Structure-based Virtual Screening Methods Research Articles

Structure-based virtual screening methods for the identification of novel phytochemical inhibitors targeting furin protease for the management of COVID-19.

The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is a highly contagious respiratory disease with widespread societal impact. The symptoms range from cough, fever, and pneumonia to complications affecting various organs, including the heart, kidneys, and nervous system. Despite various ongoing efforts, no effective drug has been developed to stop the spread of the virus. Although various types of medications used to treat bacterial and viral diseases have previously been employed to treat COVID-19 patients, their side effects have also been observed. The way SARS-CoV-2 infects the human body is very specific, as its spike protein plays an important role. The S subunit of virus spike protein cleaved by human proteases, such as furin protein, is an initial and important step for its internalization into a human host. Keeping this context, we attempted to inhibit the furin using phytochemicals that could produce minimal side effects. For this, we screened 408 natural phytochemicals from various plants having antiviral properties, against furin protein, and molecular docking and dynamics simulations were performed. Based on the binding score, the top three compounds (robustaflavone, withanolide, and amentoflavone) were selected for further validation. MM/GBSA energy calculations revealed that withanolide has the lowest binding energy of -57.2 kcal/mol followed by robustaflavone and amentoflavone with a binding energy of -45.2 kcal/mol and -39.68 kcal/mol, respectively. Additionally, ADME analysis showed drug-like properties for these three lead compounds. Hence, these natural compounds robustaflavone, withanolide, and amentoflavone, may have therapeutic potential for the management of SARS-CoV-2 by targeting furin.

Identification of TonB-dependent siderophore receptor inhibitors against Flavobacterium columnare using a structure-based high-throughput virtual screening method.

TonB-dependent siderophore receptors play a critical transport role for Flavobacterium columnare virulence formation and growth, and have become valuable targets for the development of novel antimicrobial agents. Traditional Chinese medicine has demonstrated notable efficacy in the treatment of fish diseases and includes potential antibacterial agents. Herein, we performed molecular docking-based virtual screening to discover novel TonB-dependent siderophore receptor inhibitors from traditional Chinese medicine and provide information for developing novel antibacterial agents. Firstly, we efficiently obtained 11 potential inhibitors with desirable drug-like characteristics from thousands of compounds in the TCM library based on virtual screening and property prediction. The antibacterial activity of Enoxolone, along with its interaction characteristics, were determined via an MIC assay and molecular dynamic simulation. Transcriptional profiling, along with validation experiments, subsequently revealed that an insufficient uptake of iron ions by bacteria upon binding to the TonB-dependent siderophore receptors is the antibacterial mechanism of Enoxolone. Finally, Enoxolone's acceptable toxicity was illustrated through immersion experiments. In summary, we have used virtual screening techniques for the first time in the development of antimicrobial agents in aquaculture. Through this process, we have identified Enoxolone as a promising compound targeting the TonB-dependent siderophore receptor of F. columnare. In addition, our findings will provide new ideas for the advancement of innovative antimicrobial medications in aquaculture.

In silico identification of a novel Cdc2-like kinase 2 (CLK2) inhibitor in triple negative breast cancer.

Dysregulation of RNA splicing processes is intricately linked to tumorigenesis in various cancers, especially breast cancer. Cdc2-like kinase 2 (CLK2), an oncogenic RNA-splicing kinase pivotal in breast cancer, plays a significant role, particularly in the context of triple-negative breast cancer (TNBC), a subtype marked by substantial medical challenges due to its low survival rates. In this study, we employed a structure-based virtual screening (SBVS) method to identify potential CLK2 inhibitors with novel chemical structures for treating TNBC. Compound 670551 emerged as a novel CLK2 inhibitor with a 50% inhibitory concentration (IC50) value of 619.7 nM. Importantly, Compound 670551 exhibited high selectivity for CLK2 over other protein kinases. Functionally, this compound significantly reduced the survival and proliferation of TNBC cells. Results from a cell-based assay demonstrated that this inhibitor led to a decrease in RNA splicing proteins, such as SRSF4 and SRSF6, resulting in cell apoptosis. In summary, we identified a novel CLK2 inhibitor as a promising potential treatment for TNBC therapy.

Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of -9.576 kcal/mol, -8.1 kcal/mol, -7.744 kcal/mol, -7.647 kcal/mol, and - 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors.

The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.

Structure-Based Virtual Screening of Helicobacter pylori SecA Inhibitors.

The human bacterial pathogen Helicobacter pylori causes a range of gastric diseases. The killing rate of Helicobacter pylori is declining year by year because of high antibiotics resistance. It is urgent to develop new target and novel anti- Helicobacter pylori drugs. As an "energy pump" for bacterial cells, SecA is essential for bacterial growth and drives bacterial protein transmembrane transport, moreover SecA is absent in mammals, all of which nominate SecA as an attractive antimicrobial target. Here, we provided a structure-based virtual screening method to screen the 3D-diversity natural-product-like screening library against SecA for novel anti- Helicobacter pylori inhibitors with novel scaffolds. In this study, homology modeling was used to construct the three-dimensional structure of Helicobacter pylori SecA. Two rounds of molecular docking were then used to find new small-molecule inhibitors of SecA, identifying six lead candidates that maintained key interactions with the binding pocket. After that, molecular dynamics simulations were used to explore more accurate ligand-receptor binding modes in states close to natural conditions. Encouragingly, all six compounds were relatively stable during the simulation. Apart from that the binding free energy calculation based on MM/PBSA demonstrated favorable results of < -13.642 kcal/mol. Finally, ADME-T analysis indicated that these compounds were also sufficiently druggable. All six compounds can be well combined with the crystal structure, which further facilitate the development of SecA inhibitors and lead compounds against Helicobacter pylori.

Discovery of novel TRPV1 modulators through machine learning-based molecular docking and molecular similarity searching.

The transient receptor potential vanilloid 1 (TRPV1) channel belongs to the transient receptor potential channel superfamily and participates in many physiological processes. TRPV1 modulators (both agonists and antagonists) can effectively inhibit pain caused by various factors and have curative effects in various diseases, such as itch, cancer, and cardiovascular diseases. Therefore, the development of TRPV1 channel modulators is of great importance. In this study, the structure-based virtual screening and ligand-based virtual screening methods were used to screen compound databases respectively. In the structure-based virtual screening route, a full-length human TRPV1 protein was first constructed, three molecular docking methods with different precisions were performed based on the hTRPV1 structure, and a machine learning-based rescoring model by the XGBoost algorithm was constructed to enrich active compounds. In the ligand-based virtual screening route, the ROCS program was used for 3D shape similarity searching and the EON program was used for electrostatic similarity searching. Final 77 compounds were selected from two routes for in vitro assays. The results showed that 8 of them were identified as active compounds, including three hits with IC50 values close to capsazepine. In addition, one hit is a partial agonist with both agonistic and antagonistic activity. The mechanisms of some active compounds were investigated by molecular dynamics simulation, which explained their agonism or antagonism.

Orphan receptor GPR88 as a potential therapeutic target for CNS disorders – an in silico approach

The G-protein-coupled receptors are a part of the largest and most physiologically relevant family of membrane proteins. One-third of the medications, now on the market, target the GPCR receptor family, which is one of the most important therapeutic targets for many disorders. In the reported work, we have focussed on orphan GPR88 receptor which is a part of the GPCR protein family and a potential target for central nervous system disorders. GPR88 is known to show the highest expression in the striatum, which is a key region in motor control and cognitive functions. Recent studies have reported that GPR88 is activated by two agonists, 2-PCCA and RTI-13951-33. In this study, we have predicted the three-dimensional protein structure for the orphan GPR88 using the homology modeling approach. We then used shape-based screening techniques based on known agonists and structure-based virtual screening methods employing docking to uncover novel GPR88 ligands. The screened GPR88-ligand complexes were further subjected to molecular dynamics simulation studies. The selected ligands could fasten the development of novel treatments for the vast list of movement and central nervous system disorders. Communicated by Ramaswamy H. Sarma

Exploring the potential of phytochemicals as inhibitors of 3'-phosphoadenosine 5'-phosphosulfate synthase 1 targeting cancer therapy

3'-phosphoadenosine 5'-phosphosulfate synthase 1 (PAPSS1) is an enzyme that critically synthesises the biologically active form of sulfate (PAPS) for all sulfation reactions. The discovery of PAPSS1 as a possible drug target for cancer therapy, specifically in non-small cell lung cancer, has prompted us to investigate potential small-molecule inhibitors of PAPSS1. Here, a structure-based virtual screening method was used to search for phytochemicals in the IMPPAT database to find potential inhibitors of PAPSS1. The primary hits were selected based on their physicochemical, ADMET, and drug-like properties. Then, the binding affinities were calculated and analyzed the interactions to identify safer and more effective hits. The research identified two phytochemicals, Guggulsterone and Corylin, that exhibited significant affinity and specific interaction with the ATP-binding pocket of PAPSS1. Structural observations made by molecular docking were further accompanied by molecular dynamics (MD) simulations and principal component analysis (PCA) to examine the conformational changes and stability of PAPSS1 with the elucidated compounds Guggulsterone and Corylin. MD simulation results suggested that the binding of Guggulsterone and Corylin stabilizes the PAPSS1 structure, leading to fewer conformational changes. This implies that these compounds may be useful in developing PAPSS1 inhibitors for the therapeutic development against non-small cell lung cancer (NSCLC). This study highlights the potential of phytochemicals as PAPSS1 inhibitors and the utility of computational approaches in drug discovery. Communicated by Ramaswamy H. Sarma

Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening.

Molecular docking, a structure-based virtual screening method, is a reliable tool to enrich potential bioactive molecules from molecular databases. With the rapid expansion of compound library sizes, the speed of existing molecular docking programs becomes less than adequate to meet the demand for screening ultralarge libraries containing tens of millions or billions of molecules. Here, we propose Uni-Dock, a GPU-accelerated molecular docking program that supports various scoring functions including vina, vinardo, and ad4. Uni-Dock achieves more than 1000-fold speedup with high accuracy compared with the AutoDock Vina running in single CPU core, outperforming reported GPU-accelerated docking programs including AutoDock-GPU and Vina-GPU based on head-to-head experiments. Uni-Dock docks molecules in batches simultaneously using concurrent threads of each molecule. The data flow between GPU and CPU is optimized to eliminate CPU hotspots and maximize GPU utility. Additionally, Uni-Dock also supports hydrogen bond biased docking for all scoring functions and can be migrated to multiple GPUs of different architectures and manufacturers. We analyzed the improved performance of Uni-Dock on the CASF-2016 and DUD-E datasets and recommend three combinations of hyperparameters corresponding to different docking scenarios. To demonstrate Uni-Dock's capability on routinely screening ultralarge libraries, we performed hierarchical virtual screening experiments with Uni-Dock on the Enamine Diverse REAL druglike set containing 38.2 million molecules to a popular target KRAS G12D in 12 h using 100 NVIDIA V100 GPUs. To the best of our knowledge, Uni-Dock should be the fastest GPU-accelerated docking program to date.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking.

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

Alleviation of Hepatic Steatosis by 4-azidophlorizin via the Degradation of Geranylgeranyl Diphosphate Synthase by the Ubiquitin-Proteasome Pathway in Vivo and in Vitro.

There are no known approved pharmacotherapies for non-alcoholic fatty liver disease (NAFLD) in the clinical setting. Although studies have provided substantial evidence that geranylgeranyl diphosphate synthase (GGPPS) is a potential therapeutic target for the treatment of NAFLD corresponding drug screening is rare. A GGPPS-targeted inhibitor is identified using a structure-based virtual small molecule screening method. The interaction of 4-AZ and GGPPS is detected by microscale thermophoresis. 4-AZ degradation of GGPPS by the ubiquitin-proteasome pathway is detected by western blotting. The anti-steatotic effect of 4-AZ in vivo is detected by CT. Lipid-related gene detection is detected by real-time PCR both in primary hepatocytes and mice. The compound inhibits the accumulation of lipids in primary hepatocytes and decreases lipogenic gene expression through GGPPS. Pharmacological studies show that 4-AZ can attenuate hepatic steatosis and improve liver injury in high-fat diet-induced mice. This data provides a novel application of 4-AZ NAFLD therapy, proving that the inhibition of GGPPS is a novel strategy for the treatment of NAFLD.

Virtual Screening Based on Ligand and Structure with in vitro Assessment of Neolignans against Trypanosoma cruzi

Chagas disease, caused by the parasite Trypanosoma cruzi, occurs most commonly in Latin America. As the treatment is highly toxic and ineffective in the chronic phase of the disease, alternative treatments are needed. Through quantitative structure-activity relationship modeling (QSAR) analysis using ligand-based and structure-based virtual screening methods, we predicted the trypanocidal potential of 47 neolignans against three targets, the enzymes cruzain, trypanothione reductase, and sterol 14-alpha demethylase. A combined analysis allowed for the selection of potent inhibitors against Trypanosoma cruzi. Of these compounds, two were isolated and shown to inhibit the growth of epimastigotes at concentrations of 9.64 and 8.72 μM, and trypomastigote forms at 4.88 and 2.73 μM. Therefore, the compounds (2R, 3R)-2,3-dihydro-2‑(4‑methoxyphenyl)- 3-methyl-5-(E)-propenylbenzofuran (46) and ottomentosa (47) may be a good option of growth inhibitors for the parasite stages and warrant additional study.

Structure-based virtual screening for novel p38 MAPK inhibitors and a biological evaluation

Mitogen-activated protein kinases (MAPKs) are a group of serine-threonine protein kinases that can be activated by extracellular stimuli. MAPK14 (p38α) affects major disease processes, while inhibition of p38α has been shown to have potential therapeutic effects. Many inhibitors targeting p38α have entered clinical trials but have a long development cycle and severe side effects. We developed a multi-step receptor structure-based virtual screening method to screen potential bioactive molecules from SPECS and our MCDB libraries. Compound 10 was identified as a promising p38α inhibitor that may be used in the treatment of p38αMAPK pathway-related diseases, but corollary studies are warranted.

Structure-Based Discovery and Biological Assays of a Novel PRMT5 Inhibitor for Non-Small Cell Lung Cancer.

Protein arginine methyltransferase 5 (PRMT5) is a popular anticancer target that regulates histone or nonhistone methylation and is linked to the development and poor prognosis of non-small cell lung cancer. PRMT5 inhibitors have shown great promise in clinical trials as a cancer therapy. However, most inhibitors reported recently act in a SAM-competitive mode and lack structural diversity. In this paper, a novel non-SAM inhibitor, 3039-0164, was discovered by the structure-based virtual screening method. The binding mechanism of 3039-0164 to PRMT5 was revealed via molecular docking and molecular dynamics simulations. 3039-0164 inhibited PRMT5 enzymatic activity, downregulated the expression of PRMT5 downstream target genes (FGFR3 and eIF4E), and blocked the activation of the PI3K/AKT/mTOR and ERK signaling pathways. The discovery of 3039-0164 provides precise and creative hit compounds for the design optimization of PRMT5 lead compounds in non-small cell lung cancer.

Identification of compelling inhibitors of human norovirus 3CL protease to combat gastroenteritis: A structure-based virtual screening and molecular dynamics study.

Human noroviruses (NV) are the most prevalent cause of sporadic and pandemic acute gastroenteritis. NV infections cause substantial morbidity and death globally, especially amongst the aged, immunocompromised individuals, and children. There are presently no authorized NV vaccines, small-molecule therapies, or prophylactics for humans. NV 3 C L protease (3CLP) has been identified as a promising therapeutic target for anti-NV drug development. Herein, we employed a structure-based virtual screening method to screen a library of 700 antiviral compounds against the active site residues of 3CLP. We report three compounds, Sorafenib, YM201636, and LDC4297, that were revealed to have a higher binding energy (BE) value with 3CLP than the control (Dipeptidyl inhibitor 7) following a sequential screening, in-depth molecular docking and visualization, physicochemical and pharmacological property analysis, and molecular dynamics (MD) study. Sorafenib, YM201636, and LDC4297 had BEs of -11.67, -10.34, and -9.78 kcal/mol with 3CLP, respectively, while control had a BE of -6.38 kcal/mol. Furthermore, MD simulations of the two best compounds and control were used to further optimize the interactions, and a 100 ns MD simulation revealed that they form stable complexes with 3CLP. The estimated physicochemical, drug-like, and ADMET properties of these hits suggest that they might be employed as 3CLP inhibitors in the management of gastroenteritis. However, wet lab tests are a prerequisite to optimize them as NV 3CLP inhibitors.

Docking-Based Virtual Screening for the Discovery of 1,3,4-Oxadiazoles as Aminoacyl-tRNA Synthetase Inhibitors

Aminoacyl-tRNA synthetases (aaRSs) are one of the leading targets for the development of antibiotic agents. In this paper, we reported the discovery of aaRS inhibitors using a structure-based virtual screening method. The interactions of 52 designed structures with the methionyl-tRNA synthetase (MetRS) target were performed by docking the ligands into the active zone of the MetRS using Autodock Vina. The data revealed 14 compounds displaying interactions with key amino acids (Asp287, Tyr250, Val473, Trp474, Phe522, Ile519, Ala477, Leu478, and His523) at the binding pocket of the enzyme, indicating their potential as MetRS inhibitors. These results could be served as the references for further synthetic work and bioassays experiments for discovering MetRS inhibitors and other pharmaceutical agents that may assist in the generation of new antibiotics.

It all clicks together: In silico drug discovery becoming mainstream

It all clicks together: In silico drug discovery becoming mainstream

Discovering inhibitors of TEAD palmitate binding pocket through virtual screening and molecular dynamics simulation

Transcriptional enhanced associate domain (TEAD) proteins bind to YAP/TAZ and mediate YAP/TAZ-induced gene expression. TEADs are not only the key transcription factors and final effector of the Hippo signaling pathway, but also the proteins that regulate cell proliferation and apoptosis. Disorders of Hippo signaling pathway occur in liver cancer, breast cancer, colon cancer and other cancers. S-palmitylation can stabilize the structure of TEADs and is also a necessary condition for the binding of TEADs to YAP/TAZ. The absence of TEAD palmitoylation prevents TEADs from binding to chromatin, thereby inhibiting the transcription and expression of downstream target genes in the Hippo pathway through a dominant-negative mechanism. Therefore, disrupting the S-palmitylation of TEADs has become an attractive and very feasible method in cancer treatment. The palmitate binding pockets of TEADs are conservative, and the crystal structures of TEAD2-palmitoylation inhibitor complexes and the potential TEAD2 inhibitors are more than other TEADs, TEAD2 can be selected to be the target receptor. In this study, structure-based and ligand-based virtual screening, molecular dynamics simulations, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) calculations, residue decomposition binding energy calculations, and ADME predictions have been performed to discover 11 potential TEAD2 S-palmitylation inhibitors. ChEBML196567 and ZINC000013942794 are the most recommended, because they formed strong binding energies and stable hydrogen bonds with TEAD2 and have good drugbility and high human oral absorption. We found that it was easier to find the targeting small molecules using a combination of structure-based and ligand-based virtual screening methods. Besides, a new core structure has been found in the selected small molecules. In addition, we analyzed the binding modes of these small molecules to TEAD2, and confirmed the hot spot residues Cys380, Ser345, Tyr426, Phe428, Ile408, and Met379. Availability of data and materialSupplementary materials are available online.