Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions

INTRODUCTION

Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community.

AREAS COVERED

In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds.

EXPERT OPINION

Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

Immunoinformatics Study: Multi-Epitope Based Vaccine Design from SARS-CoV-2 Spike Glycoprotein

The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

Advances in the Mechanism of Luteolin against Hepatocellular Carcinoma Based on Bioinformatics and Network Pharmacology

As one of the most common malignant tumors, hepatocellular carcinoma (HCC) has a rising incidence rate and also seriously endangers human life and health. According to research reports, hepatitis B, hepatitis C, intake of aflatoxin in the diet, and the effects of alcohol and other chemicals can induce an increase in the incidence of liver cancer. However, in the current clinical treatment of HCC, most of the drugs are chemical drugs, which have relatively large side effects and are prone to drug resistance. Therefore, the development of natural compounds to treat HCC has become a new treatment strategy. Several studies have shown that flavonoids have shown outstanding effects and exhibit strong tumor growth inhibitory effects in vivo experimental studies. Luteolin, as a natural flavonoid, has anti-tumor, anti-inflammatory, anti-viral, anti-oxidation, immune regulation, and other pharmacological effects. The anti-cancer mechanism of luteolin mainly directly acts on tumor cells to inhibit their growth, induce cell apoptosis, reduce tumor tissue angiogenesis, regulate long non-coding RNA, affect immunogenic cell death, and regulate autophagy. As well as improving the curative effect of radiotherapy and chemotherapy and chemoprevention. In this study, we evaluated the function of luteolin in regulating cancer cell proliferation, migration, and invasion will summarize and analyze luteolin and its mechanism of regulating HCC to improve the role of luteolin in the clinical prevention and treatment of HCC.

Network-Based Data Analysis Reveals Ion Channel-Related Gene Features in COVID-19: A Bioinformatic Approach

Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein-protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)-DEG, DEG-microRNA (miRNA) and DEG-disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF-DEG and DEG-miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129-2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19.

Computational Analysis of Mutations in the Receptor-Binding Domain of SARS-CoV-2 Spike and Their Effects on Antibody Binding

Currently, SARS-CoV-2 causing coronavirus disease 2019 (COVID-19) is responsible for one of the most deleterious pandemics of our time. The interaction between the ACE2 receptors at the surface of human cells and the viral Spike (S) protein triggers the infection, making the receptor-binding domain (RBD) of the SARS-CoV-2 S-protein a focal target for the neutralizing antibodies (Abs). Despite the recent progress in the development and deployment of vaccines, the emergence of novel variants of SARS-CoV-2 insensitive to Abs produced in response to the vaccine administration and/or monoclonal ones represent a potential danger. Here, we analyzed the diversity of neutralizing Ab epitopes and assessed the possible effects of single and multiple mutations in the RBD of SARS-CoV-2 S-protein on its binding affinity to various antibodies and the human ACE2 receptor using bioinformatics approaches. The RBD-Ab complexes with experimentally resolved structures were grouped into four clusters with distinct features at sequence and structure level. The performed computational analysis indicates that while single amino acid replacements in RBD may only cause partial impairment of the Abs binding, moreover, limited to specific epitopes, the variants of SARS-CoV-2 with multiple mutations, including some which were already detected in the population, may potentially result in a much broader antigenic escape. Further analysis of the existing RBD variants pointed to the trade-off between ACE2 binding and antigenic escape as a key limiting factor for the emergence of novel SAR-CoV-2 strains, as the naturally occurring mutations in RBD tend to reduce its binding affinity to Abs but not to ACE2. The results provide guidelines for further experimental studies aiming to identify high-risk RBD mutations that allow for an antigenic escape.

Integrative COVID-19 biological network inference with probabilistic core decomposition

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Biosafety chemistry and biosafety materials: a new perspective to solve biosafety problems

The Novel Coronavirus Disease (COVID-19) has rapidly swept around the globe since its first emergence near 2020. However, people have failed to fully understand its origin or mutation. Defined as an international biosafety incident, COVID-19 has again encouraged worldwide attention to reconsider the importance of biosafety due to the adverse impact on personal well-being and social stability. Most countries have already taken measures to advocate progress in biosafety-relevant research, aiming to prevent and solve biosafety problems with more advanced techniques and products. Herein, we propose a new concept of biosafety chemistry and reiterate the notion of biosafety materials, which refer to the interdisciplinary integration of biosafety and chemistry or materials. Here, we attempt to illustrate the exquisite association that chemistry and material science possess with biosafety fields and we hope to provide a pragmatic perspective on approaches to utilize the knowledge of these two subjects to handle specific biosafety issues such as detection and disinfection of pathogenic microorganisms, personal and collective protective equipment, vaccine adjuvants and specific drugs, preservation of biogenetic resources for human, animals, and plants. In addition, we hope to convey and promote the idea of multidisciplinary cooperation to strengthen biosafety research and development of relevant products for establishing possibly specific majors to defend national security in the future.

Recent omics-based computational methods for COVID-19 drug discovery and repurposing

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus–drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

Web resources facilitate drug discovery in treatment of COVID-19

The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further.

OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources

Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and clinical data is being generated, and the bioinformatics researchers produced databases, webservers and tools to gather those publicly available data and provide an opportunity of analyzing such data. However, these bioinformatics resources are scattered and researchers need to find them from different resources discretely. To facilitate researchers in finding the resources in one frame, we have developed an integrated web portal called OverCOVID (http://bis.zju.edu.cn/overcovid/). The publicly available webservers, databases and tools associated with SARS-CoV-2 have been incorporated in the resource page. In addition, a network view of the resources is provided to display the scope of the research. Other information like SARS-CoV-2 strains is visualized and various layers of interaction resources is listed in distinct pages of the web portal. As an integrative web portal, the OverCOVID will help the scientist to search the resources and accelerate the clinical research of SARS-CoV-2.