Protein-protein Interaction Databases Research Articles

Proteome-wide assessment of human interactome as a source of capturing domain-motif and domain-domain interactions.

Protein-protein interactions (PPIs) play a crucial role in various biological processes by establishing domain-motif (DMI) and domain-domain interactions (DDIs). While the existence of real DMIs/DDIs is generally assumed, it is rarely tested; therefore, this study extensively compared high-throughput methods and public PPI repositories as sources for DMI and DDI prediction based on the assumption that the human interactome provides sufficient data for the reliable identification of DMIs and DDIs. Different datasets from leading high-throughput methods (Yeast two-hybrid [Y2H], Affinity Purification coupled Mass Spectrometry [AP-MS], and Co-fractionation-coupled Mass Spectrometry) were assessed for their ability to capture DMIs and DDIs using known DMI/DDI information. High-throughput methods were not notably worse than PPI databases and, in some cases, appeared better. In conclusion, all PPI datasets demonstrated significant enrichment in DMIs and DDIs (p-value <0.001), establishing Y2H and AP-MS as reliable methods for predicting these interactions. This study provides valuable insights for biologists in selecting appropriate methods for predicting DMIs, ultimately aiding in SLiM discovery.

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

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

The Chaperone Protein Cct5 is Essential for Hematopoietic Stem Cell Maintenance.

Proper proteostasis is indispensable for the long-term maintenance of hematopoietic stem and progenitor cells (HSPCs). The TRiC/CCT (chaperonin-containing TCP-1) complex, a key constituent of cellular machinery facilitating accurate protein folding, has remained enigmatic in its specific function within HSPCs. Here we show that conditional knockout (KO) of Cct5 significantly impairs the maintenance of murine HSPCs. Primary and secondary transplantation experiments unequivocally demonstrate the incapacity of Cct5 KO HSPCs to reconstitute both myeloid and lymphoid lineage cells in recipient mice, highlighting the pivotal role of the TRiC/CCT complex in governing these cellular lineages. Furthermore, leveraging an integrated approach that merges a Protein-Protein Interaction (PPI) database with RNA sequencing (RNA-seq) data of HSPCs, our analysis reveals intricate interactions between Cct5 and vital transcription factors crucial for HSC homeostasis. Notably, Cct5 engages with MYC, PIAS1, TP53, ESR1, HOXA1, and JUN, intricately regulating the transcriptional landscape governing autophagy, myeloid differentiation, and cytoskeleton organization within HSPCs. Our study unveils the profound significance of TRiC/CCT complex-mediated proteostasis in orchestrating the maintenance and functionality of HSPCs.

Interactome‐guided machine learning modeling reveals information embedded in blood plasma protein‐protein interactions in Alzheimer’s Disease patients treated by the complex plasma fraction GRF6019

AbstractBackgroundThe relevance of plasma fraction treatment to reverse age‐related conditions was underlined by our previous pre‐clinical results. In two Phase 2 clinical trials (GRF6019‐201 and GRF6019‐202), patients with Alzheimer’s Disease (AD) were treated with the complex plasma fraction GRF6019, which was determined to be safe and well‐tolerated. Interestingly, no cognitive decline and minimal functional decline were observed. While GRF6019 treatment was associated with a quick and widespread proteomics response, lasting molecular changes were more subtle. Here, we developed new machine learning models guided by protein‐protein interaction (PPI) information to deeper understand the molecular effects of GRF6019 plasma fraction treatment.MethodMore than 7k proteins were measured in 120 plasma samples from treated subjects enrolled in the GRF6019‐201 and GRF6019‐202 trials (n = 21 and 18, respectively) using the Somascan assay. Interacting protein pairs were curated from STRING and OmniPath PPI databases. For each protein‐protein interaction, linear Support Vector Machine (SVM) models were trained to separate before and after treatment samples based on measured protein levels and compared to single‐protein models. Proteins were ranked based on their individual performance and their gain of performance when combined with interaction partnersResultProteasome‐associated elements were overrepresented among proteins with top individual performance. Interaction models provided more details about related processes involving ubiquitination. Furthermore, various other immune, hormonal, and neuronal terms found to be enriched among the top interactors.ConclusionComplex biological functions can be mapped into pathways which are composed of and regulated by PPIs. Biological functionality can be encoded in concentrations of individual proteins, also in relative concentrations of interacting proteins. Our results indicate that the latter aspect may contain a significant amount of complementary information, not accessible with traditional approaches analyzing proteins as independent entities. Consequently, the development and the application of novel system biology tools on blood plasma proteomics will help to better understand patients’ responses to treatment in clinical trials.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Exploring Asthma Mechanism of Belamcanda Chinensis by "Dose-effect Weighted Coefficient" Network Pharmacology and Experiment Validation.

Asthma is a chronic inflammatory disease of the airways that seriously endangers human health. Belamcanda chinensis (BC), a traditional Chinese medicine, has been used to counteract asthma as it has been shown to possess anti-inflammatory and regulatory immunity properties. The study aimed to investigate the mechanisms of action of BC in the treatment of asthma; a "dose-effect weighted coefficient" network pharmacology method was established to predict potential active compounds. Information on the components and content of BC was obtained by UPLC-QEOrbitrap- MS spectrometry. Based on BC content, oral bioavailability, and molecular docking binding energy, dose-effect weighting coefficients were constructed. With the degree greater than average as the index, a protein-protein interaction (PPI) database was used to obtain the core key targets for asthma under dose-effect weighting. GO function and KEGG pathway analyses of the core targets were performed using DAVID software. Finally, MTT and ELISA assays were used to assess the effects of active components on 16HBE cell proliferation. The experimental results using the 16HBE model demonstrated BC to have a potential protective effect on asthma. Network pharmacology showed SYK, AKT1, and ALOX5 to be the main key targets, and Fc epsilon RI as the promising signaling pathway. Eight components, such as tectoridin, mangiferin, luteolin, and isovitexin were the main active compounds, Finally, we analyzed the LPS-induced 16HBE proliferation of each active ingredient. Based on the activity verification study, all five predicted components promoted the proliferation of 16HBE cells. These five compounds can be used as potential quality markers for asthma. This study provides a virtual and practical method for the simple and rapid screening of active ingredients in natural products.

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome

Background22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood.ResultsUsing previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different.ConclusionsUsing the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders

Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding “guide” peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous β-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.

Mapping the protein–protein interactome in the tumor immune microenvironment

Abstract The cell-to-cell communication primarily occurs through cell-surface and secreted proteins, which form a sophisticated network that coordinates systemic immune function. Uncovering these protein–protein interactions (PPIs) is indispensable for understanding the molecular mechanism and elucidating immune system aberrances under diseases. Traditional biological studies typically focus on a limited number of PPI pairs due to the relative low throughput of commonly used techniques. Encouragingly, classical methods have advanced, and many new systems tailored for large-scale protein–protein screening have been developed and successfully utilized. These high-throughput PPI investigation techniques have already made considerable achievements in mapping the immune cell interactome, enriching PPI databases and analysis tools, and discovering therapeutic targets for cancer and other diseases, which will definitely bring unprecedented insight into this field.

Genome-Wide Association Study on the Content of Nucleotide-Related Compounds in Korean Native Chicken Breast Meat.

Meat flavor is an important factor that influences the palatability of chicken meat. Inosine 5'-monophosphate (IMP), inosine, and hypoxanthine are nucleic acids that serve as taste-active compounds, mainly enhancing flavor in muscle tissue. For this study, we performed a genome-wide association study (GWAS) using a mixed linear model to identify single-nucleotide polymorphisms (SNPs) that are significantly associated with changes in the contents of the nucleotide-related compounds of breast meat in the Korean native chicken (KNC) population. The genomic region on chicken chromosome 5 containing an SNP (rs316338889) was significantly (p < 0.05) associated with all three traits. The trait-related candidate genes located in this significant genomic region were investigated through performing a functional enrichment analysis and protein-protein interaction (PPI) database search. We found six candidate genes related to the function that possibly affected the content of nucleotide-related compounds in the muscle, namely, the TNNT3 and TNNT2 genes that regulate muscle contractions; the INS, IGF2, and DUSP8 genes associated with insulin sensitivity; and the C5NT1AL gene that is presumably related to the nucleotide metabolism process. This study is the first of its kind to find candidate genes associated with the content of all three types of nucleotide-related compounds in chicken meat using GWAS. The candidate genes identified in this study can be used for genomic selection to breed better-quality chickens in the future.

Prognostic significance and immune cell infiltration analysis of differentially expressed genes in malignant pleural mesothelioma

Objective: To explore and analyze differential expressed genes in malignant pleural mesothelioma (MPM) by bioinformatics method, and to study their prognostic value in MPM and their potential role in immunotherapy. Methods: In January 2022, the dataset GSE51024 was downloaded from the GEO database, and MPM (55 cases) and normal tissue (41 cases) samples were obtained. Using R software and HMDD and miRNet database, MPM-related differential genes were screened and co-expressed genes were identified. Co-expressed genes were enriched and functionally annotated, and protein-protein interaction (PPI) networks were constructed and key genes were identified using the STRING database and Cytoscape software. TRRUST and GEPIA databases were used to predict transcription factors of key genes and to analyze prognosis and survival. The correlation between key genes and the degree of infiltration of immune cells was analyzed using TIMER. Results: A total of 435 co-expressed genes were obtained, which were mainly concentrated in the extracellular matrix tissue and the signaling pathways of cell adhesion molecules. Combined with PPI and TRRUST database, seven key MPM prognostic genes were identified. Among them, cyclin 20 (CDC20) , cell cycle checkpoint kinase 1 (CHEK1) , enhancer of Zeste homolog 2 (EZH2) , ribonucleotide reductase subunit M2 (RRM2) , topoisomerase 2A (TOP2A) , ubiquitin like plant homeodomain and ring finger domain 1 (UHRF1) were significantly up-regulated in MPM, while cyclin A1 (CCNA1) was significantly down-regulated. The expressions of CCNA1, CDC20, CHEK1, EZH2, RRM2, TOP2A and UHRF1 genes were significantly associated with MPM overall survival (P<0.05) . The expressions of CDC20, CHEK1, EZH2, RRM2 and TOP2A genes were positively correlated with B cells and dendritic cells (P<0.05) , and negatively correlated with neutrophils (P<0.05) . Conclusion: CCNA1, CDC20, CHEK1, EZH2, RRM2, TOP2A and UHRF1 may be potential prognostic markers in MPM patients, and their expressions may be related to MPM tumor immunity.

Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer's Patients from Controls.

The recent advances in creating pluripotent stem cells from somatic cells and differentiating them into a variety of cell types is allowing us to study them without the caveats associated with disease-related changes. We generated induced Pluripotent Stem Cells (iPSCs) from eight Alzheimer's disease (AD) patients and six controls and used lentiviral delivery to differentiate them into excitatory glutamatergic neurons. We then performed RNA sequencing on these neurons and compared the Alzheimer's and control transcriptomes. We found that 621 genes show differences in expression levels at adjusted p < 0.05 between the case and control derived neurons. These genes show significant overlap and directional concordance with genes reported from a single-cell transcriptome study of AD patients; they include five genes implicated in AD from genome-wide association studies and they appear to be part of a larger functional network as indicated by an excess of interactions between them observed in the protein-protein interaction database STRING. Exploratory analysis with Uniform Manifold Approximation and Projection (UMAP) suggests distinct clusters of patients, based on gene expression, who may be clinically different. Our research outcomes will enable the precise identification of distinct biological subtypes among individuals with Alzheimer's disease, facilitating the implementation of tailored precision medicine strategies.

Protein Interaction Network Analysis of β-catenin to Map its Crucial Interacting Genes using Systems Biology Approach

β-catenin (CTNNB1), a scaffold protein, plays a vital role in embryonic development, adult tissue homeostasis, neurodegenerative diseases, bone diseases, chronic obstructive pulmonary diseases, wound healing, and pigment disorders. β-catenin is the crucial component in the Wnt-signaling pathway. β-catenin levels in the cytosol balance the Wnt-signaling pathway. The activated Wnt-signaling pathway allows the accumulation of β-catenin in the nucleus and further act as a promoter to initiate transcription of several oncogenes responsible for carcinogenesis. In the present study, we have used a systems biology approach to map β-catenin interactors. Primary protein-protein interaction databases such as PubChem, CTD, and STITCH databases were used to collect all the experimentally valid β-catenin interactors and their subcellular location was traced using UniProtKB database. The interactome was constructed and analyzed by utilizing STRING and Cytoscape tools. MCODE, Cytoscape tool, was utilized to construct and analyze the sub-networks. Correlation between modular seed proteins and β-catenin was studied using UALCAN database. Functional enrichment analysis was done using DAVID database. Cytohubba, a Cytoscape tool was utilized to identify the top gene interactors of β-catenin. Further, the expression and gene ontology of each β-catenin gene interactor were analyzed using UALCAN and CleuGO tools. The analysis reveals β-catenin interactors, TP53, EP300, RPS27A, UBC, HDAC1, SRC, AKT1, EGFR, HSP90AA1, and CREBBP as the first top gene interactors. This study uses a systems biology-driven approach and successfully identifies and understands the biological roles of the top β-catenin interactors.

Automated assembly of molecular mechanisms at scale from text mining and curated databases.

The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.

On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3.

EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and expAtaxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.

The Comprehensive Role of High Mobility Group Box 1 (HMGB1) Protein in Different Tumors: A Pan-Cancer Analysis.

HMGB1 is a highly conserved nuclear protein widely expressed in mammalian cells. This study aimed to comprehensively investigate the roles and mechanisms of HMGB1 in different tumors. Original data on HMGB1 expression, localization, potential interacting proteins, genetics were obtained from The Cancer Genome Atlas, Genotype-Tissue Expression, Cancer Cell Line Encyclopedia, Human Protein Atlas, Compartmentalized Protein-Protein Interaction and cBioPortal databases. Then, correlation between HMGB1 expression levels and tumor stage, prognosis, potential pathways, tumor microenvironment, ESTIMATE score, immune-related genes, immune cell infiltration, microsatellite instability, tumor mutation burden, or anti-tumor drug resistance was investigated. The above results consistently indicated that high expression of HMGB1 protein may be related to clinical prognosis of HCC patients. Therefore, clinical tissues of HCC patients were selected to verify the differential expression of HMGB1 protein in HCC. The sensitivity of HMGB1-siRNA transfected HepG2 cells to sorafenib was assessed. HMGB1 was found to be differentially expressed in many tumors and normal tissues. HMGB1 was mainly located in the nucleus and might interact with proteins such as TLR2 and TLR4. Furthermore, HMGB1 expression was closely related to tumor stage, prognosis, tumor microenvironment, immune-related genes, immune cell infiltration, microsatellite instability, tumor mutation burden, and anti-tumor drug resistance and might be involved in different pathways of various tumors. Immunohistochemistry results further verified the differential expression of HMGB1 in HCC and paracancerous tissues. HMGB1-siRNA transfected HepG2 cells had a tendency to be more insensitive to sorafenib treatment compared to the control group. HMGB1 was differentially expressed in most tumors and normal tissues, and was closely related to the clinical stage, prognosis, immune infiltration, tumor microenvironment, and drug resistance of tumors. Therefore, HMGB1 may serve as a novel biomarker for predicting tumor prognosis, efficacy of immune checkpoint inhibitors, and a potential target for anti-tumor therapy.

PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.

Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein-protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host-pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host-pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system. https://github.com/YasCoMa/ppintegrator, https://github.com/YasCoMa/ppi_validation_process and https://github.com/YasCoMa/predprin.

The HBV web: An insight into molecular interactomes between the hepatitis B virus and its host en route to hepatocellular carcinoma.

Hepatitis B virus (HBV) is a major aetiology associated with the development and progression of hepatocellular carcinoma (HCC), the most common primary liver malignancy. Over the past few decades, direct and indirect mechanisms have been identified in the pathogenesis of HBV-associated HCC which include altered signaling pathways, genome integration, mutation-induced genomic instability, chromosomal deletions and rearrangements. Intertwining of the HBV counterparts with the host cellular factors, though well established, needs to be systemized to understand the dynamics of host-HBV crosstalk and its consequences on HCC progression. Existence of a vast array of protein-protein and protein-nucleic acid interaction databases has led to the uncoiling of the compendia of genes/gene products associated with these interactions. This review covers the existing knowledge about the HBV-host interplay and brings it down under one canopy emphasizing on the HBV-host interactomics; and thereby highlights new strategies for therapeutic advancements against HBV-induced HCC.

Ligand-Based Drug Design of Novel Antimicrobials against Staphylococcus aureus by Targeting Bacterial Transcription.

Staphylococcus aureus is a common human commensal pathogen that causes a wide range of infectious diseases. Due to the generation of antimicrobial resistance, the pathogen becomes resistant to more and more antibiotics, resulting in methicillin-resistant S. aureus (MRSA) and even multidrug-resistant S. aureus (MDRSA), namely 'superbugs'. This situation highlights the urgent need for novel antimicrobials. Bacterial transcription, which is responsible for bacterial RNA synthesis, is a valid but underutilized target for developing antimicrobials. Previously, we reported a novel class of antimicrobials, coined nusbiarylins, that inhibited bacterial transcription by interrupting the protein-protein interaction (PPI) between two transcription factors NusB and NusE. In this work, we developed a ligand-based workflow based on the chemical structures of nusbiarylins and their activity against S. aureus. The ligand-based models-including the pharmacophore model, 3D QSAR, AutoQSAR, and ADME/T calculation-were integrated and used in the following virtual screening of the ChemDiv PPI database. As a result, four compounds, including J098-0498, 1067-0401, M013-0558, and F186-026, were identified as potential antimicrobials against S. aureus, with predicted pMIC values ranging from 3.8 to 4.2. The docking study showed that these molecules bound to NusB tightly with the binding free energy ranging from -58 to -66 kcal/mol.

Recent developments of sequence-based prediction of protein-protein interactions.

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.