Virtual Drug Screening Research Articles

Evaluations of FDA-approved Drugs Targeting 3CLP of SARS-CoV-2 Employing a Repurposing Strategy.

The SARS-CoV-2 coronavirus (COVID-19) has raised innumerable global concerns, and few effective treatment strategies have yet been permitted by the FDA to lighten the disease burden. SARS-CoV-2 3C-like proteinase (3CLP) is a crucial protease and plays a key role in the viral life cycle, as it controls replication, and thus, it is viewed as a target for drug design. In this study, we performed structure-based virtual screening of FDA drugs approved during 2015-2019 (a total of 220 drugs) for interaction with the active site of 3CLP (PDB ID 6LU7) using AutoDock 4.2. We report the top ten drugs that outperform the reported drugs against 3CLP (Elbasvir and Nelfinavir), particularly Cefiderocol, having the highest affinity among the compounds tested, with a binding energy of -9.97 kcal/mol. H-bond (LYS102:HZ2-ligand: O49), hydrophobic (ligand-VAL104), and electrostatic (LYS102:NZ-ligand: O50) interactions were observed in the cefiderocol-3CLP complex. The docked complex was subjected to a 50 ns molecular dynamics study to check its stability, and stable RMSD and RMSF graphs were observed. Accordingly, we suggest cefiderocol might be effective against SARS-CoV-2 and urge that experimental validation be performed to determine the antiviral efficacy of cefiderocol against SARS-CoV-2. Along with these, cefiderocol is effective for treating respiratory tract pathogens and a wide range of gram-negative bacteria for whom there are limited therapeutic alternatives. This article aimed to explore the FDA-approved drugs as a repurposing study against 3CLP for COVID-19 management.

Integrated virtual screening and MD simulation study to discover potential inhibitors of mycobacterial electron transfer flavoprotein oxidoreductase.

Tuberculosis (TB) continues to be a major global health burden, with high incidence and mortality rates, compounded by the emergence and spread of drug-resistant strains. The limitations of current TB medications and the urgent need for new drugs targeting drug-resistant strains, particularly multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB, underscore the pressing demand for innovative anti-TB drugs that can shorten treatment duration. This has led to a focus on targeting energy metabolism of Mycobacterium tuberculosis (Mtb) as a promising approach for drug discovery. This study focused on repurposing drugs against the crucial mycobacterial protein, electron transfer flavoprotein oxidoreductase (EtfD), integral to utilizing fatty acids and cholesterol as a carbon source during infection. The research adopted an integrative approach, starting with virtual screening of approved drugs from the ZINC20 database against EtfD, followed by molecular docking, and concluding with molecular dynamics (MD) simulations. Diacerein, levonadifloxacin, and gatifloxacin were identified as promising candidates for repurposing against TB based on their strong binding affinity, stability, and interactions with EtfD. ADMET analysis and anti-TB sensitivity predictions assessed their pharmacokinetic and therapeutic potential. Diacerein and levonadifloxacin, previously unexplored in anti-tuberculous therapy, along with gatifloxacin, known for its efficacy in drug-resistant TB, have broad-spectrum antimicrobial properties and favorable pharmacokinetic profiles, suggesting potential as alternatives to current TB treatments, especially against resistant strains. This study underscores the efficacy of computational drug repurposing, highlighting bacterial energy metabolism and lipid catabolism as fruitful targets. Further research is necessary to validate the clinical suitability and efficacy of diacerein, levonadifloxacin, and gatifloxacin, potentially enhancing the arsenal against global TB.

Structure-based virtual screening and molecular dynamics simulations of FDA-approved drugs targeting MALAT1

Structure-based virtual screening and molecular dynamics simulations of FDA-approved drugs targeting MALAT1

Identification of Anti-Inflammatory and Anti-Cancer Compounds Targeting the NF-κB-NLRP3 Inflammasome Pathway from a Phytochemical Library of the Sideritis Genus

Ethnobotanical relevanceFor centuries, the aerial parts of Sideritis species have been known for their medicinal properties as herbal teas. Although the antioxidant and anti-inflammatory properties of the genus have been widely documented, the underlying mechanisms are yet to be sufficiently clarified. Aim of the studyWe investigated the anti-inflammatory and anticancer activities of phytochemicals of the Sideritis genus. Material and methodsThrough literature mining, a chemical library containing 657 components of the Sideritis genus was formed. We studied these compounds for binding to NLRP3 and NF-κB proteins in silico by virtual drug screening and molecular docking, and in vitro by microscale thermophoresis (MST). Liquid chromatography-high-resolution mass spectrometry analysis (LC-HRMS) was performed in the Sideritis extracts. One of the identified compounds, verbascoside, was investigated for its cytotoxic activity by mining a panel of 49 tumor cell lines in the data repository of the National Cancer Institute (NCI, USA). ResultsVirtual screening and molecular docking results highlighted two compounds targeting both proteins of interest, i.e., verbascoside (acteoside) and apigenin 7,4′-bis(trans-p-coumarate), as both had lowest binding energies of less than -10 kcal/mol. Using MST, we then verified that both compounds bound to the target proteins. Verbascoside bound to NLRP3 and NF-κB with Kd values of 0.67 ± 0.18 μM and 0.01 ± 0.08 μM, while apigenin 7,4′-bis(trans-p-coumarate) had Kd values of 4.60 ± 1.66 μM and 0.27 ± 0.75 μM, respectively. Verbascoside was abundant in the Sideritis extracts, according to LC-HRMS analysis. Since inflammation is strongly related to carcinogenesis, we investigated the anticancer activity of verbascoside in the second part of this study. We investigated the activity of verbascoside in 49 tumor cell lines of the NCI. Comparing its activity with 81 standard anticancer drugs revealed numerous interactions with DNA-damaging agents (alkylators, topoisomerase I/II inhibitors, antimetabolites), indicating that verbascoside may also affect the DNA of tumor cells. We further investigated the involvement of verbascoside in several main drug resistance mechanisms, i.e., ABC transporters, oncogenes, tumor suppressors, cellular proliferation rates, and other parameters. Except for the correlation to the mutational status of NRAS, no other significant relationships were found, indicating that verbascoside is not involved in most of the common drug resistance mechanisms. Two-dimensional cluster analysis-based heatmap generation of a proteomic profile from 40 out of 3171 proteins revealed a significant correlation between the expression of these proteins in 49 tumor cell lines, and the cellular response to verbascoside. This indicates that the presence of these proteins is a determinant for sensitivity or resistance to this natural product. ConclusionThe database established here represents a valuable resource for the screening of bioactivites of the Sideritis genus. The experimental validation of the anti-inflammatory and cytotoxic activities of selected compounds proved that virtual drug screening and molecular docking are suitable tools for the identification of putative drug candidates. Verbascoside was among the top 10 compounds binding to two key anti-inflammatory proteins, NLRP3 and NF-kB. Additionally, data from the NCI indicate that verbascoside is not linked to main drug resistance mechanisms.

The Application and Challenges of Artificial Intelligence in G Protein-Coupled Receptor Drug Virtual Screening

G protein-coupled receptors play important roles in various diseases and are key targets for drug discovery. Virtual screening has traditionally been used to identify potential drug candidates from large compound libraries but faces challenges in computational resources and time constraints. The integration of artificial intelligence technologies has improved screening precision and reduced costs. This review discusses current developments, challenges, and future directions in leveraging artificial intelligence to advance GPCR drug screening.

Low-Dose Treatment with Epirubicin, a Novel Histone Deacetylase 1 Inhibitor, Exerts Anti-leukemic Effects by Inducing Ferroptosis

AimsLeukemia is hematopoietic stem cell malignant tumor with poor outcomes. Histone deacetylase 1 (HDAC1) is highly expressed in leukemia and current HDAC1 inhibitors have clinical limitations in leukemia therapy. Therefore, novel HDAC1 inhibitor is imperative to being found and its mechanism needs to be further explored. Materials and methodsNovel HDAC1 inhibitors were discovered through drug virtual screening. CCK-8, EdU and soft agar assay were used to assess the anti-leukemic effect of the candidate HDAC1 inhibitor. ROS, lipid peroxidation, intracellular Fe2+ and LIP assay were employed to verify cell ferroptosis. Additionally, a xenograft model was performed to explore the efficacy and safety of candidate HDAC1 inhibitor in vivo. ResultsHDAC1 might be a promising therapeutic target for leukemia and Epirubicin (Epi) could be used as a potential HDAC1 inhibitor. Low-dose Epi exhibited good anti-leukemic effects by inhibiting cell proliferation, DNA synthesis and colony formation. Low-dose Epi could induce ferroptosis by triggering lipid peroxidation, which was better than that treated with current HDAC1 inhibitors Chidamide or Vorinostat, ROS generation and Fe2+ overload in leukemia cells. Mechanistically, low-dose Epi induced ferroptosis by targeting amino acid metabolism and iron metabolism. Similar results were found in a xenograft model in NOG mice with a good safety profile. ConclusionOur study demonstrated that Epi might be used as a HDAC1 inhibitor. Low-dose Epi could inhibit tumor progression by inducing cell ferroptosis in vitro and in vivo. Thus, Epi administration with lower concentration may be much more favorable and safer in the treatment with leukemia.

Identification of nitrile-containing isoquinoline-related natural product derivatives as coronavirus entry inhibitors in silico and in vitro

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of infections and deaths worldwide since its emergence in Wuhan, China, in late 2019. Natural product inhibitors targeting the interaction between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2 (ACE2), crucial for viral attachment and cellular entry, are of significant interest as potential antiviral agents. In this study a library of nitrile- and sulfur-containing natural product derived compounds were used for virtual drug screening against the RBD of the SARS-CoV-2 spike protein. The top 18 compounds from docking were tested for their efficacy to inhibit virus entry. In vitro experiments revealed that compounds 9, 14, and 15 inhibited SARS-CoV-2 pseudovirus and live virus entry in HEK-ACE2 and Vero E6 host cells at low micromolar IC50 values. Cell viability assays showed these compounds exerted low cytotoxicity towards MRC5, Vero E6, and HEK-ACE2 cell lines. Microscale thermophoresis revealed all three compounds strongly bound to the RBDs of SARS-CoV-2, SARS-CoV-2 XBB, SARS-CoV-1, MERS-CoV, and HCoV-HKU1, with their Kd values increasing as RBD sequence similarity decreased. Molecular docking studies indicated compounds 9, 14, and 15 bound to the SARS-CoV-2 spike protein RBD and interacted with hotspot amino acid residues required for the RBD-ACE2 interaction and cellular infection. These three nitrile-containing candidates, particularly compound 15, should be considered for further development as potential pan-coronavirus entry inhibitors.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

MIDF-DMAP: Multimodal information dynamic fusion for drug molecule activity prediction

BackgroundComputer-aided drug development can alleviate limitations in drug research and development processes, such as long cycles, high costs, and limited targeting. Deep learning models can be used to predict the activity of drug compound molecules. However, existing models still need to adequately address semantic redundancy, conflicts, and noise between different data modalities. It results in low accuracy in predicting molecular activity and many false positive results that need further resolution. ObjectiveThis study aims to design a parallel deep learning model for processing multimodal molecular information fusion, dynamically adjusting the weights of different modal features during the fusion process. This approach ensures that high-quality modal information is more significant in molecular activity prediction, improving the model’s predictive accuracy. MethodsFirst, three modal features of drug molecules are extracted: one-dimensional fingerprints, two-dimensional topological structures, and three-dimensional geometric structures. Then, a dynamic weighting mechanism is proposed to fuse these three modal features organically. Finally, the fused features are input into the model classifier to obtain molecular activity prediction results. ResultsExperimental results show that the proposed model improved the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) value by 29.96% compared to the Weave model and 26.38% compared to the GC model on the BBBP dataset. Compared to the GraphMVP model and the multi-channel substructure graph model MSGG, the ROC-AUC value increased by 20.44% and 15.80%, respectively. This method also achieved good results on imbalanced samples and small molecule datasets. ConclusionThis study provides an effective multimodal dynamic fusion method for virtual drug screening, enriching computer-aided drug development theory and improving drug research and development efficiency.

MMFA-DTA: Multimodal Feature Attention Fusion Network for Drug-Target Affinity Prediction for Drug Repurposing Against SARS-CoV-2.

The continuous emergence of novel infectious diseases poses a significant threat to global public health security, necessitating the development of small-molecule inhibitors that directly target pathogens. The RNA-dependent RNA polymerase (RdRp) and main protease (Mpro) of SARS-CoV-2 have been validated as potential key antiviral drug targets for the treatment of COVID-19. However, the conventional new drug R&D cycle takes 10-15 years, failing to meet the urgent needs during epidemics. Here, we propose a general multimodal deep learning framework for drug repurposing, MMFA-DTA, to enable rapid virtual screening of known drugs and significantly improve discovery efficiency. By extracting graph topological and sequence features from both small molecules and proteins, we design attention mechanisms to achieve dynamic fusion across modalities. Results demonstrate the superior performance of MMFA-DTA in drug-target affinity prediction over several state-of-the-art baseline methods on Davis and KIBA data sets, validating the benefits of heterogeneous information integration for representation learning and interaction modeling. Further fine-tuning on COVID-19-relevant bioactivity data enhances model predictions for critical SARS-CoV-2 enzymes. Case studies screening the FDA-approved drug library successfully identify etacrynic acid as the potential lead compound against both RdRp and Mpro. Molecular dynamics simulations further confirm the stability and binding affinity of etacrynic acid to these targets. This study proves the great potential and advantages of deep learning and drug repurposing strategies in supporting antiviral drug discovery. The proposed general and rapid response computational framework holds significance for preparedness against future public health events.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discoveryprocess.

Deciphering Mutational Impacts on c-Src-HK2 Interaction in Colorectal Cancer Progression, and Identification of Potential Phytocompounds Inhibitors: A Molecular Simulation and Free Energy Calculation Approach.

Colorectal cancer (CRC) stands as the third most widespread cancer worldwide in both men and women, witnessing a concerning rise, especially in younger demographics. Abnormal activation of the Non-Receptor Tyrosine Kinase c-Src has been linked to the advancement of several human cancers, including colorectal, breast, lung, and pancreatic ones. The interaction between c-Src and Hexokinase 2 (HK2) triggers enzyme phosphorylation, significantly boosting glycolysis, and ultimately contributing to the development of CRC. The objectives of this study are to examine the influence of newly identified mutations on the interaction between c-Src and the HK2 enzyme and to discover potent phytocompounds capable of disrupting this interaction. In this study, we utilized molecular docking to check the effect of the identified mutation on the binding of c-Src with HK2. Virtual drug screening, MD simulation, and binding free energy were employed to identify potent drugs against the binding interface of c-Src and HK2. Among these mutations, six (W151C, L272P, A296S, A330D, R391H, and P434A) were observed to significantly disrupt the stability of the c-Src structure. Additionally, through molecular docking analysis, we demonstrated that the mutant forms of c-Src exhibited high binding affinities with HK2. The wildtype showed a docking score of -271.80 kcal/mol, while the mutants displayed scores of -280.77 kcal/mol, -369.01 kcal/mol, -324.41 kcal/mol, -362.18 kcal/mol, 266.77 kcal/mol, and -243.28 kcal/mol for W151C, L272P, A296S, A330D, R391H, and P434A respectively. Furthermore, we identified five lead phytocompounds showing strong potential to impede the binding of c-Src with HK2 enzyme, essential for colon cancer progression. These compounds exhibit robust bonding with c-Src with docking scores of -7.37 kcal/mol, -7.26 kcal/mol, -6.88 kcal/mol, -6.81 kcal/mol, and -6.73 kcal/mol. Moreover, these compounds demonstrate dynamic stability, structural compactness, and the lowest residual fluctuation during MD simulation. The binding free energies for the top five hits (-42.44±0.28 kcal/mol, -28.31±0.25 kcal/mol, -34.95±0.44 kcal/mol, -38.92±0.25 kcal/mol, and -30.34±0.27 kcal/mol), further affirm the strong interaction of these drugs with c-Src which might impede the cascade of events that drive the progression of colon cancer. Our findings serve as a promising foundation, paving the way for future discoveries in the fight against colorectal cancer.

BMS345541 is predicted as a repurposed drug for the treatment of TMZ-resistant Glioblastoma using target gene expression and virtual drug screening

Glioblastoma (GBM) is one of the most aggressive and fatal cancers, for which Temozolomide (TMZ) chemo drug is commonly used for its treatment. However, patients gradually develop resistance to this drug, leading to tumor relapse. In our previous study, we have identified lncRNAs that regulate chemoresistance through the competing endogenous RNA (ceRNA) mechanism. In this study, we tried to find FDA-approved drugs against the target proteins of these ceRNA networks through drug repurposing using differential gene expression profiles, which could be used to nullify the effect of lncRNAs and promote the sensitivity of TMZ in GBM. We performed molecular docking and simulation studies of predicted repurposed drugs and their targets. Among the predicted repurposed drugs, we found BMS345541 has a higher binding affinity towards its target protein - FOXG1, making it a more stable complex with FOXG1-DNA. The ADMET analysis of this drug BMS345541 shows a higher half-life and lower cytotoxicity level than other predicted repurposed drugs. Hence, we conjecture that this could be a better drug for increasing the sensitivity of TMZ for treating GBM patients.

CD64+ fibroblast-targeted vilanterol and a STING agonist augment CLDN18.2 BiTEs efficacy against pancreatic cancer by reducing desmoplasia and enriching stem-like CD8+ T cells

ObjectiveThe objective of this study is to improve the efficacy of CLDN18.2/CD3 bispecific T-cell engagers (BiTEs) as a promising immunotherapy against pancreatic ductal adenocarcinoma (PDAC).DesignHumanised hCD34+/hCD3e+, Trp53R172HKrasG12DPdx1-Cre (KPC), pancreas-specific Cldn18.2...

Identification of Cytisine Derivatives as Agonists of the Human Delta Opioid Receptor by Supercomputer-Based Virtual Drug Screening and Transcriptomics.

Delta opioid receptors (DORs) are rising as therapeutic targets, not only for the treatment of pain but also other neurological disorders (e.g., Parkinson's disease). The advantage of DOR agonists compared to μ-opioid receptor agonists is that they have fewer side effects and a lower potential to induce tolerance. However, although multiple candidates have been tested in the past few decades, none have been approved for clinical use. The current study focused on searching for new DOR agonists by screening a chemical library containing 40,000 natural and natural-derived products. The functional activity of the top molecules was evaluated in vitro through the cyclic adenosine monophosphate accumulation assay. Compound 3 showed promising results, and its activity was further investigated through transcriptomic methods. Compound 3 inhibited the expression of TNF-α, prevented NF-κB translocation to the nucleus, and activated the G-protein-mediated ERK1/2 pathway. Additionally, compound 3 is structurally different from known DOR agonists, making it a valuable candidate for further investigation for its anti-inflammatory and analgesic potential.

NUAK1-Mediated Phosphorylation of NADK Mitigates ROS Accumulation to Promote Osimertinib Resistance in Non-Small Cell Lung Carcinoma.

Osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, is approved as a first-line therapy in advanced non-small cell lung carcinoma (NSCLC) patients with EGFR-activating mutations or the T790M resistance mutation. However, the efficacy of osimertinib is limited due to acquired resistance, highlighting the need to elucidate resistance mechanisms to facilitate the development of improved treatment strategies. Here, we screened for significantly upregulated genes encoding protein kinases in osimertinib-resistant NSCLC cells and identified NUAK1 as a pivotal regulator of osimertinib resistance. NUAK1 was highly expressed in osimertinib-resistant NSCLC and promoted the emergence of osimertinib resistance. Genetic or pharmacological blockade of NUAK1 restored the sensitivity of resistant NSCLC cells to osimertinib in vitro and in vivo. Mechanistically, NUAK1 directly interacted with and phosphorylated NADK at serine 64 (S64), which mitigated osimertinib-induced accumulation of reactive oxygen species (ROS) and contributed to the acquisition of osimertinib resistance in NSCLC. Furthermore, virtual drug screening identified T21195 as an inhibitor of NADK-S64 phosphorylation, and T21195 synergized with osimertinib to reverse acquired resistance by inducing ROS accumulation. Collectively, these findings highlight the role of the NUAK1-NADK axis in governing osimertinib resistance in NSCLC and indicate the potential of targeting this axis as a strategy for circumventing resistance.

A computational workflow for analysis of missense mutations in precision oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation’s effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/.Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Network Medicine: A Potential Approach for Virtual Drug Screening.

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Anti-Inflammatory and Cancer-Preventive Potential of Chamomile (Matricaria chamomilla L.): A Comprehensive In Silico and In Vitro Study.

Chamomile tea, renowned for its exquisite taste, has been appreciated for centuries not only for its flavor but also for its myriad health benefits. In this study, we investigated the preventive potential of chamomile (Matricaria chamomilla L.) towards cancer by focusing on its anti-inflammatory activity. A virtual drug screening of 212 phytochemicals from chamomile revealed β-amyrin, β-eudesmol, β-sitosterol, apigenin, daucosterol, and myricetin as potent NF-κB inhibitors. The in silico results were verified through microscale thermophoresis, reporter cell line experiments, and flow cytometric determination of reactive oxygen species and mitochondrial membrane potential. An oncobiogram generated through comparison of 91 anticancer agents with known modes of action using the NCI tumor cell line panel revealed significant relationships of cytotoxic chamomile compounds, lupeol, and quercetin to microtubule inhibitors. This hypothesis was verified by confocal microscopy using α-tubulin-GFP-transfected U2OS cells and molecular docking of lupeol and quercetin to tubulins. Both compounds induced G2/M cell cycle arrest and necrosis rather than apoptosis. Interestingly, lupeol and quercetin were not involved in major mechanisms of resistance to established anticancer drugs (ABC transporters, TP53, or EGFR). Performing hierarchical cluster analyses of proteomic expression data of the NCI cell line panel identified two sets of 40 proteins determining sensitivity and resistance to lupeol and quercetin, further pointing to the multi-specific nature of chamomile compounds. Furthermore, lupeol, quercetin, and β-amyrin inhibited the mRNA expression of the proinflammatory cytokines IL-1β and IL6 in NF-κB reporter cells (HEK-Blue Null1). Moreover, Kaplan-Meier-based survival analyses with NF-κB as the target protein of these compounds were performed by mining the TCGA-based KM-Plotter repository with 7489 cancer patients. Renal clear cell carcinomas (grade 3, low mutational rate, low neoantigen load) were significantly associated with shorter survival of patients, indicating that these subgroups of tumors might benefit from NF-κB inhibition by chamomile compounds. This study revealed the potential of chamomile, positioning it as a promising preventive agent against inflammation and cancer. Further research and clinical studies are recommended.

Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches usually transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then use graph neural networks (GNNs) to predict its binding affinity. However, the node and edge features of the 2D graph are extracted based on invariant local coordinate systems of the 3D complex. As a result, these approaches can not fully learn the global information of the complex, such as the physical symmetry and the topological information of bonds. To address these issues, we propose a novel Equivariant Line Graph Network (ELGN) for binding affinity prediction of 3D protein-ligand complexes. The proposed ELGN firstly adds a super node to the 3D complex, and then builds a line graph based on the 3D complex. After that, ELGN uses a new E(3)-equivariant network layer to pass the messages between nodes and edges based on the global coordinate system of the 3D complex. Experimental results on two real datasets demonstrate the effectiveness of ELGN over several state-of-the-art baselines.