Automated docking screens: a feasibility study

SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina

Drug discovery aims to identify potential therapeutic compounds capable of modulating the activity of specific biological targets. Molecular docking can efficiently support this process by predicting binding interactions between small molecules and macromolecular targets and potentially accelerating screening campaigns. SwissDock is a computational tool released in 2011 as part of the SwissDrugDesign project, providing a free web-based service for small-molecule docking after automatized preparation of ligands and targets. Here, we present the latest version of SwissDock, in which EADock DSS has been replaced by two state-of-the-art docking programs, i.e. Attracting Cavities and AutoDock Vina. AutoDock Vina provides faster docking predictions, while Attracting Cavities offers more accurate results. Ligands can be imported in various ways, including as files, SMILES notation or molecular sketches. Targets can be imported as PDB files or identified by their PDB ID. In addition, advanced search options are available both for ligands and targets, giving users automatized access to widely-used databases. The web interface has been completely redesigned for interactive submission and analysis of docking results. Moreover, we developed a user-friendly command-line access which, in addition to all options of the web site, also enables covalent ligand docking with Attracting Cavities. The new version of SwissDock is freely available at https://www.swissdock.ch/.

Structural insights into the potential binding sites of Cathepsin D using molecular modelling techniques

Alzheimer's disease (AD) is the most prevalent type of dementia caused by the accumulation of amyloid beta (Aβ) peptides. The extracellular deposition of Aβ peptides in human AD brain causes neuronal death. Therefore, it has been found that Aβ peptide degradation is a possible therapeutic target for AD. CathD has been known to breakdown amyloid beta peptides. However, the structural role of CathD is not yet clear. Hence, for the purpose of gaining a deeper comprehension of the structure of CathD, the present computational investigation was performed using virtual screening technique to predict CathD's active site residues and substrate binding mode. Ligand-based virtual screening was implemented on small molecules from ZINC database against crystal structure of CathD. Further, molecular docking was utilised to investigate the binding mechanism of CathD with substrates and virtually screened inhibitors. Localised compounds obtained through screening performed by PyRx and AutoDock 4.2 with CathD receptor and the compounds having highest binding affinities were picked as; ZINC00601317, ZINC04214975 and ZINCC12500925 as our top choices. The hydrophobic residues Viz. Gly35, Val31, Thr34, Gly128, Ile124 and Ala13 help stabilising the CathD-ligand complexes, which in turn emphasises substrate and inhibitor selectivity. Further, MM-GBSA approach has been used to calculate binding free energy between CathD and selected compounds. Therefore, it would be beneficial to understand the active site pocket of CathD with the assistance of these discoveries. Thus, the present study would be helpful to identify active site pocket of CathD, which could be beneficial to develop novel therapeutic strategies for the AD.

In silico identification of phytochemical inhibitors for multidrug-resistant tuberculosis based on novel pharmacophore generation and molecular dynamics simulation studies

BACKGROUND

Multidrug-resistant tuberculosis (particularly resistant to pyrazinoic acid) is a life-threatening chronic pulmonary disease. Running a marketed regime specifically targets the ribosomal protein subunit-1 (RpsA) and stops trans-translation in the non-mutant bacterium, responsible for the lysis of bacterial cells. However, in the strains of mutant bacteria, this regime has failed in curing TB and killing pathogens, which may only because of the ala438 deletion, which inhibit the binding of pyrazinoic acid to the RpsA active site. Therefore, such cases of tuberculosis need an immediate and effective regime.

OBJECTIVE

This study has tried to determine and design such chemotypes that are able to bind to the mutant RpsA protein.

METHODS

For these purposes, two phytochemical databases, i.e., NPASS and SANCDB, were virtually screened by a pharmacophore model using an online virtual screening server Pharmit.

RESULTS

The model of pharmacophore was developed using the potential inhibitor (zr115) for the mutant of RpsA. Pharmacophore-based virtual screening results into 154 hits from the NPASS database, and 22 hits from the SANCDB database. All the predicted hits were docked in the binding pocket of the mutant RpsA protein. Top-ranked five and two compounds were selected from the NPASS and SANCDB databases respectively. On the basis of binding energies and binding affinities of the compounds, three compounds were selected from the NPASS database and one from the SANCDB database. All compounds were found to be non-toxic and highly active against the mutant pathogen. To further validate the docking results and check the stability of hits, molecular dynamic simulation of three compounds were performed. The MD simulation results showed that all these finally selected compounds have stronger binding interactions, lesser deviation or fluctuations, with greater compactness compared to the reference compound.

CONCLUSION

These findings indicate that these compounds could be effective inhibitors for mutant RpsA.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

Docking for Molecules That Bind in a Symmetric Stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration

Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) information on compounds and proteins, such as atom coordinates and distance matrices, to predict binding affinity. Furthermore, numerous widely adopted computational techniques have relied on sequences of amino acid characters for protein representations. This approach may constrain the model's ability to capture meaningful biochemical features, impeding a more comprehensive understanding of the underlying proteins. Here, we propose a two-step deep learning strategy named MulinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach leverages 3D information from both proteins and compounds and acquires a profound understanding of the atomic-level features of proteins. Besides, our research highlights the divide between first-principle and data-driven methods, offering new research prospects for compound-protein interaction tasks. We applied the proposed method to six datasets: Davis, Metz, KIBA, CASF-2016, DUD-E and BindingDB, to evaluate the effectiveness of our approach.

Discovery of potent STAT3 inhibitors using structure-based virtual screening, molecular dynamic simulation, and biological evaluation

Introduction

Signal transducer and activator of transcription 3 (STAT3) is ubiquitously hyper-activated in numerous cancers, rendering it an appealing target for therapeutic intervention.

Methods and results

In this study, using structure-based virtual screening complemented by molecular dynamics simulations, we identified ten potential STAT3 inhibitors. The simulations pinpointed compounds 8, 9, and 10 as forming distinct hydrogen bonds with the SH2 domain of STAT3. In vitro cytotoxicity assays highlighted compound 4 as a potent inhibitor of gastric cancer cell proliferation across MGC803, KATO III, and NCI-N87 cell lines. Further cellular assays substantiated the ability of compound 4 to attenuate IL-6-mediated STAT3 phosphorylation at Tyr475. Additionally, oxygen consumption rate assays corroborated compound 4's deleterious effects on mitochondrial function.

Discussion

Collectively, our findings position compound 4 as a promising lead candidate warranting further exploration in the development of anti-gastric cancer therapeutics.

Docking for molecules that bind in a symmetric stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

Catalytic inhibitor of Protein Phosphatase 5 activates the extrinsic apoptotic pathway by disrupting complex II in kidney cancer

Serine/threonine protein phosphatase-5 (PP5) is involved in tumor progression and survival, making it an attractive therapeutic target. Specific inhibition of protein phosphatases has remained challenging because of their conserved catalytic sites. PP5 contains its regulatory domains within a single polypeptide chain, making it a more desirable target. Here we used an in silico approach to screen and develop a selective inhibitor of PP5. Compound P053 is a competitive inhibitor of PP5 that binds to its catalytic domain and causes apoptosis in renal cancer. We further demonstrated that PP5 interacts with FADD, RIPK1, and caspase 8, components of the extrinsic apoptotic pathway complex II. Specifically, PP5 dephosphorylates and inactivates the death effector protein FADD, preserving complex II integrity and regulating extrinsic apoptosis. Our data suggests that PP5 promotes renal cancer survival by suppressing the extrinsic apoptotic pathway. Pharmacologic inhibition of PP5 activates this pathway, presenting a viable therapeutic strategy for renal cancer.

Establishing mammalian GLUT kinetics and lipid composition influences in a reconstituted-liposome system

Glucose transporters (GLUTs) are essential for organism-wide glucose homeostasis in mammals, and their dysfunction is associated with numerous diseases, such as diabetes and cancer. Despite structural advances, transport assays using purified GLUTs have proven to be difficult to implement, hampering deeper mechanistic insights. Here, we have optimized a transport assay in liposomes for the fructose-specific isoform GLUT5. By combining lipidomic analysis with native MS and thermal-shift assays, we replicate the GLUT5 transport activities seen in crude lipids using a small number of synthetic lipids. We conclude that GLUT5 is only active under a specific range of membrane fluidity, and that human GLUT1-4 prefers a similar lipid composition to GLUT5. Although GLUT3 is designated as the high-affinity glucose transporter, in vitro D-glucose kinetics demonstrates that GLUT1 and GLUT3 actually have a similar K_M, but GLUT3 has a higher turnover. Interestingly, GLUT4 has a high K_M for D-glucose and yet a very slow turnover, which may have evolved to ensure uptake regulation by insulin-dependent trafficking. Overall, we outline a much-needed transport assay for measuring GLUT kinetics and our analysis implies that high-levels of free fatty acid in membranes, as found in those suffering from metabolic disorders, could directly impair glucose uptake.

Structure-based discovery of novel cruzain inhibitors with distinct trypanocidal activity profiles

Over 110 years after the first formal description of Chagas disease, the trypanocidal drugs thus far available have limited efficacy and several side effects. This encourages the search for novel treatments that inhibit T. cruzi targets. One of the most studied anti-T. cruzi targets is the cysteine protease cruzain; it is associated with metacyclogenesis, replication, and invasion of the host cells. We used computational techniques to identify novel molecular scaffolds that act as cruzain inhibitors. First, with a docking-based virtual screening, we identified compound 8, a competitive cruzain inhibitor with a K_i of 4.6 μM. Then, aided by molecular dynamics simulations, cheminformatics, and docking, we identified the analog compound 22 with a K_i of 27 μM. Surprisingly, despite sharing the same isoquinoline scaffold, compound 8 presented higher trypanocidal activity against the epimastigote forms, while compound 22, against the trypomastigotes and amastigotes. Taken together, compounds 8 and 22 represent a promising scaffold for further development of trypanocidal compounds as drug candidates for treating Chagas disease.

Exploration of Indolo[3,2c]isoquinoline derived triazoles as potential antimicrobial and DNA cleavage agents: Synthesis, DFT calculations, and molecular modeling studies

Indole and its derivatives are well-known assorted motif in drug design and development. We here in reporting synthesis of new 9-chloro-1-(4-substituted phenyl)-12H-indolo[2,3-c][1,2,4]triazolo[3,4-a]isoquinolines 7 (a-h). Structures of the newly synthesized compounds were confirmed by making use of spectroscopic techniques like IR, NMR and Mass. The DFT calculations were taken for the selected molecules using CAM-B3LYP hybrid functional with a 6-31 + g(d) all-electron basis set using the Gaussian 09 package. The drug-likeness predictions were described for the synthesized derivatives. The In vitro antimicrobial and DNA cleavage activities were reported for all compounds 7 (a-h). The compounds 7a, 7b, and 7h showed excellent microbial inhibition and DNA cleavage activity as compared to standard drugs. Furthermore, the docking studies for the newly synthesized molecules were carried out by Auto dock software with two molecular targets Epidermal Growth Factor Receptor tyrosine kinase (1 M17) and C-kit Tyrosine Kinase (1 T46) exhibited better binding affinity of all synthesized compounds. In addition, the docking results were observed to be in full agreement with the in vitro DNA cleavage assay suggesting the potential of synthesized metal complexes in biological applications. Lastly, the protein stability, fluctuations of APO-Protein, and protein-ligand complexes were investigated through Molecular Dynamics (MD) simulations studies using Desmond Maestro 11.3 and potential lead molecules were identified.

<em>In silico</em> screening of potential compounds from begonia genus as 3CL protease (3Cl pro) SARS-CoV-2 inhibitors

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective: This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases “promising” for the anti-SARS-CoV-2 target. Methods: In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Results: Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only β-sitosterol-3-O-β-D-glucopyranoside with a lower docking score of - 9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 Å, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 Å and 0.773 Å, respectively. Conclusions: This result suggests that β-sitosterol-3-O-β-D- glucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.

Hic Sunt Dracones: Molecular Docking in Uncharted Territories with Structures from AlphaFold2 and RoseTTAfold

AlphaFold2 and RoseTTAfold impress with their high accuracy in protein structure prediction. However, for structure-based virtual screenings, not only the overall structure but especially the binding sites need to be accurately predicted. In this work, the docking performance for 66 targets with known ligands but without experimental structures available in the protein data bank was elucidated. The results suggest that using an experimental surrogate-ligand complex is often superior over homology models, and only at low sequence identity to the closest homologue AlphaFold2 structures show an equal performance. The generally high fluctuation of receiver operating characteristic area under the curve values obtained for different homology models suggests that multiple combinations of docking programs and homology models should be tested prior to prospective virtual screenings, and in some cases post-processing of crude models might be necessary.

Antitumor activity of a pexidartinib bioisostere inhibiting CSF1 production and CSF1R kinase activity in human hepatocellular carcinoma

Macrophage colony-stimulating factor (M-CSF, also known as CSF1) in tumor tissues stimulates tumor growth and tumor-induced angiogenesis through an autocrine and paracrine action on CSF1 receptor (CSF1R). In the present study, novel bioisosteres of pexidartinib (1) were synthesized and evaluated their inhibitory activities against CSF1R kinase and tumor growth. Among newly synthesized bioisosteres, compound 3 showed the highest inhibition (95.1%) against CSF1R tyrosine kinase at a fixed concentration (1 μM). The half maximal inhibitory concentration (IC₅₀) of pexidartinib (1) and compound 3 was 2.7 and 57.8 nM, respectively. Unlike pexidartinib (1), which cross-reacts to three targets with structural homology, such as CSF1R, c-KIT, and FLT3, compound 3 inhibited CSF1R, c-KIT, but not FLT3, indicating compound 3 may be a more selective CSF1R inhibitor than pexidartinib (1). The inhibitory effect of compound 3 on the proliferation of various cancer cell lines was the strongest in U937 cells followed by THP-1 cells. In the case of cancer cell lines derived from solid tumors, the anti-proliferative activity of compound 3 was weaker than pexidartinib (1), except for Hep3B. However, compound 3 was safer than pexidartinib (1) in terminally differentiated normal cells such as macrophages. Pexidartinib (1) and compound 3 suppressed the production of CSF1 in Hep3B liver cancer cells as well as in the co-culture of Hep3B cells and macrophages. Also, pexidartinib (1) and compound 3 decreased the population ratio of the M2/M1 phenotype and inhibited their migration. Importantly, compound 3 preferentially inhibited M2 phenotype over M1, and the effect was about 4 times greater than that of pexidartinib (1). In addition, compound 3 inhibited maintenance of cancer stem cell population. In a chick chorioallantoic membrane (CAM) tumor model implanted with Hep3B cells, tumor growth and tumor-induced angiogenesis were significantly blocked by compound 3 to a similar extent as pexidartinib (1). Overall, compound 3, a bioisostere of pexidartinib, is an effective dual inhibitor to block CSF1R kinase and CSF1 production, resulting in significant inhibition of tumor growth.

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model's specificity, including adding Grouping Cross Merge (GCM) modules at the model's jump joints to strengthen the model's feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids.

Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction

MOTIVATION

Compound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two types of models have accomplished promising results in exploiting molecular information: graph convolutional neural networks that construct a learned molecular representation from a graph structure (atoms and bonds), and neural networks that can be applied to compute on descriptors or fingerprints of molecules. However, the superiority of one method over the other is yet to be determined. Modern studies have endeavored to aggregate information that is extracted from compounds and proteins to form the CPI task. Nonetheless, these approaches have used a simple concatenation to combine them, which cannot fully capture the interaction between such information.

RESULTS

We propose the Perceiver CPI network, which adopts a cross-attention mechanism to improve the learning ability of the representation of drug and target interactions and exploits the rich information obtained from extended-connectivity fingerprints to improve the performance. We evaluated Perceiver CPI on three main datasets, Davis, KIBA and Metz, to compare the performance of our proposed model with that of state-of-the-art methods. The proposed method achieved satisfactory performance and exhibited significant improvements over previous approaches in all experiments.

AVAILABILITY AND IMPLEMENTATION

Perceiver CPI is available at https://github.com/dmis-lab/PerceiverCPI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening

In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.

Structure-Based Discovery of Negative Allosteric Modulators of the Metabotropic Glutamate Receptor 5

Recently determined structures of class C G protein-coupled receptors (GPCRs) revealed the location of allosteric binding sites and opened new opportunities for the discovery of novel modulators. In this work, molecular docking screens for allosteric modulators targeting the metabotropic glutamate receptor 5 (mGlu₅) were performed. The mGlu₅ receptor is activated by the main excitatory neurotransmitter of the nervous central system, L-glutamate, and mGlu₅ receptor activity can be allosterically modulated by negative or positive allosteric modulators. The mGlu₅ receptor is a promising target for the treatment of psychiatric and neurodegenerative diseases, and several allosteric modulators of this GPCR have been evaluated in clinical trials. Chemical libraries containing fragment- (1.6 million molecules) and lead-like (4.6 million molecules) compounds were docked to an allosteric binding site of mGlu₅ identified in X-ray crystal structures. Among the top-ranked compounds, 59 fragments and 59 lead-like compounds were selected for experimental evaluation. Of these, four fragment- and seven lead-like compounds were confirmed to bind to the allosteric site with affinities ranging from 0.43 to 8.6 μM, corresponding to a hit rate of 9%. The four compounds with the highest affinities were demonstrated to be negative allosteric modulators of mGlu₅ signaling in functional assays. The results demonstrate that virtual screens of fragment- and lead-like chemical libraries have complementary advantages and illustrate how access to high-resolution structures of GPCRs in complex with allosteric modulators can accelerate lead discovery.

DrugRep: an automatic virtual screening server for drug repurposing

Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ₁-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.

Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments

A multi-target small molecule modulator is advantageous for treating complicated diseases such as cancers. However, the strategy and application for discovering a multi-target modulator have been less reported. This study presents the dual inhibitors for kinase and carbonic anhydrase (CA) predicted by machine learning (ML) classifiers, and validated by biochemical and biophysical experiments. ML trained by CA I and CA II inhibitor molecular fingerprints predicted candidates from the protein-specific bioactive molecules approved or under clinical trials. For experimental tests, three sulfonamide-containing kinase inhibitors, 5932, 5946, and 6046, were chosen. The enzyme assays with CA I, CA II, CA IX, and CA XII have allowed the quantitative comparison in the molecules’ inhibitory activities. While 6046 inhibited weakly, 5932 and 5946 exhibited potent inhibitions with 100 nM to 1 μM inhibitory constants. The ML screening was extended for finding CAs inhibitors of all known kinase inhibitors. It found XMU-MP-1 as another potent CA inhibitor with an approximate 30 nM inhibitory constant for CA I, CA II, and CA IX. Differential scanning fluorimetry confirmed the direct interaction between CAs and small molecules. Cheminformatics studies, including docking simulation, suggest that each molecule possesses two separate functional moieties: one for interaction with kinases and the other with CAs.

Hydroxamic Acid as a Potent Metal-Binding Group for Inhibiting Tyrosinase

Tyrosinase, a metalloenzyme containing a dicopper cofactor, plays a central role in synthesizing melanin from tyrosine. Many studies have aimed to identify small-molecule inhibitors of tyrosinase for pharmaceutical, cosmetic, and agricultural purposes. In this study, we report that hydroxamic acid is a potent metal-binding group for interacting with dicopper atoms, thereby inhibiting tyrosinase. Hydroxamate-containing molecules, including anticancer drugs targeting histone deacetylase, vorinostat and panobinostat, significantly inhibited mushroom tyrosinase, with inhibitory constants in the submicromolar range. Of the tested molecules, benzohydroxamic acid was the most potent. Its inhibitory constant of 7 nM indicates that benzohydroxamic acid is one of the most potent tyrosinase inhibitors. Results from differential scanning fluorimetry revealed that direct binding mediates inhibition. The enzyme kinetics were studied to assess the inhibitory mechanism of the hydroxamate-containing molecules. Experiments with B16F10 cell lysates confirmed that the new inhibitors are inhibitory against mammalian tyrosinase. Docking simulation data revealed intermolecular contacts between hydroxamate-containing molecules and tyrosinase.

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.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

New Inhibitors of Laccase and Tyrosinase by Examination of Cross-Inhibition between Copper-Containing Enzymes

Coppers play crucial roles in the maintenance homeostasis in living species. Approximately 20 enzyme families of eukaryotes and prokaryotes are known to utilize copper atoms for catalytic activities. However, small-molecule inhibitors directly targeting catalytic centers are rare, except for those that act against tyrosinase and dopamine-β-hydroxylase (DBH). This study tested whether known tyrosinase inhibitors can inhibit the copper-containing enzymes, ceruloplasmin, DBH, and laccase. While most small molecules minimally reduced the activities of ceruloplasmin and DBH, aside from known inhibitors, 5 of 28 tested molecules significantly inhibited the function of laccase, with the K_i values in the range of 15 to 48 µM. Enzyme inhibitory kinetics classified the molecules as competitive inhibitors, whereas differential scanning fluorimetry and fluorescence quenching supported direct bindings. To the best of our knowledge, this is the first report on organic small-molecule inhibitors for laccase. Comparison of tyrosinase and DBH inhibitors using cheminformatics predicted that the presence of thione moiety would suffice to inhibit tyrosinase. Enzyme assays confirmed this prediction, leading to the discovery of two new dual tyrosinase and DBH inhibitors.

The extracellular region of bovine milk butyrophilin exhibits closer structural similarity to human myelin oligodendrocyte glycoprotein than to immunological BTN family receptors

Bovine butyrophilin (BTN1A1) is an abundant type I transmembrane glycoprotein exposed on the surface of milk fat globules. We have solved the crystal structure of its extracellular region via multiple wavelength anomalous dispersion after incorporation of selenomethionine into the bacterially produced protein. The butyrophilin ectodomain exhibits two subdomains with immunoglobulin fold, each comprising a β-sandwich with a central disulfide bridge as well as one N-linked glycosylation. The fifth Cys residue at position 193 is unpaired and prone to forming disulfide crosslinks. The apparent lack of a ligand-binding site or receptor activity suggests a function predominantly as hydrophilic coat protein to prevent coagulation of the milk fat droplets. While there is less structural resemblance to members of the human butyrophilin family such as BTN3A, which play a role as immune receptors, the N-terminal bovine butyrophilin subdomain shows surprising similarity to the human myelin oligodendrocyte glycoprotein, a protein exposed on the surface of myelin sheaths. Thus, our study lends structural support to earlier hypotheses of a correlation between the consumption of cow milk and prevalence of neurological autoimmune diseases and may offer guidance for the breeding of cattle strains that express modified butyrophilin showing less immunological cross-reactivity.

Structure-Based Design of First-Generation Small Molecule Inhibitors Targeting the Catalytic Pockets of AID, APOBEC3A, and APOBEC3B

Activation-induced cytidine deaminase (AID) initiates antibody diversification by mutating immunoglobulin loci in B lymphocytes. AID and related APOBEC3 (A3) enzymes also induce genome-wide mutations and lesions implicated in tumorigenesis and tumor progression. The most prevalent mutation signatures across diverse tumor genomes are attributable to the mistargeted mutagenic activities of AID/A3s. Thus, inhibiting AID/A3s has been suggested to be of therapeutic benefit. We previously used a computational-biochemical approach to gain insight into the structure of AID’s catalytic pocket, which resulted in the discovery of a novel type of regulatory catalytic pocket closure that regulates AID/A3s that we termed the “Schrodinger’s CATalytic pocket”. Our findings were subsequently confirmed by direct structural studies. Here, we describe our search for small molecules that target the catalytic pocket of AID. We identified small molecules that inhibit purified AID, AID in cell extracts, and endogenous AID of lymphoma cells. Analogue expansion yielded derivatives with improved potencies. These were found to also inhibit A3A and A3B, the two most tumorigenic siblings of AID. Two compounds exhibit low micromolar IC₅₀ inhibition of AID and A3A, exhibiting the strongest potency for A3A. Docking suggests key interactions between their warheads and residues lining the catalytic pockets of AID, A3A, and A3B and between the tails and DNA-interacting residues on the surface proximal to the catalytic pocket opening. Accordingly, mutants of these residues decreased inhibition potency. The chemistry and abundance of key stabilizing interactions between the small molecules and residues within and immediately outside the catalytic pockets are promising for therapeutic development.

Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach

Here, drug repurposing and molecular docking were employed to screen approved MPP inhibitors and their derivatives to suggest a specific therapeutic agent for the treatment of COVID-19. The approved MPP inhibitors against HIV and HCV were prioritized, while RNA dependent RNA Polymerase (RdRp) inhibitor remdesivir including Favipiravir, alpha-ketoamide were studied as control groups. The target drug surface hotspot was also investigated through the molecular docking technique. Molecular dynamics was performed to determine the binding stability of docked complexes. Absorption, distribution, metabolism, and excretion analysis was conducted to understand the pharmacokinetics and drug-likeness of the screened MPP inhibitors. The results of the study revealed that Paritaprevir (-10.9 kcal/mol) and its analog (CID 131982844) (-16.3 kcal/mol) showed better binding affinity than the approved MPP inhibitors compared in this study, including remdesivir, Favipiravir, and alpha-ketoamide. A comparative study among the screened putative MPP inhibitors revealed that the amino acids T25, T26, H41, M49, L141, N142, G143, C145, H164, M165, E166, D187, R188, and Q189 are at potentially critical positions for being surface hotspots in the MPP of SARS-CoV-2. The top 5 predicted drugs (Paritaprevir, Glecaprevir, Nelfinavir, and Lopinavir) and the topmost analog showed conformational stability in the active site of the SARS-CoV-2 MP protein. The study also suggested that Paritaprevir and its analog (CID 131982844) might be effective against SARS-CoV-2. The current findings are limited to in silico analysis and lack in vivo efficacy testing; thus, we strongly recommend a quick assessment of Paritaprevir and its analog (CID 131982844) in a clinical trial.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Ocimum basilicum (kemangi) intervention on powder and microencapsulated Spirulina platensis and its bioactive molecules

Background:  Spirulina platensis contains several bioactive molecules such as phenol, flavonoid and phycocyanin pigments. This study unveils total phenol, flavonoid, antioxidant activity, phycocyanin content and evaluated encapsulation efficiency from  Ocimum basilicum intervention on  S. platensis. O. basilicum intervention aims to reduce unpleasant odors from  S. platensis that will increase consumption and increase bioactive compounds.   Methods: The intervention was carried out by soaking a  S. platensis control sample (SP) in  O. basilicum with a ratio of 1:4 (w/v) and it was then dried (DSB) and microencapsulated by freeze drying methods (MSB) using a combination of maltodextrin and gelatin. Total flavonoid and phenolic analysis with curve fitting analysis used a linear regression approach. Antioxidant activity of samples was analysed with the 2,2'-azino-bis-3-3thylbenzthiazoline-6-sulphonic acid (ABTS) method. Data were analysed using ANOVA at significance level (p < 0.05) followed by Tukey test models using SPSS v.22.  Results: The result of this study indicated that  O. basilicum intervention treatment (DSB) has the potential to increase bioactive compounds such as total phenol, antioxidant activity and phycocyanin, and flavonoid content. Intervention of  O. basilicum on  S. platensis (DSB) significantly increases total phenol by 48.7% and phycocyanin by 40.7%. This is due to the phenol and azulene compounds in  O. basilicum which have a synergistic effect on phenol and phycocyanin in  S. platensis. Microencapsulation using a maltodexrin and gelatin coating is effective in phycocyanin protection with an encapsulation efficiency value of 71.58%.   Conclusion: The intervention of  O. basilicum on  S. platensis improved the total phenol and phycocyanin content and there is potential for a pharmaceutical product.

Ocimum basilicum (kemangi) intervention on powder and microencapsulated Spirulina platensis and its bioactive molecules

Background:  Spirulina platensis contains several bioactive molecules such as phenol, flavonoid and phycocyanin pigments. This study unveils total phenol, flavonoid, antioxidant activity, phycocyanin content and evaluated encapsulation efficiency from  Ocimum basilicum intervention on  S. platensis. O. basilicum intervention aims to reduce unpleasant odors from  S. platensis that will increase consumption and increase bioactive compounds.   Methods: The intervention was carried out by soaking a  S. platensis control sample (SP) in  O. basilicum with a ratio of 1:4 (w/v) and it was then dried (DSB) and microencapsulated by freeze drying methods (MSB) using a combination of maltodextrin and gelatin. Total flavonoid and phenolic analysis with curve fitting analysis used a linear regression approach. Antioxidant activity of samples was analysed with the 2,2'-azino-bis-3-3thylbenzthiazoline-6-sulphonic acid (ABTS) method. Data were analysed using ANOVA at significance level (p < 0.05) followed by Tukey test models using SPSS v.22.  Results: The result of this study indicated that  O. basilicum intervention treatment (DSB) has the potential to increase bioactive compounds such as total phenol, antioxidant activity and phycocyanin, and flavonoid content. Intervention of  O. basilicum on  S. platensis (DSB) significantly increases total phenol by 49.5% and phycocyanin by 40.7%. This is due to the phenol and azulene compounds in  O. basilicum which have a synergistic effect on phenol and phycocyanin in  S. platensis. Microencapsulation using a maltodexrin and gelatin coating is effective in phycocyanin protection and antioxidant activity with an encapsulation efficiency value of 71.58% and 80.5%.   Conclusion: The intervention of  O. basilicum on  S. platensis improved the total phenol and phycocyanin content and there is potential for a pharmaceutical product for a functional food and pharmaceutical product.

Ocimum basilicum (kemangi) intervention on powder and microencapsulated Spirulina platensis and its bioactive molecules

Background:  Spirulina platensis contains several bioactive molecules such as phenol, flavonoid and phycocyanin pigments. This study unveils total phenol, flavonoid, antioxidant activity, phycocyanin content and evaluated encapsulation efficiency from  Ocimum basilicum intervention on  S. platensis. O. basilicum intervention aims to reduce unpleasant odors from  S. platensis that will increase consumption and increase bioactive compounds.   Methods: The intervention was carried out by soaking a  S. platensis control sample (SP) in  O. basilicum with a ratio of 1:4 (w/v) and it was then dried (DSB) and microencapsulated by freeze drying methods (MSB) using a combination of maltodextrin and gelatin. Total flavonoid and phenolic analysis with curve fitting analysis used a linear regression approach. Antioxidant activity of samples was analysed with the 2,2'-azino-bis-3-3thylbenzthiazoline-6-sulphonic acid (ABTS) method. Data were analysed using ANOVA at significance level (p < 0.05) followed by Tukey test models using SPSS v.22.  Results: The result of this study indicated that  O. basilicum intervention treatment (DSB) has the potential to increase bioactive compounds such as total phenol, antioxidant activity and phycocyanin, and flavonoid content. Intervention of  O. basilicum on  S. platensis (DSB) significantly increases total phenol by 49.5% and phycocyanin by 40.7%. This is due to the phenol and azulene compounds in  O. basilicum which have a synergistic effect on phenol and phycocyanin in  S. platensis. Microencapsulation using a maltodexrin and gelatin coating is effective in phycocyanin protection and antioxidant activity with an encapsulation efficiency value of 71.58% and 80.5%.   Conclusion: The intervention of  O. basilicum on  S. platensis improved the total phenol and phycocyanin content and there is potential for a pharmaceutical product for a functional food and pharmaceutical product.

Molecular docking identification for the efficacy of some zinc complexes with chloroquine and hydroxychloroquine against main protease of COVID-19

Vast amount of research has been recently conducted to discover drugs for efficacious treatment of corona virus disease 2019 (COVID-19). The ambiguity about using Chloroquine/ Hydroxychloroquine to treat this illness was a springboard towards new methods for improving the adequacy of these drugs. The effective treatment of COVID-19 using Zinc complexes as add-on to Chloroquine/ Hydroxychloroquine has received major attention in this context. The current studies have shed a light on molecular docking and molecular dynamics methodologies as powerful techniques in establishing therapeutic strategies to combat COVID-19 pandemic. We are proposing some zinc compounds coordination to Chloroquine/ Hydroxychloroquine in order to enhance their activity. The molecular docking calculations showed that Zn(QC)Cl2(H2O) has the least binding energy -7.70 Kcal /mol then Zn(HQC)Cl2(H2O) -7.54 Kcal /mol. The recorded hydrogen bonds were recognized in the strongest range of H Bond category distances. Identification of binding site interactions revealed that the interaction of Zn(QC)Cl2(H2O)with the protease of COVID-19 results in three hydrogen bonds, while Zn(HQC)Cl2(H2O) exhibited a strong binding to the main protease receptor by forming eight hydrogen bonds. The dynamic behavior of the proposed complexes was revealed by molecular dynamics simulations. The outcomes obtained from Molecular dynamics calculations approved the stability of Mpro-Zn(CQ/HCQ)Cl2H2O systems. These findings recommend Zn (CQ) Cl2H2O and Zn (HCQ) Cl2H2O as potential inhibitors for COVID-19 Mpro.

Identification of Potential HCV Inhibitors Based on the Interaction of Epigallocatechin-3-Gallate with Viral Envelope Proteins

Hepatitis C is affecting millions of people around the globe annually, which leads to death in very high numbers. After many years of research, hepatitis C virus (HCV) remains a serious threat to the human population and needs proper management. The in silico approach in the drug discovery process is an efficient method in identifying inhibitors for various diseases. In our study, the interaction between Epigallocatechin-3-gallate, a component of green tea, and envelope glycoprotein E2 of HCV is evaluated. Epigallocatechin-3-gallate is the most promising polyphenol approved through cell culture analysis that can inhibit the entry of HCV. Therefore, various in silico techniques have been employed to find out other potential inhibitors that can behave as EGCG. Thus, the homology modelling of E2 protein was performed. The potential lead molecules were predicted using ligand-based as well as structure-based virtual screening methods. The compounds obtained were then screened through PyRx. The drugs obtained were ranked based on their binding affinities. Furthermore, the docking of the topmost drugs was performed by AutoDock Vina, while its 2D interactions were plotted in LigPlot+. The lead compound mms02387687 (2-[[5-[(4-ethylphenoxy) methyl]-4-prop-2-enyl-1,2,4-triazol-3-yl] sulfanyl]-N-[3(trifluoromethyl) phenyl] acetamide) was ranked on top, and we believe it can serve as a drug against HCV in the future, owing to experimental validation.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Synthesis and evaluation of tiaprofenic acid-derived UCHL5 deubiquitinase inhibitors

The ubiquitin-proteasome system (UPS) plays an important role in maintaining protein homeostasis by degrading intracellular proteins. In the proteasome, poly-ubiquitinated proteins are deubiquitinated by three deubiquitinases (DUBs) associated with 19S regulatory particle before degradation via 20S core particle. Ubiquitin carboxyl-terminal hydrolase L5 (UCHL5) is one of three proteasome-associated DUBs that control the fate of ubiquitinated substrates implicated in cancer survival and progression. In this study, we have performed virtual screening of an FDA approved drug library with UCHL5 and discovered tiaprofenic acid (TA) as a potential binder. With molecular docking analysis and in-vitro DUB assay, we have designed, synthesized, and evaluated a series of TA derivatives for inhibition of UCHL5 activity. We demonstrate that one TA derivative, TAB2, acts as an inhibitor of UCHL5.

Novel substituted N-benzyl(oxotriazinoindole) inhibitors of aldose reductase exploiting ALR2 unoccupied interactive pocket

Recently we have developed novel oxotriazinoindole inhibitors (OTIs) of aldose reductase (ALR2), characterized by high efficacy and selectivity. Herein we describe novel OTI derivatives design of which is based on implementation of additional intermolecular interactions within an unoccupied pocket of the ALR2 enzyme. Four novel derivatives, OTI-(7-10), of the previously developed N-benzyl(oxotriazinoindole) inhibitor OTI-6 were synthetized and screened. All of them revealed 2 to 6 times higher ALR2 inhibitory efficacy when compared to their non-substituted lead compound OTI-6. Moreover, the most efficient ALR2 inhibitor OTI-7 (IC₅₀ = 76 nM) possesses remarkably high inhibition selectivity (S_F ≥ 1300) in relation to structurally related aldehyde reductase (ALR1). Derivatives OTI-(8-10) bearing the substituents -CONH₂, -COOH and -CH₂OH, possess 2-3 times lower inhibitory efficacy compared to OTI-7, but better than the reference inhibitor OTI-6. Desolvation penalty is suggested as a possible factor responsible for the drop in ALR2 inhibitory efficacy observed for derivatives OTI-(8-10) in comparison to OTI-7.

In-Silico Identified New Natural Sortase A Inhibitors Disrupt S. aureus Biofilm Formation

Sortase A (SrtA) is a membrane-associated enzyme that anchors surface-exposed proteins to the cell wall envelope of Gram-positive bacteria such as Staphylococcus aureus. As SrtA is essential for Gram-positive bacterial pathogenesis but dispensable for microbial growth or viability, SrtA is considered a favorable target for the enhancement of novel anti-infective drugs that aim to interfere with key bacterial virulence mechanisms, such as biofilm formation, without developing drug resistance. Here, we used virtual screening to search an in-house natural compound library and identified two natural compounds, N1287 (Skyrin) and N2576 ((4,5-dichloro-1H-pyrrol-2-yl)-[2,4-dihydroxy-3-(4-methyl-pentyl)-phenyl]-methanone) that inhibited the enzymatic activity of SrtA. These compounds also significantly reduced the growth of S. aureus but possessed moderate mammalian toxicity. Furthermore, S. aureus strains treated with these compounds exhibited reduction in adherence to host fibrinogen, as well as biofilm formation. Hence, these compounds may represent an anti-infective therapy without the side effects of antibiotics.

Peptidomimetics in Silico

Endogenous peptides as part of physiological processes are targets of interest when it comes to finding desirable therapeutics which are able to modulate molecular interactions. The major limits presented by peptides when they are used as drugs have motivated the research of the synthesis of peptidomimetics obtained through chemical modification and the use of in silico approaches. Here recent works on the discovery of peptidomimetics by computational methods are reported. Together with molecular dynamic simulations, the use of pharmacophore research simulations helps to gain insight into and understand the molecular determinants underlying the physiological processes.

Rational molecular targeting of the inter-subunit interaction between human cardiac troponin hcTnC and hcTnI using switch peptide-competitive biogenic medicines

The human cardiac troponin (hcTn) has been implicated in diverse cardiovascular diseases (CDs). The protein function is regulated by the inter-subunit interaction between the N-terminal domain of hcTnC and the C-terminal switch peptide of hcTnI; disruption of the interaction has been recognized as a potential therapeutic strategy for CDs. Here, we report use of biogenic medicines as small-molecule competitors to directly disrupt the protein-protein interaction by competitively targeting the core binding site (CBS) of hcTnC NTD domain. A multistep virtual screening protocol is performed against a biogenic compound library to identify competitor candidates and competition assay is employed to verify the screening results. Consequently, two compounds Collismycin and Compound e are identified as strong competitors (CC50 < 10 μM) with hcTnI for hcTnC CBS site, while other tested compounds are found to have moderate (CC50 = 10-100 μM), low (CC50 > 100 μM) or no (CC50 = N.D.) potency. The competitor ligands are anchored at the core groove of hcTnC CBS site through aromatic and hydrophobic interactions, while few peripheral hydrogen bonds are formed to further confer specificity for domain-compound recognition. These molecular-level findings would benefit from further in vitro and in vivo studies at cellular and animal levels, which can help to practice the ultimate therapeutic purpose.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Identification of a Potential Inhibitor Targeting MurC Ligase of the Drug Resistant Pseudomonas aeruginosa Strain through Structure-Based Virtual Screening Approach and In Vitro Assay

BACKGROUND & OBJECTIVE

Pseudomonas aeruginosa shows resistance to a large number of antibiotics, including carbapenems and third generation cephalosporin. According to the World Health Organization global report published in February 2017, Pseudomonas aeruginosa is on the priority list among resistant bacteria, for which new antibiotics are urgently needed. Peptidoglycan serves as a good target for the discovery of novel antimicrobial drugs.

METHODS

Biosynthesis of peptidoglycan is a multi-step process involving four mur enzymes. Among these enzymes, UDP-N-acetylmuramate-L-alanine ligase (MurC) is considered to be an excellent target for the design of new classes of antimicrobial inhibitors in gram-negative bacteria.

RESULTS

In this study, a homology model of Pseudomonas aeruginosa MurC ligase was generated and used for virtual screening of chemical compounds from the ZINC Database. The best screened inhibitor i.e. N, N-dimethyl-2-oxo-2,3-dihydro-1H-1,3-benzodiazole-5-sulfonamide was then validated experimentally through inhibition assay.

CONCLUSION

The presented results based on combined computational and in vitro analysis open up new horizons for the development of novel antimicrobials against this pathogen.

Mechanism of Action of VP1-001 in cryAB(R120G)-Associated and Age-Related Cataracts

Purpose

We previously identified an oxysterol, VP1-001 (also known as compound 29), that partially restores the transparency of lenses with cataracts. To understand the mechanism of VP1-001, we tested the ability of its enantiomer, ent-VP1-001, to bind and stabilize αB-crystallin (cryAB) in vitro and to produce a similar therapeutic effect in cryAB(R120G) mutant and aged wild-type mice with cataracts. VP1-001 and ent-VP1-001 have identical physicochemical properties. These experiments are designed to critically evaluate whether stereoselective binding to cryAB is required for activity.

Methods

We compared the binding of VP1-001 and ent-VP1-001 to cryAB using in silico docking, differential scanning fluorimetry (DSF), and microscale thermophoresis (MST). Compounds were delivered by six topical administrations to mouse eyes over 2 weeks, and the effects on cataracts and lens refractive measures in vivo were examined. Additionally, lens epithelial and fiber cell morphologies were assessed via transmission electron microscopy.

Results

Docking studies suggested greater binding of VP1-001 into a deep groove in the cryAB dimer compared with ent-VP1-001. Consistent with this prediction, DSF and MST experiments showed that VP1-001 bound cryAB, whereas ent-VP1-001 did not. Accordingly, topical treatment of lenses with ent-VP1-001 had no effect, whereas VP1-001 produced a statistically significant improvement in lens clarity and favorable changes in lens morphology.

Conclusions

The ability of VP1-001 to bind native cryAB dimers is important for its ability to reverse lens opacity in mouse models of cataracts.