Docking-based inverse virtual screening: methods, applications, and challenges

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Spiro-Acridine Compound as a Pteridine Reductase 1 Inhibitor: in silico Target Fishing and in vitro Studies

Among the many neglected tropical diseases, leishmaniasis ranks second in mortality rate and prevalence. In a previous study, acridine derivatives were synthesized and tested for their antileishmanial activity against L. chagasi. The most active compound identified in that study (1) showed a single digit IC50 value against the parasite (1.10 μg/mL), but its macromolecular target remained unknown. Aiming to overcome this limitation, this work exploited inverse virtual screening to identify compound 1's putative molecular mechanism of action. In vitro assays confirmed that compound 1 binds to Leishmania chagasi pteridine reductase 1 (LcPTR1), with moderate affinity (Kd=33,1 μM), according to differential scanning fluorimetry assay. Molecular dynamics simulations confirm the stability of LcPTR1-compound 1 complex, supporting a competitive mechanism of action. Therefore, the workflow presented in this work successfully identified PTR1 as a macromolecular target for compound 1, allowing the designing of novel potent antileishmanial compounds.

Efficient Biodegradation of the Neonicotinoid Insecticide Flonicamid by Pseudaminobacter salicylatoxidans CGMCC 1.17248: Kinetics, Pathways, and Enzyme Properties

Nitrile-containing insecticides can be converted into their amide derivatives by Pseudaminobacter salicylatoxidans. N-(4-trifluoromethylnicotinoyl) glycinamide (TFNG-AM) is converted to 4-(trifluoromethyl) nicotinoyl glycine (TFNG) using nitrile hydratase/amidase. However, the amidase that catalyzes this bioconversion has not yet been fully elucidated. In this study, it was discovered that flonicamid (FLO) is degraded by P. salicylatoxidans into the acid metabolite TFNG via the intermediate TFNG-AM. A half-life of 18.7 h was observed for P. salicylatoxidans resting cells, which transformed 82.8% of the available FLO in 48 h. The resulting amide metabolite, TFNG-AM, was almost all converted to TFNG within 19 d. A novel amidase-encoding gene was cloned and overexpressed in Escherichia coli. The enzyme, PmsiA, hydrolyzed TFNG-AM to TFNG. Despite being categorized as a member of the amidase signature enzyme superfamily, PsmiA only shares 20-30% identity with the 14 previously identified members of this family, indicating that PsmiA represents a novel class of enzyme. Homology structural modeling and molecular docking analyses suggested that key residues Glu247 and Met242 may significantly impact the catalytic activity of PsmiA. This study contributes to our understanding of the biodegradation process of nitrile-containing insecticides and the relationship between the structure and function of metabolic enzymes.

MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Molecular docking and simulation studies of Chloroquine, Rimantadine and CAP-1 as potential repurposed antivirals for decapod iridescent virus 1 (DIV1)

Drug repurposing is a methodology of identifying new therapeutic use for existing drugs. It is a highly efficient, time and cost-saving strategy that offers an alternative approach to the traditional drug discovery process. Past in-silico studies involving molecular docking have been successful in identifying potential repurposed drugs for the various treatment of diseases including aquaculture diseases. The emerging shrimp hemocyte iridescent virus (SHIV) or Decapod iridescent virus 1 (DIV1) is a viral pathogen that causes severe disease and high mortality (80 %) in farmed shrimps caused serious economic losses and presents a new threat to the shrimp farming industry. Therefore, effective antiviral drugs are critically needed to control DIV1 infections. The aim of this study is to investigate the interaction of potential existing antiviral drugs, Chloroquine, Rimantadine, and CAP-1 with DIV1 major capsid protein (MCP) with the intention of exploring the potential of drug repurposing. The interaction of the DIV1 MCP and three antivirals were characterised and analysed using molecular docking and molecular dynamics simulation. The results showed that CAP-1 is a more promising candidate against DIV1 with the lowest binding energy of -8.46 kcal/mol and is more stable compared to others. We speculate that CAP-1 binding may induce the conformational changes in the DIV1 MCP structure by phosphorylating multiple residues (His123, Tyr162, and Thr395) and ultimately block the viral assembly and maturation of DIV1 MCP. To the best of our knowledge, this is the first report regarding the structural characterisation of DIV1 MCP docked with repurposing drugs.

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

Molecular dynamics articulated multilevel virtual screening protocol to discover novel dual PPAR α/γ agonists for anti-diabetic and metabolic applications

PPARα and PPARγ are isoforms of the nuclear receptor superfamily which regulate glucose and lipid metabolism. Activation of PPARα and PPARγ receptors by exogenous ligands could transactivate the expression of PPARα and PPARγ-dependent genes, and thereby, metabolic pathways get triggered, which are helpful to ameliorate treatment for the type 2 diabetes mellitus, and related metabolic complications. Herein, by understanding the structural requirements for ligands to activate PPARα and PPARγ proteins, we developed a multilevel in silico-based virtual screening protocol to identify novel chemical scaffolds and further design and synthesize two distinct series of glitazone derivatives with advantages over the classical PPARα and PPARγ agonists. Moreover, the synthesized compounds were biologically evaluated for PPARα and PPARγ transactivation potency from nuclear extracts of 3T3-L1 cell. Furthermore, glucose uptake assay on L6 cells confirmed the potency of the synthesized compounds toward glucose regulation. Percentage lipid-lowering potency was also assessed through triglyceride estimate from 3T3-L1 cell extracts. Results suggested the ligand binding mode was in orthosteric fashion as similar to classical agonists. Thus molecular docking and molecular dynamics (MD) simulation experiments were executed to validate our hypothesis on mode of ligands binding and protein complex stability. Altogether, the present study developed a newer protocol for virtual screening and enables to design of novel glitazones for activation of PPARα and PPARγ-mediated pathways. Accordingly, present approach will offer benefit as a therapeutic strategy against type 2 diabetes mellitus and associated metabolic complications.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

In vitro and in silico cholinesterase inhibitory potential of metabolites from Laurencia snackeyi (Weber-van Bosse) M. Masuda

Alzheimer's disease (AD) is a neurodegenerative disease that causes deterioration in intelligence and psychological activities. Yet, till today, no cure is available for AD. The marine environment is an important sink of bioactive compounds with neuroprotective potential with reduced adverse effects. Recently, we collected the red algae Laurencia snackeyi from Terumbu Island, Malaysia which is known to be rich in halogenated metabolites making it the most sought-after red algae for pharmaceutical studies. The red alga was identified based on basic morphological characteristics, microscopic observation and chemical data from literature. The purplish-brown algae was confirmed a new record. In Malaysia, this species is poorly documented in Peninsular Malaysia as compared to its eastern continent Borneo. Thus, this study intended to investigate the diversity of secondary metabolites present in the alga and its cholinesterase inhibiting potential for AD. The extract inhibited both acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with IC50 values of  14.45 ± 0.34 μ g mL-1 and 39.59 ± 0.24 μ g mL-1, respectively. Subsequently, we isolated the synderanes, palisadin A (1), aplysistatin (2) and 5-acetoxypalisadin B (3) that was not exhibit potential. Mass spectrometry analysis detected at total of 33 additional metabolites. The computational aided molecular docking using the AChE and BChE receptors on all metabolites shortlisted 5,8,11,14-eicosatetraynoic acid (31) and 15-hydroxy-1-[2-(hydroxymethyl)-1-piperidinyl]prost-13-ene-1,9-dione (42) with best inhibitory properties, respectively with the lowest optimal combination of S-score and RMSD values. This study shows the unexplored potential of marine natural resources, however, obtaining sufficient biomass for detailed investigation is an uphill task. Regardless, there is a lot of potential for future prospects with a wide range of marine natural resources to study and the incorporation of synthetic chemistry, in vivo studies in experimental design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03725-6.

Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries

Introduction

Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem.

Methods

In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis.

Results

The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds.

Conclusion

The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.

The depsidones from marine sponge-derived fungus Aspergillus unguis IB151 as an anti-MRSA agent: Molecular docking, pharmacokinetics analysis, and molecular dynamic simulation studies

Methicillin-resistant Staphylococcus aureus (MRSA) is an emerging nosocomial pathogen among hospitalized patients, with high morbidity and mortality rates. The discovery of a novel antibacterial is urgently needed to address this resistance problem. The present study aims to explore the antibacterial potential of three depsidone compounds: 2-clorounguinol (1), unguinol (2), and nidulin (3), isolated from the marine sponge-derived fungus Aspergillus unguis IB1, both in vitro and in silico. The antibacterial activity of all compounds was evaluated by calculating the Minimum inhibitory concentration (MIC) and Minimum bactericidal concentration (MBC) against MRSA using agar diffusion and total plate count methods, respectively. Bacterial cell morphology changes were  studied for the first time using scanning electron microscopy (SEM). Molecular docking, pharmacokinetics analysis, and molecular dynamics simulation were performed to determine possible protein-ligand interactions and the stability of the targeting penicillin-binding protein 2a (PBP2a) against 2-clorounguinol (1). The research findings indicated that compounds 1 to 3 exhibited MIC and MBC values of 2 µg/mL and 16 µg/mL against MRSA, respectively. MRSA cells displayed a distinct shape after the addition of the depsidone compound, as observed in SEM. According to the in silico study, 2-chlorounguinol exhibited the highest binding-free energy (BFE) with PBP2a (-6.7 kcal/mol). For comparison, (E)-3-(2-(4-cyanostyryl)-4-oxoquinazolin-3(4H)-yl) benzoic acid inhibits PBP2a with a BFE less than -6.6 kcal/mol. Based on the Lipinski's rule of 5, depsidone compounds constitute a class of compounds with good pharmacokinetic properties, being easily absorbed and permeable. These findings suggest that 2-chlorounguinol possesses potential antibacterial activity and could be developed as an antibiotic adjuvant to reduce antimicrobial resistance.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

In silico and in vitro analysis of the mechanisms of action of nitroxoline against some medically important opportunistic fungi

The increasing resistance to antifungal agents associated with toxicity and interactions turns therapeutic management of fungal infections difficult. This scenario emphasizes the importance of drug repositioning, such as nitroxoline - a urinary antibacterial agent that has shown potential antifungal activity. The aims of this study were to discover the possible therapeutic targets of nitroxoline using an in silico approach, and to determine the in vitro antifungal activity of the drug against the fungal cell wall and cytoplasmic membrane. We explored the biological activity of nitroxoline using PASS, SwissTargetPrediction and Cortellis Drug Discovery Intelligence web tools. After confirmation, the molecule was designed and optimized in HyperChem software. GOLD 2020.1 software was used to predict the interactions between the drug and the target proteins. In vitro investigation evaluated the effect of nitroxoline on the fungal cell wall through sorbitol protection assay. Ergosterol binding assay was carried out to assess the effect of the drug on the cytoplasmic membrane. In silico investigation revealed biological activity with alkane 1-monooxygenase and methionine aminopeptidase enzymes, showing nine and five interactions in the molecular docking, respectively. In vitro results exhibited no effect on the fungal cell wall or cytoplasmic membrane. Finally, nitroxoline has potential as an antifungal agent due to the interaction with alkane 1-monooxygenase and methionine aminopeptidase enzymes, which are not the main human therapeutic targets. These results have potentially revealed a new biological target for the treatment of fungal infections. We also consider that further studies are required to confirm the biological activity of nitroxoline on fungal cells, mainly the confirmation of the alkB gene.

Anthocyanin-Containing Purple Sweet Potato (Ipomoea batatas L.) Synbiotic Yogurt Inhibited 3T3-L1 Adipogenesis by Suppressing White Adipocyte-Specific Genes

Purpose

We unravel the effect of anthocyanin-containing purple sweet potato synbiotic yogurt (PSPY) on 3T3-L1 adipocyte differentiation and its fundamental molecular mechanisms.

Methods

Molecular docking simulation was performed to observe and identify the affinity and interaction between bioactive compounds and targeted proteins. MDI (isobutylmethylxanthine, dexamethasone, and insulin)-containing medium, a cocktail that stimulates adipogenesis, was used in this study. The toxic effect possibility of the yogurt product was evaluated using 3-[4, 5-dimethylthiazol-2-yl]-2.5 diphenyl tetrazolium bromide (MTT) assay. A 0.25%, 0.5%, 1%, and 5% (v/v) plain or purple sweet potato yogurt supernatant was given to 3T3-L1 preadipocyte culture medium from 24 h after seeding until day 11 of MDI-induced differentiation. The mRNA expression and lipid accumulation were analyzed using RT-qPCR and Oil red O staining, respectively, on day 11 after differentiation induction.

Results

In silico study suggested that anthocyanin-derived compounds have the potential to inhibit peroxisome proliferator activated receptor gamma (PPAR-γ), a master regulator for white adipogenesis. Anthocyanin-containing PSPY significantly suppressed the expression of Pparg, Adipoq, Slc2a4, and Pgc1a. PSPY significantly suppressed Pparg with 1% and 5% concentrations, while with a concentration of 0.25%, PSPY significantly suppressed Adipoq expression as compared to control. Significant inhibition of Slc2a4 and Pgc1a was observed starting from a 0.25% concentration of PSPY. The suppression of adipogenic genes was also observed with the treatment of plain yogurt; however, the effects were relatively lower than the PSPY. The group treated with 1% and 5% of PSPY also showed inhibition effects on lipid accumulation.

Conclusion

This study demonstrated PSPY inhibition effect on white adipocyte differentiation through suppression of Pparg and its downstream genes, Adipoq and Slc2a4, indicating the potential of this yogurt as a functional food for obesity management and prevention.

Scaffold repositioning of spiro-acridine derivatives as fungi chitinase inhibitor by target fishing and in vitro studies

The concept of "one target, one drug, one disease" is not always true, as compounds with previously described therapeutic applications can be useful to treat other maladies. For example, acridine derivatives have several potential therapeutic applications. In this way, identifying new potential targets for available drugs is crucial for the rational management of diseases. Computational methodologies are interesting tools in this field, as they use rational and direct methods. Thus, this study focused on identifying other rational targets for acridine derivatives by employing inverse virtual screening (IVS). This analysis revealed that chitinase enzymes can be potential targets for these compounds. Subsequently, we coupled molecular docking consensus analysis to screen the best chitinase inhibitor among acridine derivatives. We observed that 3 compounds displayed potential enhanced activity as fungal chitinase inhibitors, showing that compound 5 is the most active molecule, with an IC₅₀ of 0.6 ng/µL. In addition, this compound demonstrated a good interaction with the active site of chitinases from Aspergillus fumigatus and Trichoderma harzianum. Additionally, molecular dynamics and free energy demonstrated complex stability for compound 5. Therefore, this study recommends IVS as a powerful tool for drug development. The potential applications are highlighted as this is the first report of spiro-acridine derivatives acting as chitinase inhibitors that can be potentially used as antifungal and antibacterial candidates.

Liquid Crystal Monomer: A Potential PPARγ Antagonist

Liquid crystal monomers (LCMs) are a large family of artificial ingredients that have been widely used in global liquid crystal display (LCD) industries. As a major constituent in LCDs as well as the end products of e-waste dismantling, LCMs are of growing research interest with regard to their environmental occurrences and biochemical consequences. Many studies have analyzed LCMs in multiple environmental matrices, yet limited research has investigated the toxic effects upon exposure to them. In this study, we combined in silico simulation and in vitro assay validation along with omics integration analysis to achieve a comprehensive toxicity elucidation as well as a systematic mechanism interpretation of LCMs for the first time. Briefly, the high-throughput virtual screen and reporter gene assay revealed that peroxisome proliferator-activated receptor gamma (PPARγ) was significantly antagonized by certain LCMs. Besides, LCMs induced global metabolome and transcriptome dysregulation in HK2 cells. Notably, fatty acid β-oxidation was conspicuously dysregulated, which might be mediated through multiple pathways (IL-17, TNF, and NF-kB), whereas the activation of AMPK and ligand-dependent PPARγ antagonism may play particularly important parts. This study illustrated LCMs as a potential PPARγ antagonist and explored their toxicological mode of action on the trans-omics level, which provided an insightful overview in future chemical risk assessment.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Antiplasmodial activity of coumarins isolated from Polygala boliviensis: in vitro and in silico studies

Polygala boliviensis is found in the Brazilian semiarid region. This specie is little chemically and biologically studied. Polygala spp. have different metabolites, especially coumarins. Studies indicate that coumarins have antimalarial potential, denoting the importance of researching new active compounds from plants, since the resistance of Plasmodium strains to conventional therapy has increased. The present study aimed to evaluate the antiplasmodial activity of auraptene and poligalen against a chloroquine-resistant strain of Plasmodium falciparum. Coumarins were isolated from P. boliviensis by open column chromatography and identified by Nuclear Magnetic Resonance Spectroscopy. A cytotoxicity assay was carried out using MTT test, and the in vitro antiplasmodial activity was evaluated using the W2 strain. The antiplasmodial activity results found were IC50=0.171 ± 0.016 for auraptene and 0.164 ± 0.012 for poligalen; the selectivity indexes were 78.71 and 609.76, respectively. Inverse virtual screening in the BRAMMT database by OCTOPUS 1.2 was applied to coumarins to find potential P. falciparum targets and showed higher affinity energy of auraptene for purine nucleoside phosphorylase (PfPNP) and of poligalen for dihydroorotate dehydrogenase (PfDHODH). Molecular Dynamics studies (MD and MM-GBSA) approach were applied to calculate binding energies against selected P. falciparum targets and showed that all coumarins were stable at the binding site during simulations. Furthermore, energies were favorable for complexation. This is the first report of auraptene in P. boliviensis species and of in vitro antiplasmodial activity of auraptene and poligalen. In silico studies indicated that the mechanism of action of coumarins is the inhibition of PfPNP and PfDHODH.Communicated by Ramaswamy H. Sarma.

Toxicological impacts and likely protein targets of bisphenol a in Paramecium caudatum

Bisphenol A (BPA) is a widely used plasticizer agent and a well-known ubiquitous endocrine disruptor, which is frequently associated with a series of reproductive, developmental, and transgenerational effects over wildlife, livestocks, and humans. Although extensive toxicological data is available for metazoans, the impact of BPA over unicellular eukaryotes, which represents a considerable proportion of eukaryotic diversity, remains largely overlooked. Here, we used acute end-point toxicological assay and an inverted virtual-screening (IVS) approach to evaluate cellular impairments infringed by BPA over the cosmopolitan ciliated protist, Paramecium caudatum. Our data indicate a clear time-dependent effect over P. caudatum survival, which seems to be a consequence of disruptions to multiple core cellular functions, such as DNA and cell replication, transcription, translation and signaling pathways. Finally, the use of this ciliate as a biosensor to monitor BPA within environments and the relevance of bioinformatic methods to leverage our current knowledge on the impacts of emerging contaminants to biological systems are discussed.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

An allosteric modulator activates BK channels by perturbing coupling between Ca2+ binding and pore opening

BK type Ca²⁺-activated K⁺ channels activate in response to both voltage and Ca²⁺. The membrane-spanning voltage sensor domain (VSD) activation and Ca²⁺ binding to the cytosolic tail domain (CTD) open the pore across the membrane, but the mechanisms that couple VSD activation and Ca²⁺ binding to pore opening  are not clear. Here we show that a compound, BC5, identified from in silico screening, interacts with the CTD-VSD interface and specifically modulates the Ca²⁺ dependent activation mechanism. BC5 activates the channel in the absence of Ca²⁺ binding but Ca²⁺ binding inhibits BC5 effects. Thus, BC5 perturbs a pathway that couples Ca²⁺ binding to pore opening to allosterically affect both, which is further supported by atomistic simulations and mutagenesis. The results suggest that the CTD-VSD interaction makes a major contribution to the mechanism of Ca²⁺ dependent activation and is an important site for allosteric agonists to modulate BK channel activation.

Towards an Enrichment Optimization Algorithm (EOA)-based Target Specific Docking Functions for Virtual Screening

Docking-based virtual screening (VS) is a common starting point in many drug discovery projects. While ligand-based approaches may sometimes provide better results, the advantage of docking lies in its ability to provide reliable ligand binding modes and approximated binding free energies, two factors that are important for hit selection and optimization. Most docking programs were developed to be as general as possible and consequently their performances on specific targets may be sub-optimal. With this in mind, in this work we present a method for the development of target-specific scoring functions using our recently reported Enrichment Optimization Algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations by optimizing an enrichment-like metric. Since EOA requires target-specific active and inactive (or decoy) compounds, we retrieved such data for six targets from the DUD-E database, and used them to re-derive the weights associated with the components that make up GOLD's ChemPLP scoring function yielding target-specific, modified functions. We then used the original ChemPLP function in small-scale VS experiments on the six targets and subsequently rescored the resulting poses with the modified functions. In addition, we used the modified functions for compounds re-docking. We found that in many although not all cases, either rescoring the original ChemPLP poses or repeating the entire docking process with the modified functions, yielded better results in terms of AUC and EF_1% , two metrics, common for the evaluation of VS performances. While work on additional datasets and docking tools is clearly required, we propose that the results obtained thus far hint to the potential benefits in using EOA-based optimization for the derivation of target-specific functions in the context of virtual screening. To this end, we discuss the downsides of the methods and how it could be improved.

Complementary Dual Approach for In Silico Target Identification of Potential Pharmaceutical Compounds in Cystic Fibrosis

Cystic fibrosis is a genetic disease caused by mutation of the CFTR gene, which encodes a chloride and bicarbonate transporter in epithelial cells. Due to the vast range of geno- and phenotypes, it is difficult to find causative treatments; however, small-molecule therapeutics have been clinically approved in the last decade. Still, the search for novel therapeutics is ongoing, and thousands of compounds are being tested in different assays, often leaving their mechanism of action unknown. Here, we bring together a CFTR-specific compound database (CandActCFTR) and systems biology model (CFTR Lifecycle Map) to identify the targets of the most promising compounds. We use a dual inverse screening approach, where we employ target- and ligand-based methods to suggest targets of 309 active compounds in the database amongst 90 protein targets from the systems biology model. Overall, we identified 1038 potential target–compound pairings and were able to suggest targets for all 309 active compounds in the database.

Evaluation of antiplasmodial activity in silico and in vitro of N-acylhydrazone derivatives

N-acylhydrazones are considered privileged structures in medicinal chemistry, being part of antimicrobial compounds (for example). In this study we show the activity of N-acylhydrazone compounds, namely AH1, AH2, AH4, AH5 in in vitro tests against the chloroquine-resistant strain of Plasmodium falciparum (W2) and against WI26 VA-4 human cell lines. All compounds showed low cytotoxicity (LC₅₀ > 100 µM). The AH5 compound was the most active against Plasmodium falciparum, with an IC₅₀ value of 0.07 μM. AH4 and AH5 were selected among the tested compounds for molecular docking calculations to elucidate possible targets involved in their mechanism of action and the SwissADME analysis to predict their pharmacokinetic profile. The AH5 compound showed affinity for 12 targets with low selectivity, while the AH4 compound had greater affinity for only one target (3PHC). These compounds met Lipinski's standards in the ADME in silico tests, indicating good bioavailability results. These results demonstrate that these N-acylhydrazone compounds are good candidates for future preclinical studies against malaria.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.

Systematic understanding of anti-tumor mechanisms of Tamarixetin through network and experimental analyses

Tamarixetin, a flavonoid derived from Quercetin, was shown to possess anti-cancer properties in various types of cancer. However, the mechanism of action of this compound is not well understood. Observations from reverse docking and network pharmacology analysis, were validated by cell based studies to analyse the chemotherapeutic potential and elucidate the molecular mechanism of action of Tamarixetin in breast cancer. In silico analysis using reverse docking and PPI analysis clearly indicated that out of 35 proteins targeted by Tamarixetin, the top 3 hub genes, namely, AKT1, ESR1 and HSP90AA1, were upregulated in breast tumor tissues and more importantly showed strong negative correlation to breast cancer patient survival. Furthermore, the KEGG pathway analysis showed enrichment of target proteins of Tamarixetin in 33 pathways which are mainly involved in neoplastic signalling. In vitro cell-based studies demonstrated that Tamarixetin could inhibit cell proliferation, induce ROS and reduce mitochondrial membrane potential, leading to cell death. Tamarixetin induced cell cycle arrest at G2/M phase and inhibited the migration as well as the invasion of breast cancer cells. Taken together, the combination of in silico and in vitro approaches used in the present study clearly provides evidence for the chemotherapeutic potential of Tamarixetin in breast cancer.

Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals’ mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

Structural Insight of New Butyrylcholinesterase Inhibitors Based on Benzylbenzofuran Scaffold

In the present work, we use a merger of computational and biochemical techniques as a rational guideline for structural modification of benzofuran derivatives to find pertinent structural features for the butyrylcholinesterase inhibitory activity and selectivity. Previously, we revealed a series of 2-phenylbenzofuran compounds that displayed a selective inhibitory activity for BChE. Here, in an effort to discover novel selective BChE inhibitors with favorable physicochemical and pharmacokinetic profiles, 2-benzylbenzofurans were designed, synthesized, and evaluated as BChE inhibitors. The 2-phenylbenzofuran scaffold structure is modified by introducing one methylene spacer between the benzofuran core and the 2-phenyl ring with a hydroxyl substituent in the para or meta position. Either position 5 or 7 of the benzofuran scaffold was substituted with a bromine or chlorine atom. Further assessment of the selected list of compounds indicated that the substituent’s nature and position determined their activity and selectivity. 5-bromo-2-(4-hydroxybenzyl)benzofuran 9B proved to be the most potent butyrylcholinesterase inhibitor (IC₅₀ = 2.93 µM) of the studied series. Computational studies were carried out to correlate the theoretical and experimental binding affinity of the compounds to the BChE protein.

Synthesis and inverse virtual screening of new bi-cyclic structures towards cancer-relevant cellular targets

We report here synthetic approaches to access new classes of small molecules based on three heterocyclic scaffolds, i.e. 3,7-dihydropyrimido[4,5-d]pyridazine-4,8-dione, 1,8-naphthyridin-4(1H)-one and 4H-pyrido[1,2-a]pyrimidin-4-one. The bi-cyclic structure 3,7-dihydropyrimido[4,5-d]pyridazine-4,8-dione is a new heterocycle, described here for the first time. In silico methodologies of inverse virtual screening have been used to preliminary analyse the molecules, in order to explore their potential as hits for chemical biology investigations. Our computational study has been conducted with 43 synthetically accessible small molecules towards 31 cellular proteins involved in cancer pathogenesis. Binding energies were quantified using molecular docking calculations, allowing to define the relative affinities of the ligands for the cellular targets. Through this methodology, 16 proteins displayed effective interactions with distinct small molecules within the matrix. In addition, 23 ligands have demonstrated high affinity for at least one cellular protein, using as reference the co-crystallised ligand in the X-ray structure. The evaluation of ADME and drug score for selected hits also highlights that these new molecular series can serve as sources of lead candidates for further structure optimisation and biological studies.

Mechanistic Insights into Biological Activities of Polyphenolic Compounds from Rosemary Obtained by Inverse Molecular Docking

Rosemary (Rosmarinus officinalis L.) represents a medicinal plant known for its various health-promoting properties. Its extracts and essential oils exhibit antioxidative, anti-inflammatory, anticarcinogenic, and antimicrobial activities. The main compounds responsible for these effects are the diterpenes carnosic acid, carnosol, and rosmanol, as well as the phenolic acid ester rosmarinic acid. However, surprisingly little is known about the molecular mechanisms responsible for the pharmacological activities of rosemary and its compounds. To discern these mechanisms, we performed a large-scale inverse molecular docking study to identify their potential protein targets. Listed compounds were separately docked into predicted binding sites of all non-redundant holo proteins from the Protein Data Bank and those with the top scores were further examined. We focused on proteins directly related to human health, including human and mammalian proteins as well as proteins from pathogenic bacteria, viruses, and parasites. The observed interactions of rosemary compounds indeed confirm the beforementioned activities, whereas we also identified their potential for anticoagulant and antiparasitic actions. The obtained results were carefully checked against the existing experimental findings from the scientific literature as well as further validated using both redocking procedures and retrospective metrics.

Natural inspired piperine-based ureas and amides as novel antitumor agents towards breast cancer

In this work, the natural piperine moiety was utilised to develop two sets of piperine-based amides (5a–i) and ureas (8a–y) as potential anticancer agents. The anticancer action was assessed against triple negative breast cancer (TNBC) MDA-MB-231, ovarian A2780CP and hepatocellular HepG2 cancer cell lines. In particular, 8q stood out as the most potent anti-proliferative analogue against TNBC MDA-MB-231 cells with IC₅₀ equals 18.7 µM, which is better than that of piperine (IC₅₀ = 47.8 µM) and 5-FU (IC₅₀ = 38.5 µM). Furthermore, 8q was investigated for its possible mechanism of action in MDA-MB-231 cells via Annexin V-FITC apoptosis assay and cell cycle analysis. Moreover, an in-silico analysis has proposed VEGFR-2 as a probable enzymatic target for piperine-based derivatives, and then has explored the binding interactions within VEGFR-2 active site (PDB:4ASD). Finally, an in vitro VEGFR-2 inhibition assay was performed to validate the in silico findings, where 8q showed good VEGFR-2 inhibitory activity with IC₅₀ = 231 nM.

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor

Background

The study of drug–target interactions (DTIs) affinity plays an important role in safety assessment and pharmacology. Currently, quantitative structure–activity relationship (QSAR) and molecular docking (MD) are most common methods in research of DTIs affinity. However, they often built for a specific target or several targets, and most QSAR and MD methods were based either on structure of drug molecules or on structure of receptors with low accuracy and small scope of application. How to construct quantitative prediction models with high accuracy and wide applicability remains a challenge. To this end, this paper screened molecular descriptors based on molecular vibrations and took molecule-target as a whole system to construct prediction models with high accuracy-wide applicability based on dissociation constant (Kd) and concentration for 50% of maximal effect (EC50), and to provide reference for quantifying affinity of DTIs.

Results

After comprehensive comparison, the results showed that RF models are optimal models to analyze and predict DTIs affinity with coefficients of determination (R²) are all greater than 0.94. Compared to the quantitative models reported in literatures, the RF models developed in this paper have higher accuracy and wide applicability. In addition, E-state molecular descriptors associated with molecular vibrations and normalized Moreau-Broto autocorrelation (G3), Moran autocorrelation (G4), transition-distribution (G7) protein descriptors are of higher importance in the quantification of DTIs.

Conclusion

Through screening molecular descriptors based on molecular vibrations and taking molecule-target as whole system, we obtained optimal models based on RF with more accurate-widely applicable, which indicated that selection of molecular descriptors associated with molecular vibrations and the use of molecular-target as whole system are reliable methods for improving performance of models. It can provide reference for quantifying affinity of DTIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04389-w.

Machine-learning methods for ligand-protein molecular docking

Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Phosphate binding sites prediction in phosphorylation-dependent protein-protein interactions

MOTIVATION

Phosphate binding plays an important role in modulating protein-protein interactions, which are ubiquitous in various biological processes. Accurate prediction of phosphate binding sites is an important but challenging task. Small size and diversity of phosphate binding sites lead to a substantial challenge for developing accurate prediction methods.

RESULTS

Here we present the phosphate binding site predictor (PBSP), a novel and accurate approach to identifying phosphate binding sites from protein structures. PBSP combines an energy-based ligand-binding sites identification method with reverse focused docking using a phosphate probe. We show that PBSP outperforms not only general ligand binding sites predictors but also other existing phospholigand-specific binding sites predictors. It achieves ∼95% success rate for top 10 predicted sites with an average Matthews correlation coefficient (MCC) value of 0.84 for successful predictions. PBSP can accurately predict phosphate binding modes, with average position error of 1.4 Å and 2.4 Å in bound and unbound datasets, respectively. Lastly, visual inspection of the predictions is conducted. Reasons for failed predictions are further analyzed and possible ways to improve the performance are provided. These results demonstrate a novel and accurate approach to phosphate binding sites identification in protein structures.

AVAILABILITY

The software and benchmark datasets are freely available at http://web.pkusz.edu.cn/wu/PBSP/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19

AIMS

To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications.

BACKGROUND

SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally.

OBJECTIVE

To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs.

METHODS

A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol.

RESULTS

Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications.

CONCLUSION

Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

Drug Target Identification with Machine Learning: How to Choose Negative Examples

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering

In their previous work, Srinivas et al. [ J. Cheminf. 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known bioactivity data. In this work, we extend these implicit fingerprints/descriptors using deep learning techniques to translate latent descriptors into discrete representations of molecules (SMILES), without explicitly optimizing for chemical properties. This allows the design of new compounds based upon the latent representation of nearby proteins, thereby encoding druglike properties including binding affinities to known proteins. The implicit descriptor method does not require any fingerprint similarity search, which makes the method free of any bias arising from the empirical nature of the fingerprint models [Srinivas, R.; J. Cheminf. 2018, 10, 56]. We evaluate the properties of the potentially novel drugs generated by our approach using physical properties of druglike molecules and chemical complexity. Additionally, we analyze the reliability of the biological activity of the new compounds generated using this method by employing models of protein-ligand interaction, which assists in assessing the potential binding affinity of the designed compounds. We find that the generated compounds exhibit properties of chemically feasible compounds and are predicted to be excellent binders to known proteins. Furthermore, we also analyze the diversity of compounds created using the Tanimoto distance and conclude that there is a wide diversity in the generated compounds.