Virtual Drug Screening Research Articles

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs.

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

Molecular docking-based computational platform for high-throughput virtual screening.

Structure-based virtual screening is a key, routine computational method in computer-aided drug design. Such screening can be used to identify potentially highly active compounds, to speed up the progress of novel drug design. Molecular docking-based virtual screening can help find active compounds from large ligand databases by identifying the binding affinities between receptors and ligands. In this study, we analyzed the challenges of virtual screening, with the aim of identifying highly active compounds faster and more easily than is generally possible. We discuss the accuracy and speed of molecular docking software and the strategy of high-throughput molecular docking calculation, and we focus on current challenges and our solutions to these challenges of ultra-large-scale virtual screening. The development of Web services helps lower the barrier to drug virtual screening. We introduced some related web sites for docking and virtual screening, focusing on the development of pre- and post-processing interactive visualization and large-scale computing.

Disruption of Lipid Raft Microdomains, Regulation of CD38, TP53, and MYC Signaling, and Induction of Apoptosis by Lomitapide in Multiple Myeloma Cells

Multiple myeloma (MM) is characterized by accumulation of a malignant clone of plasma cells in the bone marrow. Curative treatments are not yet available. Therefore, we undertook a drug repurposing approach to identify possible candidates from a chemical library of 1,230 FDA-approved drugs by virtual drug screening. As a target, we have chosen the non-receptor Bruton's tyrosine kinase (BTK) which is one of the main regulators of the MM biomarker CD38. In silico virtual screening was performed by using PyRx. Flow cytometry was applied for cell cycle and apoptosis analysis. Furthermore, protein and gene expression was determined by western blotting and microarray hybridization. Lipid raft staining was observed by confocal microscopy. The in silico identified lipid-lowering lomitapide presented with the strongest cytotoxicity among the top 10 drug candidates. This drug arrested the cell cycle in the G2/M phase and induced apoptosis in MM cells. Western blot analyses revealed that treatment with lomitapide induced cleavage of the apoptosis regulator PARP and reduced the expression of CD38, an integral part of lipid rafts. Using confocal microscopy, we further observed that lipid raft microdomain formation in MM cells was inhibited by lomitapide. In four MM cell lines (KMS-12-BM, NCI-H929, RPMI-8226, and MOLP-8) treated with lomitapide, microarray analyses showed not only that the expression of CD38 and BTK was down-regulated, but also that the tumor suppressor gene TP53 and the oncogene c-MYC were among the top deregulated genes. Further analysis of these data by Ingenuity pathway analysis (IPA) suggested that lomitapide interferes with the cross-talk of CD38 and BTK and apoptosis-regulating genes via TP53 and c-MYC. Lomitapide treatment led to disruption of lipid raft domains and induction of pro-apoptotic factors and might, therefore, be considered as a potential therapeutic agent in MM.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

Energy-based virtual screening of drugs documented for schizophrenia against DRD2 and HTR2A

Energy-based virtual screening of drugs documented for schizophrenia against DRD2 and HTR2A

Energy-based virtual screening of drugs documented for schizophrenia against DRD2 and HTR2A

Schizophrenia is the most commonly known mental disorder with the number of reported cases increasing very fast. Drugs available for the disorder are unable to cure the disease completely and can o...

Machine learning and graph neural network for finding potential drugs related to multiple myeloma

An innovative voting mechanism for virtual drug screening.

In Silico Study of Pubchem Compounds for Solanum torvum as Antiviral Agent against SARS-CoV-2

Background: The recent epidemic outbreak of a novel coronavirus called SARS-CoV-2 has caused suffering among many people in the form of respiratory tract infection. Currently, there are no targeted drugs, and effective treatment options remain limited. Objective: In order to rapidly discover new compounds for clinical purposes, in silico drug design and virtual drug screening have been initiated to identify new drug leads that target the main protease of the COVID-19 virus. Mpro is a key CoV enzyme, which plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for this virus. Methods: The present study was done to investigate the PubChem compounds of an ayurvedic herb Solanum torvum as an effective antiviral agent against COVID-19. The PubChem compounds like Torvoside H, Torvoside A, Torvoside E, Torvoside F, Torvonin A, 2,3,4-trimethyltriacontane, Torvanol A Q27134802, 5-hexatriacontanone, Jurubine, Tritriacontan-3-one, Torvanol A, Chlorogenone Spirostane-3,6-dione of Solanum torvum were downloaded from NCBI PubChem database acting as ligands for protein ligand docking. The 3D structure of the viral MPro (PDB ID: 6yb7) was retrieved from the RCSB PDB database. The active sites and binding sites were analyzed, and Docking molecular simulations were realized among a total of 12 ligands against COVID-19. Results: The PubChem compounds from the fruits of Solanum torvum showed good docking score and protein-ligand interaction, indicating that the PubChem compounds can cure the COVID-19 disease and act as an effective antiviral agent. Conclusion: Most of the PubChem compounds in the fruits of Solanum torvum showed better paramagnetic parameters.

Interaction of Nootropic Drugs with Neurotransmitter Enzyme Acetylcholinesterase: An Insilico Approach: A Review

Nootropic drugs are the class of drugs or supplements that are claimed to enhance cognitive functions, specifically executive functions, memory and creativity in healthy individual. They are sometime referred as cognitive enhancers or smart drugs as they are associated with memory improvement functioning. Some of them are well known drugs and clinically approved by the Food and Drug Administration (FDA). All metabolic reactions are purely dependent on enzymatic actions as they play a very important role in regulating and maintaining most of the biological responses and various processes. An enzyme Acetylcholinesterase (AChE) seems to play an essential role in the conduction of cholinergic brain synapses and neuromuscular junctions. There have been different nootropic drugs identified and approved for curing neurodegenerative disorders such as Alzheimer, Parkinson and Huntington's disease. Their binding efficiency and energy have been well studied an established by using the in-silico docking tools. There are different docking tools available today for analysis of molecules such as PyRx, Auto dock and schrodinger suite. The advent of these tools is being widely used by the pharmaceutical industries for the virtual screening of the formulated drugs against the desired target molecule. It has made the drug formulation process more time efficient and cost effective. Thus, an in-silico approach has been widely accepted for drug discovery and its design.

Trends in Deep Learning for Property-driven Drug Design.

It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the development of large-scale multimodal predictive models for virtual drug screening. Recently, deep generative models have emerged as a powerful tool to explore the chemical space and raise hopes to expedite the drug discovery process. Following this progress in chemocentric approaches for generative chemistry, the next challenge is to build multimodal conditional generative models that leverage disparate knowledge sources when mapping biochemical properties to target structures. Here, we call the community to bridge drug discovery more closely with systems biology when designing deep generative models. Complementing the plethora of reviews on the role of DL in chemoinformatics, we specifically focus on the interface of predictive and generative modelling for drug discovery. Through a systematic publication keyword search on PubMed and a selection of preprint servers (arXiv, biorXiv, chemRxiv, and medRxiv), we quantify trends in the field and find that molecular graphs and VAEs have become the most widely adopted molecular representations and architectures in generative models, respectively. We discuss progress on DL for toxicity, drug-target affinity, and drug sensitivity prediction and specifically focus on conditional molecular generative models that encompass multimodal prediction models. Moreover, we outline future prospects in the field and identify challenges such as the integration of deep learning systems into experimental workflows in a closed-loop manner or the adoption of federated machine learning techniques to overcome data sharing barriers. Other challenges include, but are not limited to interpretability in generative models, more sophisticated metrics for the evaluation of molecular generative models, and, following up on that, community-accepted benchmarks for both multimodal drug property prediction and property-driven molecular design.

In Silico Characterization and Virtual Screening of GntR/HutC Family Transcriptional Regulator MoyR: A Potential Monooxygenase Regulator in Mycobacterium tuberculosis.

Simple SummaryIn an era where the world faces new diseases and pathogens, another emerging challenge is neglected pathogens becoming more notorious. Transcriptional regulators play a vital role in the pathogenesis and survival of these pathogens. Hence, characterizing transcriptional regulators, either in vitro or in silico, is of great importance. Here, we present the first structural characterization of a GntR/HutC regulator in Mycobacterium tuberculosis via in silico methods. We have suggested its possible role and potential as a drug target as well as identified possible drug leads that can be used for further improvements.Mycobacterium tuberculosis is a well-known pathogen due to the emergence of drug resistance associated with it, where transcriptional regulators play a key role in infection, colonization and persistence. The genome of M. tuberculosis encodes many transcriptional regulators, and here we report an in-depth in silico characterization of a GntR regulator: MoyR, a possible monooxygenase regulator. Homology modelling provided a reliable structure for MoyR, showing homology with a HutC regulator DasR from Streptomyces coelicolor. In silico physicochemical analysis revealed that MoyR is a cytoplasmic protein with higher thermal stability and higher pI. Four highly probable binding pockets were determined in MoyR and the druggability was higher in the orthosteric binding site consisting of three conserved critical residues: TYR179, ARG223 and GLU234. Two highly conserved leucine residues were identified in the effector-binding region of MoyR and other HutC homologues, suggesting that these two residues can be crucial for structure stability and oligomerization. Virtual screening of drug leads resulted in four drug-like compounds with greater affinity to MoyR with potential inhibitory effects for MoyR. Our findings support that this regulator protein can be valuable as a therapeutic target that can be used for developing drug leads.

An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2.

The TIPE2 (tumor necrosis factor-alpha-induced protein 8-like 2) protein is a major regulator of cancer and inflammatory diseases. The availability of its sequence and structure, as well as the critical amino acids involved in its ligand binding, provides insights into its function and helps greatly identify novel drug candidates against TIPE2 protein. With the current advances in deep learning and molecular dynamics simulation-based drug screening, large-scale exploration of inhibitory candidates for TIPE2 becomes possible. In this work, we apply deep learning-based methods to perform a preliminary screening against TIPE2 over several commercially available compound datasets. Then, we carried a fine screening by molecular dynamics simulations, followed by metadynamics simulations. Finally, four compounds were selected for experimental validation from 64 candidates obtained from the screening. With surprising accuracy, three compounds out of four can bind to TIPE2. Among them, UM-164 exhibited the strongest binding affinity of 4.97 µM and was able to interfere with the binding of TIPE2 and PIP2 according to competitive bio-layer interferometry (BLI), which indicates that UM-164 is a potential inhibitor against TIPE2 function. The work demonstrates the feasibility of incorporating deep learning and MD simulation in virtual drug screening and provides high potential inhibitors against TIPE2 for drug development.

Target-based drug repurposing against Candida albicans-A computational modeling, docking, and molecular dynamic simulations study.

The emergence of multidrug-resistant strains of Candida albicans has become a global threat mostly due to co-infection with immune-compromised patients leading to invasive candidiasis. The life-threatening form of the disease can be managed quickly and effectively by drug repurposing. Thus, the study used in silico approaches to evaluate Food and Drug Administration (FDA) approved drugs against three drug targets-TRR1, TOM40, and YHB1. The tertiary structures of three drug targets were modeled, refined, and evaluated for their structural integrity based on PROCHECK, ERRAT, and PROSA. High-throughput virtual screening of FDA-approved drugs (8815), interaction analysis, and energy profiles had revealed that DB01102 (Arbutamine), DB01611 (Hydroxychloroquine), and DB09319 (Carindacillin) exhibited better binding affinity with TRR1, TOM40, and YHB1, respectively. Notably, the molecular dynamic simulation explored that Gln45, Thr119, and Asp288 of TRR1; Thr107 and Ser121 of TOM40; Arg193, Glu213, and Ser228 of YHB1 are crucial residues for stable drug-target interaction. Additionally, it also prioritized Arbutamine-TRR1 as the best drug-target complex based on MM-PBSA (-52.72 kcal/mol), RMSD (2.43 Å), and radius of gyration (-21.49 Å) analysis. In-depth, PCA results supported the findings of molecular dynamic simulations. Interestingly, the conserved region (>70%) among the TRR1 sequences from pathogenic Candida species indicated the effectiveness of Arbutamine against multiple species of Candida as well. Thus, the study dispenses new insight and enriches the understanding of developing an advanced technique to consider potential antifungals against C. albicans. Nonetheless, a detailed experimental validation is needed to investigate the efficacy of Arbutamin against life-threatening candidiasis.

Identification of Novel Anthracycline Resistance Genes and Their Inhibitors.

Differentially expressed genes have been previously identified by us in multidrug-resistant tumor cells mainly resistant to doxorubicin. In the present study, we exemplarily focused on some of these genes to investigate their causative relationship with drug resistance. HMOX1, NEIL2, and PRKCA were overexpressed by lentiviral-plasmid-based transfection of HEK293 cells. An in silico drug repurposing approach was applied using virtual screening and molecular docking of FDA-approved drugs to identify inhibitors of these new drug-resistant genes. Overexpression of the selected genes conferred resistance to doxorubicin and daunorubicin but not to vincristine, docetaxel, and cisplatin, indicating the involvement of these genes in resistance to anthracyclines but not to a broader MDR phenotype. Using virtual drug screening and molecular docking analyses, we identified FDA-approved compounds (conivaptan, bexarotene, and desloratadine) that were interacting with HMOX1 and PRKCA at even stronger binding affinities than 1-(adamantan-1-yl)-2-(1H-imidazol-1-yl)ethenone and ellagic acid as known inhibitors of HMOX1 and PRKCA, respectively. Conivaptan treatment increased doxorubicin sensitivity of both HMOX1- and PRKCA-transfected cell lines. Bexarotene treatment had a comparable doxorubicin-sensitizing effect in HMOX1-transfected cells and desloratadine in PRKCA-transfected cells. Novel drug resistance mechanisms independent of ABC transporters have been identified that contribute to anthracycline resistance in MDR cells.

RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases.

Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.

Repurposing drug molecule against SARS-Cov-2 (COVID-19) through molecular docking and dynamics: a quick approach to pick FDA-approved drugs.

A novel coronavirus known as severe acute respiratory syndrome is rapidly spreading worldwide. The international health authorities are putting all their efforts on quick diagnosis and placing the patients in quarantine. Although different vaccines have come for quick use as prophylactics, drug repurposing seems to be of paramount importance because of inefficient therapeutic options and clinical trial limitations. Here, we used structure-based drug designing approach to find and check the efficacy of the possible drug that can inhibit coronavirus main protease which is involved in polypeptide processing to functional protein. We performed virtual screening, molecular docking and molecular dynamics simulations of the FDA-approved drugs against the main protease of SARS-CoV-2. Using well-defined computational methods, we identified amprenavir, cefoperazone, riboflavin, diosmin, nadide and troxerutin approved for human therapeutic uses, as COVID-19 main protease inhibitors. These drugs bind to the SARS-CoV-2 main protease conserved residues of substrate-binding pocket and formed a remarkable number of non-covalent interactions. We have found diosmin as an inhibitor which binds covalently to the COVID-19 main protease. This study provides enough evidences for therapeutic use of these drugs in controlling COVID-19 after experimental validation and clinical demonstration.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00894-021-04923-w.

Molecular mechanism of ATP and RNA binding to Zika virus NS3 helicase and identification of repurposed drugs using molecular dynamics simulations

Congenital Zika virus syndrome has caused a public health emergency of international concern. So far, there are no drugs available to prevent or treat the infection caused by Zika virus. The Zika virus NS3 helicase is a potential protein target for drug discovery due to its vital role in viral genome replication. NS3 helicase unwinds the viral RNA to enable the reproduction of the viral genome by the NS5 protein. NS3 helicase has two crucial binding sites; the ATP binding site and the RNA binding site. We used molecular docking and molecular dynamics (MD) simulations to study the structural behavior of Zika virus NS3 helicase in its apo form and in the presence of ATP, single-stranded RNA, and both ATP-RNA to understand their potential implications in NS3 helicase activity. Further, we have carried out virtual screening of FDA approved drugs, followed by molecular docking to identify the ATP-competitive hit molecules as probable Zika virus NS3 helicase inhibitors. The MD simulations trajectories were analyzed using normal mode analysis and principal component analysis that reveals fluctuations in the R-loop. These findings aid in understanding the molecular mechanisms of the simultaneous binding of ATP and RNA, and guide the design and discovery of new inhibitors of the Zika virus NS3 helicase as a promising drug target to treat the Zika virus infection. Communicated by Ramaswamy H. Sarma

Binary-QSAR guided virtual screening of FDA approved drugs and compounds in clinical investigation against SARS-CoV-2 main protease.

With the emergence of the new SARS-CoV-2 virus, drug repurposing studies have gained substantial importance. Combined with the efficacy of recent improvements in ligand- and target-based virtual screening approaches, virtual screening has become faster and more productive than ever. In the current study, an FDA library of approved drugs and compounds under clinical investigation were screened for their antiviral activity using the antiviral therapeutic activity binary QSAR model of the MetaCore/MetaDrug platform. Among 6733-compound collection, we found 370 compounds with a normalized therapeutic activity value greater than a cutoff of 0.75. Only these selected compounds were used for molecular docking studies against the SARS-CoV-2 main protease (Mpro). After initial short (10 ns) molecular dynamics (MD) simulations with the top-50 docking scored compounds and following molecular mechanics generalized born surface area (MM/GBSA) calculations, top-10 compounds were subjected to longer (100 ns) MD simulations and end-point MM/GBSA estimations. Our virtual screening protocol yielded Cefuroxime pivoxetil, an ester prodrug of second-generation cephalosporin antibiotic Cefuroxime, as being a considerable molecule for drug repurposing against the SARS-CoV-2 Mpro.

Rotational Variance-Based Data Augmentation in 3D Graph Convolutional Network.

This work proposes the data augmentation by molecular rotation, with consideration that the protein-ligand binding events are rotation-variant. As a proof-of-concept, known active (i. e., 1-labeled) ligands to human β-secretase 1 (BACE-1) are rotated for the generation of 0-labeled data, and the rotation-dependent prediction accuracy of 3D graph convolutional network (3DGCN) is investigated after data augmentation. The data augmentation makes the orientation-recognizing ability of 3DGCN improved significantly in the classification task for BACE-1/ligand binding. Furthermore, the data-augmented 3DGCN has a capability for predicting active ligands from a candidate dataset, via improved performance of orientation recognition, which would be applied to virtual drug screening and discovery.

Computational Screening of FDA Approved Drugs from ZINC Database for Potential Inhibitors of Zika Virus NS2B/NS3 Protease: A Molecular Docking and Dynamics Simulation Study

Zika virus is a mosquito borne pathogen with a single strand RNA genome. Human infection with this virus is usually asymptomatic, however outbreaks reported in both Pacific region and Latin America have been associated with increase in frequency of microcephaly in newborns and fetuses of infected mothers. Also, the incidence of Guillain-Barré syndrome had also increased among adults with Zika virus infection. Currently, neither vaccine nor antiviral drug has been developed against Zika virus. Structure based virtual screening can be employed, through drug repurposing strategy, to accelerate the identification of potential anti-Zika virus candidates. As such, virtual screening of approved drugs against Zika virus NS2B/NS3 protease can help to recognize new hits capable of hindering viral ability to replicate and evade immune system of the host. In this computational study, we have screened 1615 FDA approved drugs against NS2B/NS3 protease enzyme of Zika virus by using both molecular docking and dynamics simulation. Our virtual screening results indicate that the anti-muscarinic agent Darifenacin and the anti-diarrheal agent Loperamide may have a promising capacity to inhibit Zika virus NS2B/NS3 protease. According to molecular docking and dynamics simulation, these two approved drugs have good binding capacity to NS2B/NS3 as reported by docking energy of binding and MM-PBSA binding energy. In addition, both Darifenacin and Loperamide were able to maintain close proximity to protease crystal throughout simulation period. However, invitro evaluation of these two drugs against Zika virus NS2B/NS3 protease is required to confirm these computational results.