Protein-protein interactions essentials: key concepts to building and analyzing interactome networks

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Testing biological network motif significance with exponential random graph models

Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00434-y.

A rapid, efficient, and low-cost BiFC protocol and its application in studying in vivo interaction of seed-specific transcription factors, RISBZ and RPBF

Proteins regulate cellular and biological processes in all living organisms. More than 80% of the proteins interact with one another to perform their respective functions; therefore, studying the protein-protein-interaction has gained attention in functional characterization studies. Bimolecular fluorescence complement (BiFC) assay is widely adopted to determine the physical interaction of two proteins in vivo. Here, we developed a simple, yet effective BiFC assay for protein-protein-interaction using transient Agrobacterium-mediated-transformation of onion epidermal cells by taking case study of Rice-P-box-Binding-Factor (RPBF) and rice-seed-specific-bZIP (RISBZ) in vivo interaction. Our result revealed that both the proteins, i.e., RISBZ and RPBF, interacted in the nucleus and cytosol. These two transcription factors are known for their coordinate/synergistic regulation of seed-protein content via concurrent binding to the promoter region of the seed storage protein (SSP) encoding genes. We further validated our results with BiFC assay in Nicotiana by agroinfiltration method, which exhibited similar results as Agrobacterium-mediated-transformation of onion epidermal cells. We also examined the subcellular localization of RISBZ and RPBF to assess the efficacy of the protocol. The subcellular localization and BiFC assay presented here is quite easy-to-follow, reliable, and reproducible, which can be completed within 2-3 days without using costly instruments and technologies that demand a high skill set.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases

PDZ (postsynaptic density (PSD95), discs large (Dlg), and zonula occludens (ZO-1)-dependent interactions are widely distributed within different cell types and regulate a variety of cellular processes. To date, some of these interactions have been identified as targets of small molecules or peptides, mainly related to central nervous system disorders and cancer. Recently, the knowledge of PDZ proteins and their interactions has been extended to various cell types of the immune system, suggesting that their targeting by viral pathogens may constitute an immune evasion mechanism that favors viral replication and dissemination. Thus, the pharmacological modulation of these interactions, either with small molecules or peptides, could help in the control of some immune-related diseases. Deeper structural and functional knowledge of this kind of protein–protein interactions, especially in immune cells, will uncover novel pharmacological targets for a diversity of clinical conditions.

Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations

Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES in almost all situations. Further analyses show that the machine-learned SES is incredibly stable in terms of rotational variation of tested molecules. Our timing analysis shows that the machine-learned SES is roughly 2.5 times as efficient as the classical SES routine implemented in Amber/PBSA on a tested central processing unit (CPU) platform. We expect further performance gain on massively parallel platforms such as graphics processing units (GPUs) given the ease in converting the machine-learned SES to a parallel procedure. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with a 1% deviation on average. Given its level set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms.

Towards optimizing peptide-based inhibitors of protein-protein interactions: predictive saturation variation scanning (PreSaVS)

A simple-to-implement and experimentally validated computational workflow for sequence modification of peptide inhibitors of protein–protein interactions (PPIs) is described.

An experimentally validated approach for in silico modification of peptide based protein–protein interaction inhibitors is described.

Epidermal galactose spurs chytrid virulence and predicts amphibian colonization

The chytrid fungal pathogens Batrachochytrium dendrobatidis and Batrachochytrium salamandrivorans cause the skin disease chytridiomycosis in amphibians, which is driving a substantial proportion of an entire vertebrate class to extinction. Mitigation of its impact is largely unsuccessful and requires a thorough understanding of the mechanisms underpinning the disease ecology. By identifying skin factors that mediate key events during the early interaction with B. salamandrivorans zoospores, we discovered a marker for host colonization. Amphibian skin associated beta-galactose mediated fungal chemotaxis and adhesion to the skin and initiated a virulent fungal response. Fungal colonization correlated with the skin glycosylation pattern, with cutaneous galactose content effectively predicting variation in host susceptibility to fungal colonization between amphibian species. Ontogenetic galactose patterns correlated with low level and asymptomatic infections in salamander larvae that were carried over through metamorphosis, resulting in juvenile mortality. Pronounced variation of galactose content within some, but not all species, may promote the selection for more colonization resistant host lineages, opening new avenues for disease mitigation.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Measuring expression heterogeneity of single-cell cytoskeletal protein complexes

Multimeric cytoskeletal protein complexes orchestrate normal cellular function. However, protein-complex distributions in stressed, heterogeneous cell populations remain unknown. Cell staining and proximity-based methods have limited selectivity and/or sensitivity for endogenous multimeric protein-complex quantification from single cells. We introduce micro-arrayed, differential detergent fractionation to simultaneously detect protein complexes in hundreds of individual cells. Fractionation occurs by 60 s size-exclusion electrophoresis with protein complex-stabilizing buffer that minimizes depolymerization. Proteins are measured with a ~5-hour immunoassay. Co-detection of cytoskeletal protein complexes in U2OS cells treated with filamentous actin (F-actin) destabilizing Latrunculin A detects a unique subpopulation (~2%) exhibiting downregulated F-actin, but upregulated microtubules. Thus, some cells may upregulate other cytoskeletal complexes to counteract the stress of Latrunculin A treatment. We also sought to understand the effect of non-chemical stress on cellular heterogeneity of F-actin. We find heat shock may dysregulate filamentous and globular actin correlation. In this work, our assay overcomes selectivity limitations to biochemically quantify single-cell protein complexes perturbed with diverse stimuli.

Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query

Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Identification of Potential Kinase Inhibitors within the PI3K/AKT Pathway of Leishmania Species

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases’ participation in protein–protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti-Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania, as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

Methods for binding analysis of small GTP-binding proteins with their effectors

Proteins often do not function as a single biomolecular entity; instead, they frequently interact with other proteins and biomolecules forming complexes. There is increasing evidence depicting the essentiality of protein-protein interactions (PPIs) governing a wide array of cellular processes. Thus, it is crucial to understand PPIs. Commonly used approaches like genetic (e.g., Yeast Two-Hybrid, Y2H), optical (e.g., Surface Plasmon Resonance, SPR; Fluorescence Resonance Energy Transfer, FRET), and biochemical have rendered ease in developing interactive protein maps as freely available information in protein databases on the web. The underlying basis of traditional protein interaction analysis is the core of biochemical methodologies providing direct evidence of interactions. Co-Immunoprecipitation (Co-IP) is a powerful biochemical technique that facilitates identifying novel interacting partners of a protein of interest in vivo, allowing specific capture of their complexes on an immunoglobulin. Here, using Arf-like (Arl) GTPase-8b (Arl8b) and Pleckstrin Homology Domain-Containing Family M Member 1 (PLEKHM1) as an example of small GTPase-effector pair, we provide a detailed protocol for performing Y2H and Co-IP assays to confirm the interaction between a small GTPase and its effector protein.

Proteomics of Multiple Sclerosis: Inherent Issues in Defining the Pathoetiology and Identifying (Early) Biomarkers

Multiple Sclerosis (MS) is a demyelinating disease of the human central nervous system having an unconfirmed pathoetiology. Although animal models are used to mimic the pathology and clinical symptoms, no single model successfully replicates the full complexity of MS from its initial clinical identification through disease progression. Most importantly, a lack of preclinical biomarkers is hampering the earliest possible diagnosis and treatment. Notably, the development of rationally targeted therapeutics enabling pre-emptive treatment to halt the disease is also delayed without such biomarkers. Using literature mining and bioinformatic analyses, this review assessed the available proteomic studies of MS patients and animal models to discern (1) whether the models effectively mimic MS; and (2) whether reasonable biomarker candidates have been identified. The implication and necessity of assessing proteoforms and the critical importance of this to identifying rational biomarkers are discussed. Moreover, the challenges of using different proteomic analytical approaches and biological samples are also addressed.

A two-way rectification method for identifying differentially expressed genes by maximizing the co-function relationship

BACKGROUND

The identification of differentially expressed genes (DEGs) is an important task in many biological studies. The currently widely used methods often calculate a score for each gene by estimating the significance level in terms of the differential expression. However, biological experiments often have only three duplications, plus plenty of noises contain in gene expression datasets, which brings a great challenge to statistical analysis methods. Moreover, the abundance of gene expression levels are not evenly distributed. Thus, those low expressed genes are more easily to be detected by fold-change based methods, which may results in high false positives among the DEG list. Since phenotypical changes result from DEGs should be strongly related to several distinct cellular functions, a more robust method should be designed to increase the true positive rate of the functional related DEGs.

RESULTS

In this study, we propose a two-way rectification method for identifying DEGs by maximizing the co-function relationships between genes and their enriched cellular pathways. An iteration strategy is employed to sequentially narrow down the group of identified DEGs and their associated biological functions. Functional analyses reveal that the identified DEGs are well organized in the form of functional modules, and the enriched pathways are very significant with lower p-value and larger gene count.

CONCLUSIONS

An integrative rectification method was proposed to identify key DEGs and their related functions simultaneously. The experimental validations demonstrate that the method has high interpretability and feasibility. It performs very well in terms of the identification of remarkable functional related genes.

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

NetSets.js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams

MOTIVATION

Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks.

RESULTS

We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks).

AVAILABILITYAND IMPLEMENTATION

http://web.rniapps.net/netsets (freely available for academic use).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene Expression Profiles Associated with Radio-Responsiveness in Locally Advanced Rectal Cancer

Simple Summary

Standard treatment of locally advanced rectal cancer (LARC) consists of chemotherapy, radiotherapy, and surgery. Identification of radio-resistant (RR) and radio-sensitive (RS) LARC has been a major hurdle for patient-specific treatment. The development of biomarkers that can discriminate radio-responsiveness before surgery could improve standard treatment and minimize unwanted side effects.

Abstract

LARC patients were sorted according to their radio-responsiveness and patient-derived organoids were established from the respective cancer tissues. Expression profiles for each group were obtained using RNA-seq. Biological and bioinformatic analysis approaches were used in deciphering genes and pathways that participate in the radio-resistance of LARC. Thirty candidate genes encoding proteins involved in radio-responsiveness–related pathways, including the immune system, DNA repair and cell-cycle control, were identified. Interestingly, one of the candidate genes, cathepsin E (CTSE), exhibited differential methylation at the promoter region that was inversely correlated with the radio-resistance of patient-derived organoids, suggesting that methylation status could contribute to radio-responsiveness. On the basis of these results, we plan to pursue development of a gene chip for diagnosing the radio-responsiveness of LARC patients, with the hope that our efforts will ultimately improve the prognosis of LARC patients.

Double the Fun, Double the Trouble: Paralogs and Homologs Functioning in the Endoplasmic Reticulum

The evolution of eukaryotic genomes has been propelled by a series of gene duplication events, leading to an expansion in new functions and pathways. While duplicate genes may retain some functional redundancy, it is clear that to survive selection they cannot simply serve as a backup but rather must acquire distinct functions required for cellular processes to work accurately and efficiently. Understanding these differences and characterizing gene-specific functions is complex. Here we explore different gene pairs and families within the context of the endoplasmic reticulum (ER), the main cellular hub of lipid biosynthesis and the entry site for the secretory pathway. Focusing on each of the ER functions, we highlight specificities of related proteins and the capabilities conferred to cells through their conservation. More generally, these examples suggest why related genes have been maintained by evolutionary forces and provide a conceptual framework to experimentally determine why they have survived selection.

Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm

The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning algorithms in the development of scoring functions has led to a significant improvement in docking performance. In this work, we introduce a new machine learning (ML) based scoring function called ET-Score, which employs the distance-weighted interatomic contacts between atom type pairs of the ligand and the protein for featurizing protein-ligand complexes and Extremely Randomized Trees algorithm for the training process. The performance of ET-Score is compared with some successful ML-based scoring functions and several popular classical scoring functions on the PDBbind 2016v core set. It is shown that our ET-Score model (with Pearson's correlation of 0.827 and RMSE of 1.332) achieves very good performance in comparison with most of the ML-based scoring functions and all classical scoring functions despite its extremely low computational cost. ET-Score's codes are freely available on the web at https://github.com/miladrayka/ET_Score.

Prognostic and clinicopathological insights of phosphodiesterase 9A gene as novel biomarker in human colorectal cancer

BACKGROUND

PDE9A (Phosphodiesterase 9A) plays an important role in proliferation of cells, their differentiation and apoptosis via intracellular cGMP (cyclic guanosine monophosphate) signaling. The expression pattern of PDE9A is associated with diverse tumors and carcinomas. Therefore, PDE9A could be a prospective candidate as a therapeutic target in different types of carcinoma. The study presented here was designed to carry out the prognostic value as a biomarker of PDE9A in Colorectal cancer (CRC). The present study integrated several cancer databases with in-silico techniques to evaluate the cancer prognosis of CRC.

RESULTS

The analyses suggested that the expression of PDE9A was significantly down-regulated in CRC tissues than in normal tissues. Moreover, methylation in the DNA promoter region might also manipulate PDE9A gene expression. The Kaplan-Meier curves indicated that high level of expression of PDE9A gene was associated to higher survival in OS, RFS, and DSS in CRC patients. PDE9A demonstrated the highest positive correlation for rectal cancer recurrence with a marker gene CEACAM7. Furtheremore, PDE9A shared consolidated pathways with MAPK14 to induce survival autophagy in CRC cells and showed interaction with GUCY1A2 to drive CRPC.

CONCLUSIONS

Overall, the prognostic value of PDE9A gene could be used as a potential tumor biomarker for CRC.

Potential effect of Maxing Shigan decoction against coronavirus disease 2019 (COVID-19) revealed by network pharmacology and experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Since the occurrence of coronavirus disease 2019 (COVID-19) in Wuhan, China in December 2019, COVID-19 has been quickly spreading out to other provinces and countries. Considering that traditional Chinese medicine (TCM) played an important role during outbreak of SARS and H1N1, finding potential alternative approaches for COVID-19 treatment is necessary before vaccines are developed. According to previous studies, Maxing Shigan decoction (MXSGD) present a prominent antivirus effect and is often used to treat pulmonary diseases. Furthermore, we collected 115 open prescriptions for COVID-19 therapy from the National Health Commission, State Administration of TCM and other organizations, MXSGD was identified as the key formula. However, the underlying molecular mechanism of MXSGD against COVID-19 is still unknown.

AIM OF THE STUDY

The present study aimed to evaluate the therapeutic mechanism of MXSGD against COVID-19 by network pharmacology and in vitro experiment verification, and screen the potential components which could bind to key targets of COVID-19 via molecular docking method.

MATERIALS AND METHODS

Multiple open-source databases related to TCM or compounds were employed to screen active ingredients and potential targets of MXSGD. Network pharmacology analysis methods were used to initially predict the antivirus and anti-inflammatory effects of MXSGD against COVID-19. IL-6 induced rat lung epithelial type Ⅱ cells (RLE-6TN) damage was established to explore the anti-inflammatory damage activity of MXSGD. After MXSGD intervention, the expression level of related proteins and their phosphorylation in the IL-6 mediated JAK-STAT signaling pathway were detected by Western blot. Molecular docking technique was used to further identify the potential substances which could bind to three key targets (ACE2, Mpro and RdRp) of COVID-19.

RESULTS

In this study, 105 active ingredients and 1025 candidate targets were selected for MXSGD, 83 overlapping targets related to MXSGD and COVID-19 were identified, and the protein-protein interaction (PPI) network of MXSGD against COVID-19 was constructed. According to the results of biological enrichment analysis, 63 significant KEGG pathways were enriched, and most of them were related to signal transduction, immune system and virus infection. Furthermore, according the relationship between signal pathways, we confirmed MXSGD could effectively inhibit IL-6 mediated JAK-STAT signal pathway related protein expression level, decreased the protein expression levels of p-JAK2, p-STAT3, Bax and Caspase 3, and increased the protein expression level of Bcl-2, thereby inhibiting RLE-6TN cells damage. In addition, according to the LibDock scores screening results, the components with strong potential affinity (Top 10) with ACE2, Mpro and RdRp are mainly from glycyrrhiza uralensis (Chinese name: Gancao) and semen armeniacae amarum (Chinese name: Kuxingren). Among them, amygdalin was selected as the optimal candidate component bind to all three key targets, and euchrenone, glycyrrhizin, and glycyrol also exhibited superior affinity interactions with ACE2, Mpro and RdRp, respectively.

CONCLUSION

This work explained the positive characteristics of multi-component, multi-target, and multi-approach intervention with MXSGD in combating COVID-19, and preliminary revealed the antiviral and anti-inflammatory pharmacodynamic substances and mechanism of MXSGD, which might provide insights into the vital role of TCM in the prevention and treatment of COVID-19.

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus–host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.

Peptide array-based interactomics

The analysis of protein-protein interactions (PPIs) is essential for the understanding of cellular signaling. Besides probing PPIs with immunoprecipitation-based techniques, peptide pull-downs are an alternative tool specifically useful to study interactome changes induced by post-translational modifications. Peptides for pull-downs can be chemically synthesized and thus offer the possibility to include amino acid exchanges and post-translational modifications (PTMs) in the pull-down reaction. The combination of peptide pull-down and analysis of the binding partners with mass spectrometry offers the direct measurement of interactome changes induced by PTMs or by amino acid exchanges in the interaction site. The possibility of large-scale peptide synthesis on a membrane surface opened the possibility to systematically analyze interactome changes for mutations of many proteins at the same time. Short linear motifs (SLiMs) are amino acid patterns that can mediate protein binding. A significant number of SLiMs are located in regions of proteins, which are lacking a secondary structure, making the interaction motifs readily available for binding reactions. Peptides are particularly well suited to study protein interactions, which are based on SLiM-mediated binding. New technologies using arrayed peptides for interaction studies are able to identify SLIM-based interaction and identify the interaction motifs.

Pyruvate kinase M2 in chronic inflammations: a potpourri of crucial protein-protein interactions

Chronic inflammation (CI) is a primary contributing factor involved in multiple diseases like cancer, stroke, diabetes, Alzheimer's disease, allergy, asthma, autoimmune diseases, coeliac disease, glomerulonephritis, sepsis, hepatitis, inflammatory bowel disease, reperfusion injury, and transplant rejections. Despite several expansions in our understanding of inflammatory disorders and their mediators, it seems clear that numerous proteins participate in the onset of CI. One crucial protein pyruvate kinase M2 (PKM2) much studied in cancer is also found to be inextricably woven in the onset of several CI's. It has been found that PKM2 plays a significant role in several disorders using a network of proteins that interact in multiple ways. For instance, PKM2 forms a close association with epidermal growth factor receptors (EGFRs) for uncontrolled growth and proliferation of tumor cells. In neurodegeneration, PKM2 interacts with apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) to onset Alzheimer's disease pathogenesis. The cross-talk of protein tyrosine phosphatase 1B (PTP1B) and PKM2 acts as stepping stones for the commencement of diabetes. Perhaps PKM2 stores the potential to unlock the pathophysiology of several diseases. Here we provide an overview of the notoriously convoluted biology of CI's and PKM2. The cross-talk of PKM2 with several proteins involved in stroke, Alzheimer's, cancer, and other diseases has also been discussed. We believe that considering the importance of PKM2 in inflammation-related diseases, new options for treating various disorders with the development of more selective agents targeting PKM2 may appear.

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

Bisphenol A promotes breast cancer cell proliferation by driving miR-381-3p-PTTG1-dependent cell cycle progression

Bisphenol A (BPA) is a high-production-volume industrial chemical that facilitates the development of breast cancer. However, the molecular mechanism associated with BPA-induced breast cancer cell proliferation and migration remains elusive. In our study, we exposed MCF-7 cells to different concentrations of BPA (0.1, 1 and 10 μM) for 24, 48, or 72 h. We found that BPA exposure significantly promoted MCF-7 cell proliferation and migration but not invasion. To elucidate the mechanisms, the differentially expressed genes between the BPA and control groups were investigated with the Gene Expression Omnibus (GEO) database through GEO2R. Kyoto Encyclopedia of Genes and Genomes (KEGG) and pathway action network analyses demonstrated the important role of the cell cycle pathway in the effects of BPA exposure on MCF-7 cells. Importantly, analysis with the cytoHubba plugin of Cytoscape software coupled with analysis of enriched genes in the cell cycle pathway identified PTTG1 and CDC20 (two hub genes) as key targets associated with BPA-induced MCF-7 cell proliferation and migration. Interestingly, BPA significantly increased the protein expression levels of PTTG1 but not CDC20. Knockdown of PTTG1 inhibited the BPA-induced increase in proliferation and maintained cell cycle progression. In addition, we confirmed that the increased expression of PTTG1 upon BPA exposure was caused by miR-381-3p inhibition. Moreover, we verified that miR-381-3p expression was low and inversely correlated with PTTG1 expression in breast cancer tissues. Together, these findings demonstrate that BPA promotes high PTTG1 expression and alters the cell cycle to enhance MCF-7 cell proliferation by inhibiting miR-381-3p expression.

Biological network analysis with deep learning

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Biological network analysis with deep learning

Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

Abattoir survey of bovine tuberculosis in tanta, centre of the Nile delta, with in silico analysis of gene mutations and protein-protein interactions of the involved mycobacteria

Bovine tuberculosis is a transboundary disease of high economic and public health burden worldwide. In this study, post-mortem examination of 750 cattle and buffalo in Tanta abattoir, Centre of the Nile Delta, revealed visible TB in 4% of animals and a true prevalence of 6.85% (95% CI: 5.3%-8.9%). Mycobacterial culture, histopathology and RT-PCR targeting all members of M. tuberculosis complex were performed, upon which 85%, 80% and 100% of each tested lesions were confirmed as TB, respectively. Mpb70-targeting PCR was conducted on ten RT-PCR positive samples for sequencing and identified nine Mycobacterium (M.) bovis strains and, interestingly, one M. tuberculosis (Mtb) strain from a buffalo. Bioinformatics tools were used for prediction of mutations, nucleotide polymorphisms, lineages, drug resistance and protein-protein interactions (PPI) of the sequenced strains. The Mtb strain was resistant to rifampicin, isoniazid and streptomycin, and to the best of our knowledge, this is the first report of multidrug resistant (MDR)-Mtb originating from buffaloes. Seven M. bovis strains were resistant to ethambutol and ethionamide. Such resistances were associated with KatG, rpoB, rpsL, embB and ethA genes mutations. Other mutations and nucleotide polymorphisms were also predicted, some are reported for the first time and require experimental work for validation. PPI revealed more interactions than what would be expected for a random set of proteins of similar size and had dense interactions between nodes that are biologically connected, as a group. Two M. bovis strains belonged to BOV AFRI lineage (Spoligotypes BOV 1; BOV 2) and eight strains belonged to East-Asian (Beijing) lineage. In conclusion, visible TB was prevalent in the study area, RT-PCR is the best to confirm the disease, MDR-Mtb is associated with buffalo TB, and mycobacteria of different lineages carry many resistance genes to chemotherapeutic agents used in treatment of human TB constituting a major public health risk.

Expression and affinity purification of recombinant mammalian Mitochondrial Ribosomal Small Subunit (MRPS) proteins and protein-protein interaction analysis indicate putative role in tumorigenic cellular processes

MRPS group of proteins are structural constituents of the small subunit of mitoribosomes involved in translation. Recent studies indicate role in tumorigenic process, however, unlike cytosolic ribosomal proteins, knowledge on the role of MRPS proteins in alternate cellular processes is very limited. Mapping protein-protein interactions (PPIs) onto known cellular processes can be a valuable tool to identify novel protein functions. In this study, to identify PPIs of MRPS proteins, we have constructed thirty-one GST/MRPS fusion clones. GST/MRPS fusion proteins were confirmed by MALDI-TOF analysis. GST pull-downs were performed using eight GST/MRPS proteins (MRPS9, MRPS10, MRPS11, MRPS18B, MRPS31, MRPS33, MRPS38, MRPS39), GST alone as pull-down control, and HEK293 cell lysate as the source for anchor proteins followed by nLC/MS/MS analysis and probable PPIs of eight MRPS proteins were identified. Three PPIs from GST pull-downs and interaction between six MRPS proteins and p53 previously reported in PPI database were validated. The PPI network analysis revealed putative role in cellular processes with implications for tumorigenesis. Gene expression screening of a cancer cell line panel indicated overexpression of MRPS10 and MRPS31 in breast cancer. Co-expression module identification tool analysis of breast cancer gene expression and MRPS10 and MRPS31 PPIs revealed putative role for PPI with ACADSB in fatty acid oxidation process regulated by brain-derived neurotrophic factor (BDNF) signaling pathway.

Emodin Inhibits the Proliferation of MCF-7 Human Breast Cancer Cells Through Activation of Aryl Hydrocarbon Receptor (AhR)

Natural products have proved to be a promising source for the development of potential anticancer drugs. Emodin, a natural compound from Rheum palmatum, is used to treat several types of cancers, including lung, liver, and pancreatic. However, there are few reports regarding its use in the treatment of breast cancer. Thus, the therapeutic effect and mechanism of emodin on MCF-7 human breast cancer cells were investigated in this study. Morphological observations and cell viability were evaluated to determine the anti-proliferation activity of emodin. Network pharmacology and molecular docking were performed to screen the potential targets. Western blot analysis was used to explore a potential antitumor mechanism. The results showed that emodin (50–100 μmol/L) could significantly inhibit the proliferation of MCF-7 cells in a time and dose-dependent manner. Furthermore, virtual screening studies indicated that emodin was a potent aryl hydrocarbon receptor (AhR) agonist in chemotherapy for breast cancer. Finally, when MCF-7 cells were treated with emodin (100 μmol/L) for 24 h, the AhR and cytochrome P450 1A1 (CYP1A1) protein expression levels were significantly upregulated compared with the control group. Our study indicated that emodin exhibited promising antitumor activity in MCF-7 cells, likely through activation of the AhR-CYP1A1 signaling pathway. These findings lay a foundation for the application of emodin in breast cancer treatment.

Deep Learning applications for COVID-19

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of big data and how learning tasks are constructed. We begin by evaluating the current state of Deep Learning and conclude with key limitations of Deep Learning for COVID-19 applications. These limitations include Interpretability, Generalization Metrics, Learning from Limited Labeled Data, and Data Privacy. Natural Language Processing applications include mining COVID-19 research for Information Retrieval and Question Answering, as well as Misinformation Detection, and Public Sentiment Analysis. Computer Vision applications cover Medical Image Analysis, Ambient Intelligence, and Vision-based Robotics. Within Life Sciences, our survey looks at how Deep Learning can be applied to Precision Diagnostics, Protein Structure Prediction, and Drug Repurposing. Deep Learning has additionally been utilized in Spread Forecasting for Epidemiology. Our literature review has found many examples of Deep Learning systems to fight COVID-19. We hope that this survey will help accelerate the use of Deep Learning for COVID-19 research.

Development of Peptide-Based PDZ Domain Inhibitors

Over the past decades, peptide-based drugs have gained increasing interest in a wide range of treatment applications, primarily because of high potency and selectivity, as well as good efficacy, tolerability, and safety often achieved with peptides. Attempts to target postsynaptic density protein of 95 (PSD-95) PSD-95/Discs large/Zonula occludens-1 (PDZ) domains, which mediate the formation of a ternary complex with the N-methyl-D-aspartate (NMDA) receptor and neuronal nitric oxide synthase (nNOS) responsible for excitotoxicity in ischemic stroke, by high-affinity small molecules have failed in the past. In this chapter, we focus on the discovery of peptide-based drugs targeting PSD-95, using AVLX-144 as an example, from the synthesis, over binding assays to its target, to further in vitro experiments based on the development of AVLX-144, a potential stroke treatment, which is planned to enter clinical trials in 2020.

Molecular and Biochemical Techniques for Deciphering p53-MDM2 Regulatory Mechanisms

The p53 and Mouse double minute 2 (MDM2) proteins are hubs in extensive networks of interactions with multiple partners and functions. Intrinsically disordered regions help to adopt function-specific structural conformations in response to ligand binding and post-translational modifications. Different techniques have been used to dissect interactions of the p53-MDM2 pathway, in vitro, in vivo, and in situ each having its own advantages and disadvantages. This review uses the p53-MDM2 to show how different techniques can be employed, illustrating how a combination of in vitro and in vivo techniques is highly recommended to study the spatio-temporal location and dynamics of interactions, and to address their regulation mechanisms and functions. By using well-established techniques in combination with more recent advances, it is possible to rapidly decipher complex mechanisms, such as the p53 regulatory pathway, and to demonstrate how protein and nucleotide ligands in combination with post-translational modifications, result in inter-allosteric and intra-allosteric interactions that govern the activity of the protein complexes and their specific roles in oncogenesis. This promotes elegant therapeutic strategies that exploit protein dynamics to target specific interactions.

Graph embeddings on gene ontology annotations for protein-protein interaction prediction

Background

Protein–protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term–term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph.

Results

We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.

Conclusion

Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

Next Generation Sequencing Identify Rare Copy Number Variants in Non-syndromic Patent Ductus Arteriosus

Patent ductus arteriosus (PDA) is a common congenital cardiovascular malformation with both inherited and acquired causes. Several genes have been reported to be related to PDA, but the molecular pathogenesis is still unclear. Here, we screened a population matched cohort of 39 patients with PDA and 100 healthy children using whole exome sequencing (WES). And identified 10 copy number variants (CNVs) and 20 candidate genes using Gene ontology (GO) functional enrichment analysis. In gene network analysis, we screened 7 pathogenic CNVs of 10 candidate genes (MAP3K1, MYC, VAV2, WDR5, RXRA, APLNR, TJP1, ERCC2, FOSB, CHRNA4). Further analysis of transcriptome array showed that 7 candidate genes (MAP3K1, MYC, VAV2, APLNR, TJP1, FOSB, CHRNA4) were indeed significantly expressed in human embryonic heart. Moreover, CHRNA4 was observed the most important genes. Our data provided rare CNVs as potential genetic cause of PDA in humans and also advance understanding of the genetic components of PDA.

Network vaccinology

The structure and function of the immune system is governed by complex networks of interactions between cells and molecular components. Vaccination perturbs these networks, triggering specific pathways to induce cellular and humoral immunity. Systems vaccinology studies have generated vast data sets describing the genes related to vaccination, motivating the use of new approaches to identify patterns within the data. Here, we describe a framework called Network Vaccinology to explore the structure and function of biological networks responsible for vaccine-induced immunity. We demonstrate how the principles of graph theory can be used to identify modules of genes, proteins, and metabolites that are associated with innate and adaptive immune responses. Network vaccinology can be used to assess specific and shared molecular mechanisms of different types of vaccines, adjuvants, and routes of administration to direct rational design of the next generation of vaccines.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

NMR fragment screening reveals a novel small molecule binding site near the catalytic surface of the disulfide-dithiol oxidoreductase enzyme DsbA from Burkholderia pseudomallei

The presence of suitable cavities or pockets on protein structures is a general criterion for a therapeutic target protein to be classified as 'druggable'. Many disease-related proteins that function solely through protein-protein interactions lack such pockets, making development of inhibitors by traditional small-molecule structure-based design methods much more challenging. The 22 kDa bacterial thiol oxidoreductase enzyme, DsbA, from the gram-negative bacterium Burkholderia pseudomallei (BpsDsbA) is an example of one such target. The crystal structure of oxidized BpsDsbA lacks well-defined surface pockets. BpsDsbA is required for the correct folding of numerous virulence factors in B. pseudomallei, and genetic deletion of dsbA significantly attenuates B. pseudomallei virulence in murine infection models. Therefore, BpsDsbA is potentially an attractive drug target. Herein we report the identification of a small molecule binding site adjacent to the catalytic site of oxidized BpsDsbA. ¹H^N CPMG relaxation dispersion NMR measurements suggest that the binding site is formed transiently through protein dynamics. Using fragment-based screening, we identified a small molecule that binds at this site with an estimated affinity of K_D ~ 500 µM. This fragment inhibits BpsDsbA enzymatic activity in vitro. The binding mode of this molecule has been characterized by NMR data-driven docking using HADDOCK. These data provide a starting point towards the design of more potent small molecule inhibitors of BpsDsbA.

IHP-PING-generating integrated human protein-protein interaction networks on-the-fly

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

In Situ Cross-Linking of Artificial Basement Membranes in 3D Tissues and Their Size-Dependent Molecular Permeability

In the human body, highly organized tissues rely on the compartmentalization effect of basement membranes (BMs) that separate different types of cells. We recently reported an artificial basement membrane (A-BM) composed of type-IV collagen and laminin (Col-IV/LM), which are the main components of natural BMs, for cell compartmentalization in three-dimensional (3D) tissues. However, such compartmentalized structures can be maintained only for 3 days, probably due to the degradation issues. In this study, a robust A-BM was fabricated by in situ cross-linking the Col-IV/LM layer-by-layer (LbL) nanofilms in 3D tissues by transglutaminase. The effects of molecular size and configuration on the permeability of obtained A-BMs were comprehensively studied using polystyrene nanoparticles (PS NPs) and dextran with various hydrodynamic diameters, as well as albumin. The findings agreed well with the known size-selective behavior of the glomerular basement membrane. Cross-linked Col-IV/LM nanofilms demonstrate improved stability and a more powerful barrier effect to maintain cell compartmentalization for organized 3D tissues. This in vitro A-BM exhibit great potentials for the design of more complex compartmentalized 3D tissues, for understanding the unique cell-cell cross talk through BMs, and for providing a more reliable 3D tissue model for new drug screening and other in vitro physiological studies.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

MOTIVATION

Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein-protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses.

RESULTS

We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities.

AVAILABILITY AND IMPLEMENTATION

Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.