Computational Biology Research Articles

Cucurbitacin C as an effective anti-cancer agent: unveiling its potential role against cholangiocarcinoma and mechanistic insights.

Cholangiocarcinoma (CCA) is a malignant epithelial tumor characterized by a dismal prognosis. Given the lack of therapeutic strategies and durable treatment options currently available, identifying innovative treatments for CCA is an urgent unmet clinical need. Cucurbitacin C (CuC) is a distinct variant of the cucurbitacin family, displaying promising anti-cancer activity against various tumor types. The primary objective of our research is to elucidate the promising effects of CuC on CCA. The impact of CuC on CCA cell lines was assessed by cell count kit-8 assay, EdU staining assay, colony formation assay, wound-healing assay, and Transwell assay. Flow cytometric analysis was conducted to explore the function of CuC treatments on cell-cycle distribution and apoptosis in CCA cells. Computational biology and network pharmacology approaches were utilized to predict potential targets of CuC. Furthermore, a tumor xenograft mouse model was established using CCA cells to explore the anti-cancer effects of CuC in vivo. Our research findings revealed that CuC exerted a suppressive effect on CCA cell progression. Cell viability assays, EdU staining assays, and colony formation assays demonstrated that CuC effectively suppressed viability and proliferation of CCA cells. Wound-healing assays and Transwell assays indicated that CuC effectively inhibits the migratory and invasive capabilities of CCA cells. Flow cytometry analysis elucidated that CuC played its anti-proliferative role in CCA cells by arresting G0/G1 phase and increasing apoptosis. Through bioinformatics and network pharmacology analysis, in conjunction with western blot analysis, we demonstrated CuC mediated the inhibition of CCA cell progression through modulation of JAK2/STAT3 pathway. Additionally, the CCA xenograft tumor model was established, and the results supported the inhibition of CuC treatment against CCA progression in vivo. Our study demonstrates that CuC possesses notable capabilities to suppress cell proliferation, migration, and invasion in CCA. Importantly, the inhibitory effects of CuC on CCA progression are attributed to its modulation of the JAK2/STAT3 signaling pathway. Altogether, our study demonstrated that CuC holds promise as a prospective therapeutic agent for treating CCA.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

Abstract: The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Integrated procedures for accelerating, deepening, and leading genetic inquiry: A first application on human muscle secretome.

Beyond classical procedures, bioinformatic-assisted approaches and computational biology offer unprecedented opportunities for scholars. However, these amazing possibilities still need epistemological criticism, as well as standardized procedures. Especially those topics with a huge body of data may benefit from data science (DS)-assisted methods. Therefore, the current study dealt with the combined expert-assisted and DS-assisted approaches to address the broad field of muscle secretome. We aimed to apply DS tools to fix the literature research, suggest investigation targets with a data-driven approach, predict possible scenarios, and define a workflow. Recognized scholars with expertise on myokines were invited to provide a list of the most important myokines. GeneRecommender, GeneMANIA, HumanNet, and STRING were selected as DS tools. Networks were built on STRING and GeneMANIA. The outcomes of DS tools included the top 5 recommendations. Each expert-led discussion has been then integrated with an DS-led approach to provide further perspectives. Among the results, 11 molecules had already been described as bona-fide myokines in literature, and 11 molecules were putative myokines. Most of the myokines and the putative myokines recommended by the DS tools were described as present in the cargo of extracellular vesicles. Including both supervised and unsupervised learning methods, as well as encompassing algorithms focused on both protein interaction and gene represent a comprehensive approach to tackle complex biomedical topics. DS-assisted methods for reviewing existent evidence, recommending targets of interest, and predicting original scenarios are worth exploring as in silico recommendations to be integrated with experts' ideas for optimizing molecular studies.

In silico nanotoxicology: The computational biology state of art for nanomaterial safety assessments

An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer.

Neurological potency of native plants from sub-Himalayan West Bengal through reverse pharmacology

Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), and epilepsy, pose a growing global health challenge due to an aging population. These conditions share common processes, including protein accumulation, oxidative stress, and neuro-inflammation, making their treatment complex and costly. Network pharmacology, an innovative approach integrating systems biology and computational biology, offers insights into multi-target formulations and the repurposing of existing medications for neurodegenerative diseases. We shortlisted 730 bioactive compounds from 25 traditional Himalayan plants, assessed their drug-like properties using ADME criteria, and predicted their potential target proteins through reverse docking and pharmacophore mapping. Our study identified 287 compounds with high gastrointestinal absorption and good blood-brain barrier permeability. These compounds were subjected to target prediction, yielding a list of 171 potential target proteins. Functional annotation and pathway enrichment analysis highlighted their involvement in steroid hormone-related pathways, MAPK signaling, FOXO signaling, TNF signaling, VEGF signaling, and neurotrophin signaling. Importantly, one plant, Valeriana jatamansi, exhibited an association with beta-amyloid binding activity, a potential therapeutic approach for AD. From our study we could understand how these plants modulate our body to manage these diseases. However, further in vitro and in vivo validation is needed before commercial and public use of this data.

Open MoA: revealing the mechanism of action (MoA) based on network topology and hierarchy

Abstract Motivation Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). Results We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. Availability and implementation Source code is available at https://github.com/XinmengLiao/Open_MoA.

An Evolutionary-Based Mutation With Functional Annotation to Identify Protein Complexes Within PPI Networks

     The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological  properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network.

In vitro and in silico analysis proving DPP4 inhibition and diabetes-associated gene network modulation by a polyherbal formulation: Nisakathakadi Kashaya

Dipeptidyl-peptidase IV (DPP4) inhibitors are an important class of anti-diabetic drugs recognised for their systemic biological actions. Polyherbal preparations like Ayurveda formulations are considered to be ideal sources for discovering novel DPP4 inhibitors owing to their rich phytochemical composition. The current study reports the DPP4 inhibitory potential of a clinically established Ayurvedic anti-diabetic formulation Nisakathakadi Kashaya (NK) using in vitro assay and substantiates it by identifying potential bioactives responsible for DPP4 inhibition using computational biology tools. NK showed a dose-dependent DPP4 inhibition with an IC50 of 2.06 μg GAE/mL, and the molecular docking and simulation studies showed three compounds, namely Terchebin, Locaracemoside B and 1,2,4,6 Tetra o Galloyl Beta D Glucose having stable interactions with DPP4 similar to the standard drug Vildagliptin. Further, for the reason that polyherbal formulations exert a network pharmacology mode of action, in silico analysis was carried out to identify the other putative phytochemical-protein networks modulated by NK. The complex pharmacological network of the formulation was explored further using a subnetwork of diabetes proteins and their relationship with diabetes-associated comorbidities. A number of key targets like TNFα, TGFβ1, SOD1, SOD2, AKT1, DPP4 and GLP1R were identified in the protein-protein interaction network that is vital to diabetic progression and complications. A combination of in vitro and in silico methods allowed us to prove the DPP4 inhibition potential of NK as well as provided insights into the possible pharmacological networking through which NK potentially exerts its systemic effect in diabetes management. Communicated by Ramaswamy H. Sarma

Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets.

Accurate prediction of protein stability upon a point mutation has important applications in drug discovery and personalized medicine. It remains a challenging issue in computational biology. Existing computational prediction methods, which range from mechanistic to supervised learning approaches, have experienced limited progress over the last few decades. This stagnation is largely due to their heavy reliance on both the quantity and quality of the training data. This is evident in recent state-of-the-art methods that continue to yield substantial errors on two challenging blind test sets: frataxin and p53, with average root-mean-square errors exceeding 3 and 1.5 kcal/mol, respectively, which is still above the theoretical 1 kcal/mol prediction barrier. Rigorous approaches, on the other hand, offer greater potential for accuracy without relying on training data but are computationally demanding and require both wild-type and mutant structure information. Although they showed high accuracy for conserving mutations, their performance is still limited for charge-changing mutation cases. This might be due to the lack of an available mutant structure, often represented by a simplified capped peptide. The recent advances in protein structure prediction methods now make it possible to obtain structures comparable to experimental ones, including complete mutant structure information. In this work, we compare the performance of supervised learning-based methods and rigorous approaches for predicting protein stability on point mutations in difficult targets: frataxin and p53. The rigorous alchemical method significantly surpasses state-of-the-art techniques in terms of both the root-mean-squared error and Pearson correlation coefficient in these two challenging blind test sets. Additionally, we propose an improved alchemical method that employs the pmx double-system/single-box approach to accurately predict the folding free energy change upon both conserving and charge-changing mutations. The enhanced protocol can accurately predict both types of mutations, thereby outperforming existing state-of-the-art methods in overall performance.

PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture

Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metadata standards for livestock and crops can be used to ingest scRNAseq datasets in a manner consistent with HCA DCP standards and if established resources (e.g., Terra) can be used to analyze the ingested data. Currently, the most comprehensive data ingestion portal for high throughput sequencing datasets from plants, fungi, protists, and animals/humans is Annotare (located at EMBL-European Bioinformatics Institute), ensures that sufficient metadata are collected to enable re-analysis and dissemination via the Single Cell Expression Atlas (SCEA). Annotare supports user-directed annotation and processing of their data, as well as search tools via the SCEA and transferred to the Galaxy analysis space. For animal datasets, another EMBL-EBI portal, the FAANG portal, has been developed that provides bulk and scRNAseq data access. scRNAseq data/metadata can be submitted to FAANG using a semi-automated process. We have extended this tool for scRNAseq data so that files can be validated using the HCA DCP metadata and data validation service. These files are also incorporated using EMBL-EBI’s HCA DCP ingestion service and transferred to Terra for further analysis. Once incorporated, datasets will augment the DCP resource for the scientific community. In an extension of these efforts, we have also created a Shiny-based web application, called Shiny-PIGGI, for the single cell-level transcriptomic study of pig immune tissues and peripheral blood mononuclear cells, which will be an important resource for improved annotation of porcine immune genes and cell types. The Shiny-PIGGI (https://shinypiggi.ansci.iastate.edu) is implemented completely in R, runs on any modern web browser, and requires no programming. This tool thus increases accessibility through eliminating technical training requirements for using Seurat object and related R packages commonly used in scRNAseq analysis. Our main goal was to develop an interactive web application that allows users such as animal scientists and immunologists to visualize and analyze biological datasets. This tool will be demonstrated at the meeting. We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem to facilitate single cell-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

Diffusion models in bioinformatics and computational biology

Computational biology is poised to advance precision medicine with machine learning

Computational biology is poised to advance precision medicine with machine learning Today, scientists are attempting to model whole cells using computational biology, building virtual cells that capture the dynamics of living. In the post-genomic era, vast quantities of data describing the parts of living cells, both normally functioning and diseased, have been amassed. Understanding how cells behave has been the goal of reductionist science, and many of the principles are well understood. In elucidating the details, complexity has been revealed to the degree that scientists have limited capacity to understand the systems underlying the biological networks. This is to say, cellular behavior is difficult to predict.

The cytotoxic potential of sinapic acid on luminal A breast cancer; a computational and experimental pharmacology approach

Breast cancer is a highly concerning and prevalent disease that impacts a significant proportion of women worldwide, whose repeated exposure to therapies leads to resistance for drugs; making it alarming to identify novel chemotherapeutic agents. Sinapic acid is a phenolic acid that occurs naturally and is known to exhibit cytotoxic action in a variety of cancer cell types. In the present study, we utilized cell cytotoxicity assays to assess the cytotoxic potential of sinapic acid on various breast cancer subtypes. In addition, we assessed the cell migration rate, cell apoptosis, and cell cycle phases. Moreover, we utilized multiple system biology tools to predict the potential targets, and molecular docking was performed on the hub targets followed by molecular dynamic (MD) simulations. Cytotoxicity assay was performed on cell lines MCF7, T47D, MDA-MB-468, and SKBR3 at different time exposures of 24, 48, and 96 h. Our results revealed sinapic acid to be potent on MCF7 and T47D cell lines. The cell cycle analysis and cell apoptotic assays revealed sinapic acid to cause cell death by apoptosis majorly in the G0/G1 phase. Computational biology revealed KIF18B and VKORC1 to possess the highest binding affinity of −6.5 and −7.5 kcal/mol; displayed stable trajectories on MD run. The cytotoxicity of sinapic acid on luminal A cell lines may be due to the modulation of VKORC1 and KIF18B with major cell death in the G0/G1 phase. However, the mechanism has been proposed via in silico tools, which need further validation using wet lab protocols. Communicated by Ramaswamy H. Sarma

Application of bioinformatics methods in pesticide research and development

The use of pesticides is accompanied by a number of environmental and agrotechnological problems. Many pesticides do not degrade and stay in the soil for years and have low selectivity. Massive application of pesticides with non-selective nature of action caused a gradual increase of resistance in pests due to persistent inherited changes in their DNA. This affects the efficiency of growing agricultural plants and pollution of the environment and food. Computational biology methods, which are actively developing all over the world, can help to solve this problem. Despite the fact that in Russia bioinformatics methods are used to study plant genes of animals, metagenomes of microorganisms, there are no own databases and specialized computer applications for such research and pesticide modernization. Development of domestic similar bioinformatics tools is also an urgent task. The article highlights the problem of creating new effective and environmentally friendly pesticides. The methods of bioinformatics that can be used in the research and development of pesticides are given. The stages of creating new pesticides by bioinformatics methods (review of databases, modeling of molecules, modeling of the interaction of a pesticide with a target, prediction of biological activity) are considered. A description is given of the methods for optimizing the molecular framework of pesticides, which is a change in the carbon skeleton in order to search for new active compounds and screen out many similar compounds in the chemical space. Foreign web resources used to assess the presence of pesticidal properties in substances, such as toxicity, metabolism and physico-chemical properties, and their subsequent registration as pesticides are given.

Genomic characterization and molecular evolution of human monkeypox viruses.

Monkeypox virus is a member of the family Poxviridae, as are variola virus and vaccinia virus. It has a linear double-strand DNA genome approximately 197 kb long, containing ~190 non-overlapping ORFs. Comparison of members of the Central and West African clades shows the presence of unique genes that are associated with different disease presentations, depending on the strain. The last smallpox vaccination efforts ended in the mid-1980s, and there is concern about the recent spread of human monkeypox disease around the world. Almost 87,000 human monkeypox cases have been diagnosed in the world, of which more than 10,900 were in Brazil. The aim of this study was to evaluate the epidemiology and molecular evolution of hMpxV. From computational biology analysis of 640 hMpxV genomes from 1962 to 2022, synteny breaks and gene conservation were observed between Central and West clade genomes, and strains belonged with the 2022 outbreak assigned to the West African clade. Evidence was found for diversifying selective pressure at specific sites within protein coding sequences, acting on immunomodulatory processes. The existence of different sites under diversifying and purifying selection in paralog genes indicates adaptive mechanisms underlying the host-pathogen interaction of monkeypox virus in humans.

Computational analysis of carboxylesterase genes and proteins in non-pathogenic food bacterium Alicyclobacillus acidocaldarius: insights from proteogenomics.

Over recent years, Alicyclobacillus acidocaldarius, a Gram-positive nonpathogenic rod-shaped thermo-acid-tolerant bacterium, has posed numerous challenges for the fruit juice industry. However, the bacterium's unique characteristics, particularly its nonpathogenic and thermophilic capabilities, offer significant opportunities for genetic exploration by biotechnologists. This study presents the computational proteogenomics report on the carboxylesterase (CE) enzyme in A. acidocaldarius, shedding light on structural and evolutional of CEs from this bacterium. Our analysis revealed that the average molecular weight of CEs in A. acidocaldarius was 41kDa, with an isoelectric point around 5. The amino acid composition favored negative amino acids over positive ones. The aliphatic index and hydropathicity were approximately 88 and - 0.15, respectively. While the protein sequence showed no disulfide bonds in the CEs' structure, the presence of Cys amino acids was observed in the structure of CEs. Phylogenetic analysis presented more than 99% similarity between CEs, indicating their close evolutionary relationship. By applying homology modeling, the 3-dimensional structural models of the carboxylesterase were constructed, which with the help of structural conservation and solvent accessibility analysis highlighted key residues and regions responsible for enzyme stability and conformation. The specific patterns presented the total solvent accessibility of less than 25 (Å2) was in considerable position as well as Gly residues were noticeably have high accessibility to solvent in all structures. Ala was the more frequent amino acids in the conserved-SASA of carboxylesterases. Furthermore, unsupervised agglomerative hierarchical clustering based on solvent accessibility feature successfully clustered and even distinguished this enzyme from proteases from the same genome. These findings contribute to a deeper understanding of the nonpathogenic A. acidocaldarius carboxylesterase and its potential applications in biotechnology. Additionally, structural analysis of CEs would help to address potential solutions in fruit juice industry with utilization of computational structural biology.

Therapeutic Intervention of Serine Protease Inhibitors against Hepatitis C Virus.

Hepatitis C virus (HCV) is a globally prevalent and hazardous disorder that is responsible for inducing several persistent and potentially fatal liver diseases. Current treatment strategies offer limited efficacy, often accompanied by severe and debilitating adverse effects. Consequently, there is an urgent and compelling need to develop novel therapeutic interventions that can provide maximum efficacy in combating HCV while minimizing the burden of adverse effects on patients. One promising target against HCV is the NS3-4A serine protease, a complex composed of two HCV-encoded proteins. This non-covalent heterodimer is crucial in the viral life cycle and has become a primary focus for therapeutic interventions. Although peginterferon, combined with ribavirin, is commonly employed for HCV treatment, its efficacy is hampered by significant adverse effects that can profoundly impact patients' quality of life. In recent years, the development of direct-acting antiviral agents (DAAs) has emerged as a breakthrough in HCV therapy. These agents exhibit remarkable potency against the virus and have demonstrated fewer adverse effects when combined with other DAAs. However, it is important to note that there is a potential for developing resistance to DAAs due to alterations in the amino acid position of the NS3-4A protease. This emphasizes the need for ongoing research to identify strategies that can minimize the emergence of resistance and ensure long-term effectiveness. While the combination of DAAs holds promise for HCV treatment, it is crucial to consider the possibility of drug-drug interactions. These interactions may occur when different DAAs are used concurrently, potentially compromising their therapeutic efficacy. Therefore, carefully evaluating and monitoring potential drug interactions are vital to optimize treatment outcomes. In the pursuit of novel therapeutic interventions for HCV, the field of computational biology and bioinformatics has emerged as a valuable tool. These advanced technologies and methodologies enable the development and design of new drugs and therapeutic agents that exhibit maximum efficacy, reduced risk of resistance, and minimal adverse effects. By leveraging computational approaches, researchers can efficiently screen and optimize potential candidates, accelerating the discovery and development of highly effective treatments for HCV, treatments.

Positional difference and Frequency (PdF) based alignment-free technique for genome sequence comparison

In the field of computational biology, genome sequence comparison among different species is essential and has applications in both the research and scientific fields. Owing to the lengthy processing time and large number of data sets, the alignment-based approaches are unsuitable and ineffective. Therefore, alignment-free techniques have obtained popularity for acquiring proper sequence clustering and evolutionary relationship among species. In this paper, a complete bipartite graph based Positional difference and Frequency (PdF) vector descriptor is introduced. Positional difference and Frequency, two parameters, are applied to the genome sequence to create a 16- dimensional vector descriptor using the di-nucleotide representation of genome sequence. Subsequently, a distance matrix is calculated to construct the phylogenetic trees for different data sets of mammals and viruses. The achieved outcomes are compared with the phylogenetic trees of the earlier methods viz. the FFP method, the ClustalW method, the MEV method, the PCNV method and the FIS method. In most instances, the proposed method produces more precise outcomes than the preceding techniques and has potential for genome sequence comparison on both the equal and unequal length of data-sets. Communicated by Ramaswamy H. Sarma

Exploring the Molecular Mechanism of Niuxi-Mugua Formula in Treating Coronavirus Disease 2019 via Network Pharmacology, Computational Biology, and Surface Plasmon Resonance Verification.

In China, Niuxi-Mugua formula (NMF) has been widely used to prevent and treat coronavirus disease 2019 (COVID-19). However, the mechanism of NMF for treating COVID-19 is not yet fully understood. This study aimed to explore the potential mechanism of NMF for treating COVID-19 by network pharmacology, computational biology, and surface plasmon resonance (SPR) verification. The NMF-compound-target network was constructed to screen the key compounds, and the Molecular Complex Detection (MCODE) tool was used to screen the preliminary key genes. The overlapped genes (OGEs) and the preliminary key genes were further analyzed by enrichment analysis. Then, the correlation analysis of immune signatures and the preliminary key genes was performed. Molecular docking and molecular dynamic (MD) simulation assays were applied to clarify the interactions between key compounds and key genes. Moreover, the SPR interaction experiment was used for further affinity kinetic verification. Lipid and atherosclerosis, TNF, IL-17, and NF-kappa B signaling pathways were the main pathways of NMF in the treatment of COVID-19. There was a positive correlation between almost the majority of immune signatures and all preliminary key genes. The key compounds and the key genes were screened out, and they were involved in the main pathways of NMF for treating COVID-19. Moreover, the binding affinities of most key compounds binding to key genes were good, and IL1B-Quercetin had the best binding stability. SPR analysis further demonstrated that IL1B-Quercetin showed good binding affinity. Our findings provided theoretical grounds for NMF in the treatment of COVID19.