Protein-protein interactions: methods for detection and analysis

HawkDock version 2: an updated web server to predict and analyze the structures of protein-protein complexes

Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integrated computational platform for predicting and analyzing protein-protein complexes. Over the past 6 years, HawkDock has successfully processed >234 000 computational tasks. In this study, an updated version of HawkDock was developed with the following advancements: (1) a deep learning-based flexible docking method, GeoDock, has been integrated to improve docking accuracy, particularly for apo-protein structures; (2) the VD-MM/GBSA method, which outperforms conventional MM/GBSA approaches in predicting binding affinities, has been implemented; (3) a new Mutation Analysis Module has been added to systematically evaluate the energetic impacts of amino acid mutations on protein-protein binding; (4) the server has been migrated to a high-performance cluster with Amber upgraded to version 24. Here, we describe the general protocol of HawkDock2, with a particular focus on its new features related to flexible docking, VD-MM/GBSA affinity prediction, and amino acid residue mutations. Comprehensive validation studies have demonstrated the reliability and effectiveness of these new features. HawkDock2 will remain freely accessible to all users at http://cadd.zju.edu.cn/hawkdock/.

Amylase trypsin inhibitors activation of toll-like receptor 4 revisited: The dominance of lipopolysaccharides contamination

Amylase trypsin inhibitors (ATIs) potentially play a role in irritable bowel syndrome (IBS) and non-celiac wheat sensitivity (NCWS). These cereal-derived inhibitors are suspected to bind to the TLR4-MD2-CD14 complex and trigger intestinal pro-inflammatory responses, but confirmation through more extensive cell line studies is required. In this study, an amylase trypsin inhibitors enriched fraction (AEF) was prepared and characterized. Then, AEF binding potential to TLR4-MD2-CD14 was investigated using the human TLR4 reporter cell line HEK-BlueTM. The method took into account the presence of lipopolysaccharides (LPS) using Polymyxin B (PMB) to block LPS binding to TLR4. Proteinase K was also used to hydrolyze AEF proteins and eliminate their induced response. The cell line experiments showed that PMB treatment of AEF reduced the binding signal by 92 %. Complete hydrolysis of the protein by Proteinase K doubled the TLR4 activation signal and might indicate that protein-LPS complexation reduced LPS's ability to activate the TLR4-receptor. These finding underline the need for future work to consider the non-protein part in cell assays, especially the LPS bias. Altogether, these results indicated that LPS activates TLR4-MD2-CD14 and challenges ATIs' intestinal inflammation capacity contribution in irritable bowel syndrome and non-celiac wheat sensitivity.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Identification, functional characterization and immune response profiles of interleukin-10 in Nibea albiflora

Interleukin-10 (IL-10) is an immunosuppressive cytokine, which plays a vital role in regulating inflammation for inhibiting the generation and function of pro-inflammatory cytokines in vivo or in vitro. In the present study, the full length cDNA of IL-10 was characterized from Nibea albiflora (named as NaIL-10) of 1238 base pairs (bp), containing a 5'-UTR (untranslated region) of 350 bp, a 3'-UTR of 333 bp and an open reading frame (ORF) of 555 bp (Fig. 1A) to encode 184 amino acid residues with a signal peptide at the N-terminus. The sequence analysis showed that NaIL-10 possessed the typical IL-10 family symbolic motif and conversed cysteine residues, similar to its teleost orthologues. Real-time PCR indicated that NaIL-10 had wide distribution in different healthy tissues, with a relatively high expression in immune-related tissues (head kidney, spleen, kidney, liver and gill). Significantly, up-regulations of NaIL-10 after infection against Vibrio parahaemolyticus, Vibrio alginolyticus and Poly I:C were also observed. Subcellular localization manifested that NaIL-10 mainly distributed in the cytoplasm unevenly and aggregately, and there was also a small amount on the cell membrane, indicating that NaIL-10 was secreted to the extracellular space as the known IL-10 homologous molecules. It could co-locate with IL-10 Rα on the membrane of HEK293T cells for their potential interaction, and GST pull-down and Co-IP studies certified the specific and direct interaction between NaIL-10 and NaIL-10 Rα, confirming that an IL-10 ligand-receptor system existed in N.albiflora. The expression of pro-inflammatory cytokines, including TNF-α, IL-6, IL-1β, were dramatically inhibited in LPS-stimulated RAW264.7 macrophages pre-incubated with recombinant NaIL-10 protein, demonstrating its anti-inflammatory roles. Taken together, the results demonstrated the existence of IL-10 ligand-receptor system in N.albiflora for the first time, and indicated the suppressive function of NaIL-10 on pro-inflammatory cytokine expression in inflammatory response, which would be conducive to better comprehending the role of IL-10 in the immunomodulatory mechanisms of teleost.

Heliorhodopsin-mediated light-modulation of ABC transporter

Heliorhodopsins (HeRs) have been hypothesized to have widespread functions. Recently, the functions for few HeRs have been revealed; however, the hypothetical functions remain largely unknown. Herein, we investigate light-modulation of heterodimeric multidrug resistance ATP-binding cassette transporters (OmrDE) mediated by Omithinimicrobium cerasi HeR. In this study, we classifiy genes flanking the HeR-encoding genes and identify highly conservative residues for protein-protein interactions. Our results reveal that the interaction between OcHeR and OmrDE shows positive cooperatively sequential binding through thermodynamic parameters. Moreover, light-induced OcHeR upregulates OmrDE drug transportation. Hence, the binding may be crucial to drug resistance in O. cerasi as it survives in a drug-containing habitat. Overall, we unveil a function of HeR as regulatory rhodopsin for multidrug resistance. Our findings suggest potential applications in optogenetic technology.

Responses to commentaries on "The gene: An appraisal"

The central conclusions of "The Gene: An Appraisal" are that genetic variance does not underpin biological evolution, and, therefore, that genes are not Mendel's units of inheritance. In this response, I will address the criticisms I have received via commentaries on that paper by defending the following statements: 1. Epistasis does not explain the power-law fitness profile of the Long-Term Evolution Experiment (LTEE). The data from the evolution of natural systems displays the power-law form ubiquitously. Epistasis plays no role in evolution. 2. The common characteristics of living things (natural systems) are described by the principle of least action in de Maupertuis's original form, which is synonymous with the 2nd law of thermodynamics and Newton's 2nd law of motion in its complete form, i.e., F = dp/dt. Organisms strive to achieve free energy balance with their environments. 3. Based on an appraisal of the scientific environment between 1880 and 1911, I conclude that Johannsen's genotype conception was perhaps, the only option available to him. 4. The power-law fitness profile of the LTEE falsifies Fisher's Genetical Theory of Natural Selection, Johannsen's genotype conception, and the idea that 'Darwinian evolution' is an exception to the generic thermodynamic process of evolution in natural systems. 5. The use of the technique of genome-wide association to identify the causes and the likelihoods of inherited common diseases and behavioural traits is a 'wild goose chase' because genes are not the units of inheritance.

A novel bivalent interaction mode underlies a non-catalytic mechanism for Pin1-mediated protein kinase C regulation

Regulated hydrolysis of the phosphoinositide phosphatidylinositol(4,5)-bis-phosphate to diacylglycerol and inositol-1,4,5-P3 defines a major eukaryotic pathway for translation of extracellular cues to intracellular signaling circuits. Members of the lipid-activated protein kinase C isoenzyme family (PKCs) play central roles in this signaling circuit. One of the regulatory mechanisms employed to downregulate stimulated PKC activity is via a proteasome-dependent degradation pathway that is potentiated by peptidyl-prolyl isomerase Pin1. Here, we show that contrary to prevailing models, Pin1 does not regulate conventional PKC isoforms α and βII via a canonical cis-trans isomerization of the peptidyl-prolyl bond. Rather, Pin1 acts as a PKC binding partner that controls PKC activity via sequestration of the C-terminal tail of the kinase. The high-resolution structure of full-length Pin1 complexed to the C-terminal tail of PKCβII reveals that a novel bivalent interaction mode underlies the non-catalytic mode of Pin1 action. Specifically, Pin1 adopts a conformation in which it uses the WW and PPIase domains to engage two conserved phosphorylated PKC motifs, the turn motif and hydrophobic motif, respectively. Hydrophobic motif is a non-canonical Pin1-interacting element. The structural information combined with the results of extensive binding studies and experiments in cultured cells suggest that non-catalytic mechanisms represent unappreciated modes of Pin1-mediated regulation of AGC kinases and other key enzymes/substrates.

Anti-PD-1 treatment protects against seizure by suppressing sodium channel function

AIMS

Although programmed cell death protein 1 (PD-1) typically serves as a target for immunotherapies, a few recent studies have found that PD-1 is expressed in the nervous system and that neuronal PD-1 might play a crucial role in regulating neuronal excitability. However, whether brain-localized PD-1 is involved in seizures and epileptogenesis is still unknown and worthy of in-depth exploration.

METHODS

The existence of PD-1 in human neurons was confirmed by immunohistochemistry, and PD-1 expression levels were measured by real-time quantitative PCR (RT-qPCR) and western blotting. Chemoconvulsants, pentylenetetrazol (PTZ) and cyclothiazide (CTZ), were applied for the establishment of in vivo (rodents) and in vitro (primary hippocampal neurons) models of seizure, respectively. SHR-1210 (a PD-1 monoclonal antibody) and sodium stibogluconate (SSG, a validated inhibitor of SH2-containing protein tyrosine phosphatase-1 [SHP-1]) were administrated to investigate the impact of PD-1 pathway blockade on epileptic behaviors of rodents and epileptiform discharges of neurons. A miRNA strategy was applied to determine the impact of PD-1 knockdown on neuronal excitability. The electrical activities and sodium channel function of neurons were determined by whole-cell patch-clamp recordings. The interaction between PD-1 and α-6 subunit of human voltage-gated sodium channel (Nav1.6) was validated by performing co-immunostaining and co-immunoprecipitation (co-IP) experiments.

RESULTS

Our results reveal that PD-1 protein and mRNA levels were upregulated in lesion cores compared with perifocal tissues of surgically resected specimens from patients with intractable epilepsy. Furthermore, we show that anti-PD-1 treatment has anti-seizure effects both in vivo and in vitro. Then, we reveal that PD-1 blockade can alter the electrophysiological properties of sodium channels. Moreover, we reveal that PD-1 acts together with downstream SHP-1 to regulate sodium channel function and hence neuronal excitability. Further investigation suggests that there is a direct interaction between neuronal PD-1 and Nav1.6.

CONCLUSION

Our study reveals that neuronal PD-1 plays an important role in epilepsy and that anti-PD-1 treatment protects against seizures by suppressing sodium channel function, identifying anti-PD-1 treatment as a novel therapeutic strategy for epilepsy.

Kinetic study of membrane protein interactions: from three to two dimensions

Molecular interactions are contingent upon the system's dimensionality. Notably, comprehending the impact of dimensionality on protein-protein interactions holds paramount importance in foreseeing protein behaviour across diverse scenarios, encompassing both solution and membrane environments. Here, we unravel interactions among membrane proteins across various dimensionalities by quantifying their binding rates through fluorescence recovery experiments. Our findings are presented through the examination of two protein systems: streptavidin-biotin and a protein complex constituting a bacterial efflux pump. We present here an original approach for gauging a two-dimensional binding constant between membrane proteins embedded in two opposite membranes. The quotient of protein binding rates in solution and on the membrane represents a metric denoting the exploration distance of the interacting sites-a novel interpretation.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

BioID Analysis of Actin-Binding Proteins

Proteins often exist and function as part of higher-order complexes or networks. A challenge is to identify the universe of proximal and interacting partners for a given protein. We describe how the high-activity promiscuous biotin ligase called TurboID is fused to the actin-binding peptide LifeAct to label by biotinylation proteins that bind, or are in close proximity, to actin. The rapid enzyme kinetics of TurboID allows the profiles of actin-binding proteins to be compared under different conditions, such as acute disruption of filamentous actin structures with cytochalasin D.

Proximity Proteomics Revealed Aberrant mRNA Splicing Elicited by ALS-Linked Profilin-1 Mutants

Profilin 1 (PFN1) is a cytoskeleton protein that modulates actin dynamics through binding to monomeric actin and polyproline-containing proteins. Mutations in PFN1 have been linked to the pathogenesis of familial amyotrophic lateral sclerosis (ALS). Here, we employed an unbiased proximity labeling strategy in combination with proteomic analysis for proteome-wide profiling of proteins that differentially interact with mutant and wild-type (WT) PFN1 proteins in human cells. We uncovered 11 mRNA splicing proteins that are preferentially enriched in the proximity proteomes of the two ALS-linked PFN1 variants, C71G and M114T, over that of wild-type PFN1. We validated the preferential interactions of the ALS-linked PFN1 variants with two mRNA splicing factors, hnRNPC and U2AF2, by immunoprecipitation, followed with immunoblotting. We also found that the two ALS-linked PFN1 variants promoted the exonization of Alu elements in the mRNAs of MTO1, TCFL5, WRN and POLE genes in human cells. Together, we showed that the two ALS-linked PFN1 variants interacted preferentially with mRNA splicing proteins, which elicited aberrant exonization of the Alu elements in mRNAs. Thus, our work provided pivotal insights into the perturbations of ALS-linked PFN1 variants in RNA biology and their potential contributions to ALS pathology.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

Genome-wide identification of the opsin protein in Leptosphaeria maculans and comparison with other fungi (pathogens of Brassica napus)

The largest family of transmembrane receptors are G-protein-coupled receptors (GPCRs). These receptors respond to perceived environmental signals and infect their host plants. Family A of the GPCR includes opsin. However, there is little known about the roles of GPCRs in phytopathogenic fungi. We studied opsin in Leptosphaeria maculans, an important pathogen of oilseed rape (Brassica napus) that causes blackleg disease, and compared it with six other fungal pathogens of oilseed rape. A phylogenetic tree analysis of 31 isoforms of the opsin protein showed six major groups and six subgroups. All three opsin isoforms of L. maculans are grouped in the same clade in the phylogenetic tree. Physicochemical analysis revealed that all studied opsin proteins are stable and hydrophobic. Subcellular localization revealed that most isoforms were localized in the endoplasmic reticulum membrane except for several isoforms in Verticillium species, which were localized in the mitochondrial membrane. Most isoforms comprise two conserved domains. One conserved motif was observed across all isoforms, consisting of the BACTERIAL_OPSIN_1 domain, which has been hypothesized to have an identical sensory function. Most studied isoforms showed seven transmembrane helices, except for one isoform of V. longisporum and four isoforms of Fusarium oxysporum. Tertiary structure prediction displayed a conformational change in four isoforms of F. oxysporum that presumed differences in binding to other proteins and sensing signals, thereby resulting in various pathogenicity strategies. Protein-protein interactions and binding site analyses demonstrated a variety of numbers of ligands and pockets across all isoforms, ranging between 0 and 13 ligands and 4 and 10 pockets. According to the phylogenetic analysis in this study and considerable physiochemically and structurally differences of opsin proteins among all studied fungi hypothesized that this protein acts in the pathogenicity, growth, sporulation, and mating of these fungi differently.

Function and Characteristic Analysis of Candidate PEAR Proteins in Populus yunnanensis

PEAR proteins are a type of plant-specific DNA binding with one finger (Dof) transcription factors that play a key role in the regulation of plant growth, especially during phloem cell growth and seed germination in Arabidopsis. However, the identification, characteristics and function of PEAR proteins, particularly in woody plants, need to be further studied. In the present study, 43 candidate PEAR proteins harboring the conserved Zf-Dof domain were obtained in Populus yunnanensis. Based on phylogenetic and structural analysis, 10 representative PEAR candidates were selected, belonging to different phylogenetic groups. The functions of PEAR proteins in the stress response, signal transduction, and growth regulation of stem cambium and roots undergoing vigorous cell division in Arabidopsis were revealed based on their expression patterns as characterized by qRT-PCR analysis, in accordance with the results of cis-element analysis. In vitro experiments showed that the interaction of transcription factor (E2F) and cyclin indirectly reflects the growth regulation function of PEAR through light signaling and cell-cycle regulation. Therefore, our results provide new insight into the identity of PEAR proteins and their function in stress resistance and vigorous cell division regulation of tissues in P. yunnanensis, which may serve as a basis for further investigation of the functions and characteristics of PEAR proteins in other plants.

A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction

Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions.

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

Medicinal Chemistry Strategies for the Development of Inhibitors Disrupting β-Catenin's Interactions with Its Nuclear Partners

Dysregulation of the Wnt/β-catenin signaling pathway is strongly associated with various aspects of cancer, including tumor initiation, proliferation, and metastasis as well as antitumor immunity, and presents a promising opportunity for cancer therapy. Wnt/β-catenin signaling activation increases nuclear dephosphorylated β-catenin levels, resulting in β-catenin binding to TCF and additional cotranscription factors, such as BCL9, CBP, and p300. Therefore, directly disrupting β-catenin's interactions with these nuclear partners holds promise for the effective and selective suppression of the aberrant activation of Wnt/β-catenin signaling. Herein, we summarize recent advances in biochemical techniques and medicinal chemistry strategies used to identify potent peptide-based and small-molecule inhibitors that directly disrupt β-catenin's interactions with its nuclear binding partners. We discuss the challenges involved in developing drug-like inhibitors that target the interactions of β-catenin and its nuclear binding partner into therapeutic agents.

The Interactome of Protein, DNA, and RNA

Proteins participate in many processes of the organism and are very important for maintaining the health of the organism. However, proteins cannot function independently in the body. They must interact with proteins, DNA, RNA, and other substances to perform biological functions and maintain the body's health. At present, there are many experimental methods and software tools that can detect and predict the interaction between proteins and other substances. There are also many databases that record the interaction between proteins and other substances. This article mainly describes protein-protein, protein-DNA, and protein-RNA interactions in detail by introducing some commonly used experimental methods, the software tools produced with the accumulation of experimental data and the rapid development of machine learning, and the related databases that record the relationship between proteins and some substances. By this review, we hope that through the analysis and summary of various aspects, it will be convenient for researchers to conduct further research on protein interactions.

Methods to Measure NLR Oligomerization I: Size Exclusion Chromatography, Co-immunoprecipitation, and Cross-Linking

Protein oligomerization is a common principle of regulating cellular responses. Oligomerization of NLRs is essential for the formation of NLR signaling platforms and can be detected by several biochemical techniques. Some of these biochemical methods can be combined with functional assays, such as caspase-1 activity assay. Size exclusion chromatography (SEC) allows separation of native protein lysates into different sized complexes by FPLC for follow-up analysis. Using co-immunoprecipitation (co-IP), combined with SEC or on its own, enables subsequent antibody-based purification of NLR complexes and associated proteins, which can then be analyzed by immunoblot and/or subjected to functional caspase-1 activity assay. Native gel electrophoresis also allows detection of the NLR oligomerization state by immunoblot. Chemical cross-linking covalently joins two or more molecules, thus capturing the oligomeric state with high sensitivity and stability. ASC oligomerization has been successfully used as readout for NLR/ALR inflammasome activation in response to various PAMPs and DAMPs in human and mouse macrophages and THP-1 cells. Here, we provide a detailed description of the methods used for NLRP7 oligomerization in response to infection with Staphylococcus aureus (S. aureus) in primary human macrophages, co-immunoprecipitation, and immunoblot analysis of NLRP7 and NLRP3 inflammasome complexes as well as caspase-1 activity assays. Also, ASC oligomerization is shown in response to dsDNA, LPS/ATP, and LPS/nigericin in mouse bone marrow-derived macrophages (BMDMs) and/or THP-1 cells or human primary macrophages.

Detection of Protein-Protein Interactions Utilizing the Split-Ubiquitin Membrane-Based Yeast Two-Hybrid System

Identifying the interactors of a protein is a key step in understanding its possible cellular function(s). Among the various methods that can be used to study protein-protein interactions (PPIs), the yeast two-hybrid (Y2H) assay is one of the most standardized, sensitive, and cost-effective in vivo methods available. The most commonly used GAL4-based Y2H system utilizes the yeast transcription factor GAL4 to detect interactions between soluble proteins. By virtue of involving a transcription factor, the protein-protein interactions occur in the nucleus. The split-ubiquitin Y2H system offers an alternative to the traditional GAL4-based Y2H system and takes advantage of the reconstitution of split-ubiquitin in the cytosol to identify interactions between two proteins. Moreover, new membranous and soluble interacting partner(s) can be identified by screening a target protein against proteins produced from a cDNA library using this system.

Protein binding sites for drug design

Drug development is a lengthy and challenging process that can be accelerated at early stages by new mathematical approaches and modern computers. To address this important issue, we are developing new mathematical solutions for the detection and characterization of protein binding sites that are important for new drug development. In this review, we present algorithms based on graph theory combined with molecular dynamics simulations that we have developed for studying biological target proteins to provide important data for optimizing the early stages of new drug development. A particular focus is the development of new protein binding site prediction algorithms (ProBiS) and new web tools for modeling pharmaceutically interesting molecules-ProBiS Tools (algorithm, database, web server), which have evolved into a full-fledged graphical tool for studying proteins in the proteome. ProBiS differs from other structural algorithms in that it can align proteins with different folds without prior knowledge of the binding sites. It allows detection of similar binding sites and can predict molecular ligands of various types of pharmaceutical interest that could be advanced to drugs to treat a disease, based on the entire Protein Data Bank (PDB) and AlphaFold database, including proteins not yet in the PDB. All ProBiS Tools are freely available to the academic community at http://insilab.org and https://probis.nih.gov.

Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process

BACKGROUND

In cell signaling pathways, proteins interact with each other to determine cell fate in response to either cell-extrinsic (micro-environmental) or intrinsic cues. One of the well-studied pathways, the mitogen-activated protein kinase (MAPK) signaling pathway, regulates cell processes such as differentiation, proliferation, apoptosis, and survival in response to various micro-environmental stimuli in eukaryotes. Upon micro-environmental stimulus, receptors on the cell membrane become activated. Activated receptors initiate a cascade of protein activation in the MAPK pathway. This activation involves protein binding, creating scaffold proteins, which are known to facilitate effective MAPK signaling transduction.

RESULTS

This paper presents a novel mathematical model of a cell signaling pathway coordinated by protein scaffolding. The model is based on the extended Boolean network approach with stochastic processes. Protein production or decay in a cell was modeled considering the stochastic process, whereas the protein-protein interactions were modeled based on the extended Boolean network approach. Our model fills a gap in the binary set applied to previous models. The model simultaneously considers the stochastic process directly. Using the model, we simulated a simplified mitogen-activated protein kinase (MAPK) signaling pathway upon stimulation of both a single receptor at the initial time and multiple receptors at several time points. Our simulations showed that the signal is amplified as it travels down to the pathway from the receptor, generating substantially amplified downstream ERK activity. The noise generated by the stochastic process of protein self-activity in the model was also amplified as the signaling propagated through the pathway.

CONCLUSIONS

The signaling transduction in a simplified MAPK signaling pathway could be explained by a mathematical model based on the extended Boolean network model with a stochastic process. The model simulations demonstrated signaling amplifications when it travels downstream, which was already observed in experimental settings. We also highlight the importance of stochastic activity in regulating protein inactivation.

Advances in endogenous RNA pull-down: A straightforward dextran sulfate-based method enhancing RNA recovery

Detecting RNA/RNA interactions in the context of a given cellular system is crucial to gain insights into the molecular mechanisms that stand beneath each specific RNA molecule. When it comes to non-protein coding RNA (ncRNAs), and especially to long noncoding RNAs (lncRNAs), the reliability of the RNA purification is dramatically dependent on their abundance. Exogenous methods, in which lncRNAs are in vitro transcribed and incubated with protein extracts or overexpressed by cell transfection, have been extensively used to overcome the problem of abundance. However, although useful to study the contribution of single RNA sub-modules to RNA/protein interactions, these exogenous practices might fail in revealing biologically meaningful contacts occurring in vivo and risk to generate non-physiological artifacts. Therefore, endogenous methods must be preferred, especially for the initial identification of partners specifically interacting with elected RNAs. Here, we apply an endogenous RNA pull-down to lncMN2-203, a neuron-specific lncRNA contributing to the robustness of motor neurons specification, through the interaction with miRNA-466i-5p. We show that both the yield of lncMN2-203 recovery and the specificity of its interaction with the miRNA dramatically increase in the presence of Dextran Sulfate Sodium (DSS) salt. This new set-up may represent a powerful means for improving the study of RNA-RNA interactions of biological significance, especially for those lncRNAs whose role as microRNA (miRNA) sponges or regulators of mRNA stability was demonstrated.

Investigating Interactions Between Histone Modifying Enzymes and Transcription Factors in vivo by Fluorescence Resonance Energy Transfer

Epigenetic regulation of gene expression is commonly affected by histone modifying enzymes (HMEs) that generate heterochromatic or euchromatic histone marks for transcriptional repression or activation, respectively. HMEs are recruited to their target chromatin by transcription factors (TFs). Thus, detecting and characterizing direct interactions between HMEs and TFs are critical for understanding their function and specificity better. These studies would be more biologically relevant if performed in vivo within living tissues. Here, a protocol is described for visualizing interactions in plant leaves between a plant histone deubiquitinase and a plant transcription factor using fluorescence resonance energy transfer (FRET), which allows the detection of complexes between protein molecules that are within <10 nm from each other. Two variations of the FRET technique are presented: SE-FRET (sensitized emission) and AB-FRET (acceptor bleaching), in which the energy is transferred non-radiatively from the donor to the acceptor or emitted radiatively by the donor upon photobleaching of the acceptor. Both SE-FRET and AB-FRET approaches can be adapted easily to discover other interactions between other proteins in planta.

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

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.

Dengue Virus NS4b N-Terminus Disordered Region Interacts with NS3 Helicase C-Terminal Subdomain to Enhance Helicase Activity

Dengue virus replicates its single-stranded RNA genome in membrane-bound complexes formed on the endoplasmic reticulum, where viral non-structural proteins (NS) and RNA co-localize. The NS proteins interact with one another and with the host proteins. The interaction of the viral helicase and protease, NS3, with the RNA-dependent RNA polymerase, NS5, and NS4b proteins is critical for replication. In vitro, NS3 helicase activity is enhanced by interaction with NS4b. We characterized the interaction between NS3 and NS4b and explained a possible mechanism for helicase activity modulation by NS4b. Our bacterial two-hybrid assay results showed that the N-terminal 57 residues region of NS4b is enough to interact with NS3. The molecular docking of the predicted NS4b structure onto the NS3 structure revealed that the N-terminal disordered region of NS4b wraps around the C-terminal subdomain (CTD) of the helicase. Further, NS3 helicase activity is enhanced upon interaction with NS4b. Molecular dynamics simulations on the NS4b-docked NS3 crystal structure and intrinsic tryptophan fluorescence studies suggest that the interaction results in NS3 CTD domain motions. Based on the interpretation of our results in light of the mechanism explained for NS3 helicase, NS4b–NS3 interaction modulating CTD dynamics is a plausible explanation for the helicase activity enhancement.

The Landscape of Virus-Host Protein–Protein Interaction Databases

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

Microscale Thermophoresis as a Tool to Study Protein Interactions and Their Implication in Human Diseases

The review highlights how protein–protein interactions (PPIs) have determining roles in most life processes and how interactions between protein partners are involved in various human diseases. The study of PPIs and binding interactions as well as their understanding, quantification and pharmacological regulation are crucial for therapeutic purposes. Diverse computational and analytical methods, combined with high-throughput screening (HTS), have been extensively used to characterize multiple types of PPIs, but these procedures are generally laborious, long and expensive. Rapid, robust and efficient alternative methods are proposed, including the use of Microscale Thermophoresis (MST), which has emerged as the technology of choice in drug discovery programs in recent years. This review summarizes selected case studies pertaining to the use of MST to detect therapeutically pertinent proteins and highlights the biological importance of binding interactions, implicated in various human diseases. The benefits and limitations of MST to study PPIs and to identify regulators are discussed.

CETSA Feature Based Clustering for Protein Outlier Discovery by Protein-to-Protein Interaction Prediction

The Cellular Thermal Shift Assay (CETSA) is a biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins. This technology has revolutionized cell-based target engagement studies and has been used as guidance for drug design. Although many ap-plications of CETSA data have been explored, the correlations between CETSA data and protein-protein interactions (PPI) have barely been touched. In this study, we conduct the first exploration study applying CETSA data for PPI prediction. We use a machine learning method, Decision Tree, to predict PPI scores using proteins' CETSA features. It shows promising results that the predicted PPI scores closely match the ground-truth PPI scores. Furthermore, for a small number of protein pairs, whose PPI score predictions mismatch the ground truth, we use iterative clustering strategy to gradually reduce the number of these pairs. At the end of iterative clustering, the remaining protein pairs may have some unusual properties and are of scientific value for further biological investigation. Our study has demonstrated that PPI is a brand-new application of CETSA data. At the same time, it also manifests that CETSA data can be used as a new data source for PPI exploration study.

Targeting Protein Interaction Hotspots Using Structured and Disordered Chimeric Peptide Inhibitors

The main challenge in inhibiting protein-protein interactions (PPI) for therapeutic purposes is designing molecules that bind specifically to the interaction hotspots. Adding to the complexity, such hotspots can be within both structured and disordered interaction interfaces. To address this, we present a strategy for inhibiting the structured and disordered hotspots of interactions using chimeric peptides that contain both structured and disordered parts. The chimeric peptides we developed are comprised of a cyclic structured part and a disordered part, which target both disordered and structured hotspots. We demonstrate our approach by developing peptide inhibitors for the interactions of the antiapoptotic iASPP protein. First, we developed a structured, α-helical stapled peptide inhibitor, derived from the N-terminal domain of MDM2. The peptide bound two hotspots on iASPP at the low micromolar range and had a cytotoxic effect on A2780 cancer cells with a half-maximal inhibitory concentration (IC₅₀) value of 10 ± 1 μM. We then developed chimeric peptides comprising the structured stapled helical peptide and the disordered p53-derived LinkTer peptide that we previously showed to inhibit iASPP by targeting its disordered RT loop. The chimeric peptide targeted both structured and disordered domains in iASPP with higher affinity compared to the individual structured and disordered peptides and caused cancer cell death. Our strategy overcomes the inherent difficulty in inhibiting the interactions of proteins that possess structured and disordered regions. It does so by using chimeric peptides derived from different interaction partners that together target a much wider interface covering both the structured and disordered domains. This paves the way for developing such inhibitors for therapeutic purposes.

Activating transcription factor 3 mediates apoptotic functions through a p53-independent pathway in human papillomavirus 18 infected HeLa cells

Activating transcription factor 3 (ATF3) is the first p53 stability regulator that interferes with the ubiquitination of p53. However, the E6 oncoprotein of high-risk human papillomaviruses (HPVs) binds to and induces proteasome-dependent degradation of the host p53 protein. Herein, we investigate the effects of ATF3 overexpression on cell cycle progression and apoptosis in HPV-18-infected HeLa cells, and further examine whether ATF3 could alter the apoptosis level of HeLa cells through the inhibition of E6-mediated p53 degradation. Cytological function of HeLa cells prior and subsequent to the overexpression of ATF3 was assessed using cell cycle and annexin V/PI flow cytometry analysis. Western blotting assays revealed no significant effect of ATF3 on the levels of p53 and E6 in HeLa cells. However, annexin V staining demonstrated increases in apoptosis. ATF3 acts as a tumor suppressor factor in HPV18-related cervical cancer which mediates apoptotic functions through a p53-independent pathway.

Uncovering Novel Viral Innate Immune Evasion Strategies: What Has SARS-CoV-2 Taught Us?

The ongoing SARS-CoV-2 pandemic has tested the capabilities of public health and scientific community. Since the dawn of the twenty-first century, viruses have caused several outbreaks, with coronaviruses being responsible for 2: SARS-CoV in 2007 and MERS-CoV in 2013. As the border between wildlife and the urban population continue to shrink, it is highly likely that zoonotic viruses may emerge more frequently. Furthermore, it has been shown repeatedly that these viruses are able to efficiently evade the innate immune system through various strategies. The strong and abundant antiviral innate immunity evasion strategies shown by SARS-CoV-2 has laid out shortcomings in our approach to quickly identify and modulate these mechanisms. It is thus imperative that there be a systematic framework for the study of the immune evasion strategies of these viruses, to guide development of therapeutics and curtail transmission. In this review, we first provide a brief overview of general viral evasion strategies against the innate immune system. Then, we utilize SARS-CoV-2 as a case study to highlight the methods used to identify the mechanisms of innate immune evasion, and pinpoint the shortcomings in the current paradigm with its focus on overexpression and protein-protein interactions. Finally, we provide a recommendation for future work to unravel viral innate immune evasion strategies and suitable methods to aid in the study of virus-host interactions. The insights provided from this review may then be applied to other viruses with outbreak potential to remain ahead in the arms race against viral diseases.

Proximity Ligation Assay (PLA)

Proximity ligation assay (PLA), also referred to as Duolink^® PLA technology, permits detection of protein-protein interactions in situ (<40 nm distance) at endogenous protein levels. It exploits specific antibodies identifying (either directly or indirectly) the two proteins of interest and takes advantage of specific DNA primers covalently linked to the antibodies. A hybridization step followed by a PCR amplification with fluorescent probes then permits visualization of spots of proximity by fluorescence microscopy.

Affinity of anti-spike antibodies in SARS-CoV-2 patient plasma and its effect on COVID-19 antibody assays

Background

Measuring anti-spike protein antibodies in human plasma or serum is commonly used to determine prior exposure to SARS-CoV-2 infection and to assess the anti-viral protection capacity. According to the mass-action law, a lesser concentration of tightly binding antibody can produce the same quantity of antibody-antigen complexes as higher concentrations of lower affinity antibody. Thus, measurements of antibody levels reflect both affinity and concentration. These two fundamental parameters cannot be disentangled in clinical immunoassays, and so produce a bias which depends on the assay format.

Methods

To determine the apparent affinity of anti-spike protein antibodies, a small number of antigen-coated magnetic microparticles were imaged by fluorescence microscopy after probing antigen-antibody equilibria directly in patient plasma. Direct and indirect anti-SARS-CoV-2 immunoassays were used to measure antibody levels in the blood of infected and immunised individuals.

Findings

We observed affinity maturation of antibodies in convalescent and vaccinated individuals, showing that higher affinities are achieved much faster by vaccination. We demonstrate that direct and indirect immunoassays for measuring anti-spike protein antibodies depend differently on antibody affinity which, in turn, affects accurate interpretation of the results.

Interpretation

Direct immunoassays show substantial antibody affinity dependence. This makes them useful for identifying past SARS-CoV-2 exposure. Indirect immunoassays provide more accurate quantifications of anti-viral antibody levels.

Funding

The authors are all full-time employees of Abbott Laboratories. Abbott Laboratories provided all operating funds. No external funding sources were used in this study.

From gene to biomolecular networks: a review of evidences for understanding complex biological function in plants

Although at the infancy stage, biomolecular network biology is a comprehensive approach to understand complex biological function in plants. Recent advancements in the accumulation of multi-omics data coupled with computational approach have accelerated our current understanding of the complexities of gene function at the system level. Biomolecular networks such as protein-protein interaction, co-expression and gene regulatory networks have extensively been used to decipher the intricacies of transcriptional reprogramming of hundreds of genes and their regulatory interaction in response to various environmental perturbations mainly in the model plant Arabidopsis. This review describes recent applications of network-based approaches to understand the biological functions in plants and focuses on the challenges and opportunities to harness the full potential of the approach.

Pairwise Biological Network Alignment Based on Discrete Bat Algorithm

The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.

Microscale thermophoresis analysis of the molecular interaction between small nuclear ribonucleoprotein polypeptide G and the RING finger domain of RBBP6 towards anti-cancer drug discovery

Regulatory core-splicing proteins are now becoming highly promising therapeutic targets for the development of anti-cancer drugs. SNRPG and RBBP6 are two good examples of regulatory core-splicing proteins involved in tumorigenesis and tumor development whose multi-functional role is primarily mediated by protein-protein interactions. Over the years, skepticism abutting from the two onco-proteins has been mounting. Suggestive evidence using yeast 2-hybrid technique observed possible involvement between SNRPG and the RING finger domain of RBBP6. However, the putative interaction remains elusive and yet to be characterized. In this study, we developed the first MST-based assay to confirm the interaction between SNRPG and the RING finger domain of RBBP6. The results demonstrated a strong binding affinity between SNRPG and the RING finger domain of RBBP6 with a K_D in the low nanomolar concentration range of 3.1596 nM. The results are congruent with previous findings suggesting possible involvement between the two proteins in cancer-cell networks, thereby providing a new mechanistic insight into the interaction between SNRPG and the RING finger domain of RBBP6. The interaction is therapeutically relevant and represents a great milestone in the anti-cancer drug discovery space. Identification of small molecule inhibitors to modulate the binding affinity between the two proteins would therefore be a major breakthrough in the development of new PPI-focused anti-cancer drugs.

Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)—AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (−31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (−60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.

An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens

Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.

Label-free methods for optical in vitro characterization of protein-protein interactions

Protein-protein interactions are involved in the regulation and function of the majority of cellular processes. As a result, much effort has been aimed at the development of methodologies capable of quantifying protein-protein interactions, with label-free methods being of particular interest due to the associated simplified workflows and minimisation of label-induced perturbations. Here, we review recent advances in optical technologies providing label-free in vitro measurements of affinities and kinetics. We provide an overview and comparison of existing techniques and their principles, discussing advantages, limitations, and recent applications.

Influence of Purification Process on the Function of Synthetic Polymer Nanoparticles

Multifunctional synthetic polymers can bind to target molecules and are therefore widely investigated in diagnostics, drug delivery carriers, and separation carriers. Because these polymers are synthesized from nonbiological components, purification processes (e.g., chromatography, dialysis, extraction, and centrifugation) must be conducted after the synthesis. Although several purification methods are used for polymer purification, few reports have revealed the influence of purification process on the functions of polymer. In this study, we demonstrated that the characteristics, function, and stability of synthetic polymer depend on the purification process. N-Isopropylacrylamide-based polymer nanoparticles (NPs) and melittin (i.e., honey bee venom) were used as a model of synthetic polymer and target toxic peptide, respectively. Synthesized NPs were purified by dialysis in methanol, acetone precipitation, or centrifugation. NPs purified by dialysis in ultrapure water were used as control NPs. Then, NP size, surface charge, toxin neutralization effect, and stability were determined. NP size did not considerably change by purification with centrifugation; however, it decreased by purification using dialysis in methanol and acetone precipitation compared with that of control NPs. The ζ-potential of NPs changed after each purification process compared with that of control NPs. The melittin neutralization efficiency of NPs depended on the purification process; i.e., it decreased by acetone precipitation and increased by dialysis in methanol and centrifugation compared with that of control NPs. Of note, the addition of methanol and acetone decreased NP stability. These studies implied the importance of considering the effect of the purification method on synthetic polymer function.

Compartmentalization-aided interaction screening reveals extensive high-order complexes within the SARS-CoV-2 proteome

Bearing a relatively large single-stranded RNA genome in nature, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes sophisticated replication/transcription complexes (RTCs), mainly composed of a network of nonstructural proteins and nucleocapsid protein, to establish efficient infection. In this study, we develop an innovative interaction screening strategy based on phase separation in cellulo, namely compartmentalization of protein-protein interactions in cells (CoPIC). Utilizing CoPIC screening, we map the interaction network among RTC-related viral proteins. We identify a total of 47 binary interactions among 14 proteins governing replication, discontinuous transcription, and translation of coronaviruses. Further exploration via CoPIC leads to the discovery of extensive ternary complexes composed of these components, which infer potential higher-order complexes. Taken together, our results present an efficient and robust interaction screening strategy, and they indicate the existence of a complex interaction network among RTC-related factors, thus opening up opportunities to understand SARS-CoV-2 biology and develop therapeutic interventions for COVID-19.

Screening and characterization of hypothetical proteins of Plasmodium falciparum as novel vaccine candidates in the fight against malaria using reverse vaccinology

BACKGROUND

Plasmodium falciparum is the most deadly and leading cause of morbidity and mortality in Africa. About 90% of all malaria deaths in the world today occur in Sub-Saharan Africa especially in children aged < 5 years. In 2018, it was reported that there were 228 million malaria cases that resulted in 405,000 deaths from 91 countries. Currently, a fully effective and long-lasting preventive malaria vaccine is still elusive therefore more effort is needed to identify better effective vaccine candidates. The aim of this study was to identify and characterize hypothetical proteins as vaccine candidates derived from Plasmodium falciparum 3D7 genome by reverse vaccinology.

RESULTS

Of the 23 selected hypothetical proteins, 5 were predicted on the extracellular localization by WoLFPSORTv.2.0 program and all the 5 had less than 2 transmembrane regions that were predicted by TMHMMv2.0 and HMMTOP programs at default settings. Two out of the five proteins lacked secretory signal peptides as predicted by SignalP program. Among the 5 extracellular proteins, 3 were predicted to be antigenic by VaxiJen (score ≥ 0.5) and had negative GRAVY values ranging from - 1.156 to - 0.440. B cell epitope prediction by ABCpred and BCpred programs revealed a total of 15 antigenic epitopes. A total of 13 cytotoxic T cells were predicted from the 3 proteins using CTLPred online server. Only 2 out of the 13 CTL were antigenic, immunogenic, non-allergenic, and non-toxic using VaxiJen, IEDB, AllergenFp, and Toxinpred servers respectively in that order. Five HTL peptides from XP_001351030.1 protein are predicted inducers of all the three cytokines. STRING protein-protein network analysis of HPs revealed XP_001350955.1 closely interacts with nucleoside diphosphate kinase (PF13-0349) at 0.704, XP_001351030.1 interacts with male development protein1 (Mdv-1) at 0.645, and XP_001351047.1 with an uncharacterized protein (MAL8P1.53) at 0.400.

CONCLUSION

Reverse vaccinology is a promising strategy for the screening and identification of antigenic antigens with potential capacity to elicit cellular and humoral immune responses against P. falciparum infection. In this study, potential vaccine candidates of Plasmodium falciparum were identified and screened using standard bioinformatics tools. The vaccine candidates contained antigenic and immunogenic epitopes which could be considered for novel and effective vaccine targets. However, we strongly recommend in vivo and in vitro experiments to validate their immunogenicity and protective efficacy to completely decipher the vaccine targets against malaria.

A semi-quantitative pull-down assay to study tRNA substrate specificity of modification enzymes

A tRNA interacts with numerous proteins throughout its biogenesis and during translation, and a significant portion of these interacting proteins are involved in post-transcriptional modifications. While some of the modifying enzymes use relatively simple recognition elements for substrate recognition, many enzymes selectively modify a specific subset of tRNA species without obvious recognition rules. In this chapter we describe a semi-quantitative pull-down assay to study tRNA substrate specificity of modification enzymes, by using the yeast Saccharomyces cerevisiae m³C₃₂ methyltransferase Trm140 as an example. We also discuss some overall considerations for a successful pull-down experiment, with a focus on practical applications of the dissociation constant K_D between the protein and the tRNA and the off-rate.

Reconstruction of the Fas-Based Death-Inducing Signaling Complex (DISC) Using a Protein-Protein Docking Meta-Approach

The death-inducing signaling complex (DISC) is a fundamental multiprotein complex, which triggers the extrinsic apoptosis pathway through stimulation by death ligands. DISC consists of different death domain (DD) and death effector domain (DED) containing proteins such as the death receptor Fas (CD95) in complex with FADD, procaspase-8, and cFLIP. Despite many experimental and theoretical studies in this area, there is no global agreement neither on the DISC architecture nor on the mechanism of action of the involved species. In the current work, we have tried to reconstruct the DISC structure by identifying key protein interactions using a new protein-protein docking meta-approach. We combined the benefits of five of the most employed protein-protein docking engines, HADDOCK, ClusPro, HDOCK, GRAMM-X, and ZDOCK, in order to improve the accuracy of the predicted docking complexes. Free energy of binding and hot spot interacting residues were calculated and determined for each protein-protein interaction using molecular mechanics generalized Born surface area and alanine scanning techniques, respectively. In addition, a series of in-cellulo protein-fragment complementation assays were conducted to validate the protein-protein docking procedure. The results show that the DISC formation initiates by dimerization of adjacent Fas_DD trimers followed by recruitment of FADD through homotypic DD interactions with the oligomerized death receptor. Furthermore, the in-silico outcomes indicate that cFLIP cannot bind directly to FADD; instead, cFLIP recruitment to the DISC is a hierarchical and cooperative process where FADD initially recruits procaspase-8, which in turn recruits and heterodimerizes with cFLIP. Finally, a possible structure of the entire DISC is proposed based on the docking results.

Mutational Analysis of the GXXXG/A Motifs in the Human Na+/Taurocholate Co-Transporting Polypeptide NTCP on Its Bile Acid Transport Function and Hepatitis B/D Virus Receptor Function

Homodimerization is essential for plasma membrane sorting of the liver bile acid transporter NTCP and its function as Hepatitis B/D Virus (HBV/HDV) receptor. However, the protein domains involved in NTCP dimerization are unknown. NTCP bears two potential GXXXG/A dimerization motifs in its transmembrane domains (TMDs) 2 and 7. The present study aimed to analyze the role of these GXXXG/A motifs for the sorting, function, and dimerization of NTCP. The NTCP mutants G₆₀LXXXA₆₄L (TMD2), G₂₃₃LXXXG₂₃₇L (TMD7) and a double mutant were generated and analyzed for their interaction with wild-type NTCP using a membrane-based yeast-two hybrid system (MYTH) and co-immunoprecipitation (co-IP). In the MYTH system, the TMD2 and TMD7 mutants showed significantly lower interaction with the wild-type NTCP. In transfected HEK293 cells, membrane expression and bile acid transport activity were slightly reduced for the TMD2 mutant but were completely abolished for the TMD7 and the TMD2/7 mutants, while co-IP experiments still showed intact protein-protein interactions. Susceptibility for in vitro HBV infection in transfected HepG2 cells was reduced to 50% for the TMD2 mutant, while the TMD7 mutant was not susceptible for HBV infection at all. We conclude that the GXXXG/A motifs in TMD2 and even more pronounced in TMD7 are important for proper folding and sorting of NTCP, and so indirectly affect glycosylation, homodimerization, and bile acid transport of NTCP, as well as its HBV/HDV receptor function.