) and duration (temporal) of intermolecular contacts at the GPCR:Gα protein interface play a critical role in how GPCRs selectively interact with G proteins. We identify that some GPCR:G protein interface contacts are common across Gα subfamilies and others specific to Gα subfamilies. Using large scale data analysis techniques on the MD simulation snapshots we derive a spatio-temporal code for contacts that confer G protein selective coupling and validated these contacts using G protein activation BRET assays. Our results demonstrate that promiscuous GPCRs show persistent sampling of the common contacts more than G protein specific contacts. These findings suggest that GPCRs maintain contact with G proteins through a common central interface, while the selectivity comes from G protein specific contacts at the periphery of the interface.

SARS-COV-2 spike binding to ACE2 in living cells monitored by TR-FRET

Abstract: Targeting the interaction between the SARS-CoV-2 spike protein and human ACE2, its primary cell membrane receptor, is a promising therapeutic strategy to prevent viral entry. Recent in vitro studies revealed that the receptor binding domain (RBD) of the spike protein plays a prominent role in ACE2 binding, yet a simple and quantitative assay for monitoring this interaction in a cellular environment is lacking. Here, we developed an RBD-ACE2 binding assay that is based on time-resolved FRET, which reliably monitors the interaction in a physiologically relevant and cellular context. Because it is modular, the assay can monitor the impact of different cellular components, such as heparan sulfate, lipids, and membrane proteins on the RBD-ACE2 interaction and it can be extended to the full-length spike protein. The assay is HTS compatible and can detect small-molecule competitive and allosteric modulators of the RBD-ACE2 interaction with high relevance for SARS-CoV-2 therapeutics.

Protein‐protein interaction networks as miners of biological discovery

Abstract: Protein‐protein interactions (PPIs) form the basis of a myriad of biological pathways and mechanism, such as the formation of protein complexes or the components of signaling cascades. Here, we reviewed experimental methods for identifying PPI pairs, including yeast two‐hybrid (Y2H), mass spectrometry (MS), co‐localization, and co‐immunoprecipitation. Furthermore, a range of computational methods leveraging biochemical properties, evolution history, protein structures and more have enabled identification of additional PPIs. Given the wealth of known PPIs, we reviewed important network methods to construct and analyze networks of PPIs. These methods aid biological discovery through identifying hub genes and dynamic changes in the network, and have been thoroughly applied in various fields of biological research. Lastly, we discussed the challenges and future direction of research utilizing the power of PPI networks.

Protein–protein and protein–lipid interactions of pore-forming BCL-2 family proteins in apoptosis initiation

Abstract: Apoptosis is a common cell death program that is important in human health and disease. Signaling in apoptosis is largely driven through protein-protein interactions. The BCL-2 family proteins function in protein-protein interactions as key regulators of mitochondrial poration, the process that initiates apoptosis through the release of cytochrome c, which activates the apoptotic caspase cascade leading to cellular demolition. The BCL-2 pore-forming proteins BAK and BAX are the key executors of mitochondrial poration. We review the state of knowledge of protein-protein and protein-lipid interactions governing the apoptotic function of BAK and BAX, as determined through X-ray crystallography and NMR spectroscopy studies. BAK and BAX are dormant, globular α-helical proteins that participate in protein-protein interactions with other pro-death BCL-2 family proteins, transforming them into active, partially unfolded proteins that dimerize and associate with and permeabilize mitochondrial membranes. We compare the protein-protein interactions observed in high-resolution structures with those derived in silico by AlphaFold, making predictions based on combining experimental and in silico approaches to delineate the structural basis for novel protein-protein interaction complexes of BCL-2 family proteins.

Structural insight into the Scribble PDZ domains interaction with the oncogenic Human T‐cell lymphotrophic virus‐1 (HTLV‐1) Tax1 PBM

Abstract: Scribble (Scrib) is a highly conserved cell polarity regulator that harbours potent tumour suppressor activity and plays an important role in cell migration. Dysregulation of polarity is associated with poor prognosis during viral infections. Human T‐cell lymphotrophic virus‐1 (HTLV‐1) encodes for the oncogenic Tax1 protein, a modulator of the transcription of viral and human proteins that can cause cell cycle dysregulation as well as a loss of genomic integrity. Previous studies established that Scribble interacts with Tax1 via its C‐terminal PDZ‐binding motif (PBM), leading to aggregation of polarity regulators and subsequent perturbation of host cell adhesion, proliferation, and signalling. Using isothermal titration calorimetry, we now show that all four PDZ domains of Scribble bind to Tax1 PBM. We then determined crystal structures of Scribble PDZ1, PDZ2 and PDZ3 domains bound to Tax1 PBM. Our findings establish a structural basis for Tax1‐mediated subversion of Scribble‐mediated cell polarity signalling and provide the platform for mechanistic studies to examine Tax1 induced mislocalization of Scribble and the associated changes in cellular architecture and subsequent tumorigenesis.

Preferential Interactions of a Crowder Protein with the Specific Binding Site of a Native Protein Complex.

Abstract: Nonspecific binding of crowder proteins with functional proteins is likely prevalent in vivo, yet direct quantitative evidence, let alone residue-specific information, is scarce. Here we present nuclear magnetic resonance (NMR) characterization showing that bovine serum albumin weakly but preferentially interacts with the histidine carrier protein (HPr). Notably, the binding interface overlaps with that for HPr's specific partner protein, EIN, leading to competition. The crowder protein thus decreases the EIN-HPr binding affinity and accelerates the dissociation of the native complex. In contrast, Ficoll-70 stabilizes the native complex and slows its dissociation, as one would expect from excluded-volume and microviscosity effects. Our atomistic modeling of macromolecular crowding rationalizes the experimental data and provides quantitative insights into the energetics of protein-crowder interactions. The integrated NMR and modeling study yields benchmarks for the effects of crowded cellular environments on protein-protein specific interactions, with implications for evolution regarding how nonspecific binding can be minimized or exploited.

The First Class of Small Molecules Potently Disrupting the YAP‐TEAD Interaction by Direct Competition

Abstract: Inhibition of the YAP‐TEAD protein‐protein interaction is an attractive therapeutic concept under intense investigation with the objective to treat cancers associated with a dysregulation of the Hippo pathway. However, owing to the very extended surface of interaction of the two proteins, the identification of small drug‐like molecules able to efficiently prevent YAP from binding to TEAD by direct competition has been elusive so far. We disclose here the discovery of the first class of small molecules potently inhibiting the YAP‐TEAD interaction by binding at one of the main interaction sites of YAP at the surface of TEAD. These inhibitors, providing a path forward to pharmacological intervention in the Hippo pathway, evolved from a weakly active virtual screening hit advanced to high potency by structure‐based design.