A Teach-Discover-Treat Application of ZincPharmer: An Online Interactive Pharmacophore Modeling and Virtual Screening Tool

Scaffold Hopping and Screening for Potent Small Molecule Agonists for GRP94: Implications to Alleviate ER Stress-Associated Pathogenesis

Disparity in the activity of Endoplasmic reticulum (ER) leads to degenerative diseases, mainly associated with protein misfolding and aggregation leading to cellular dysfunction and damage, ultimately contributing to ER stress. ER stress activates the complex network of Unfolded Protein Response (UPR) signaling pathways mediated by transmembrane proteins IRE1, ATF6, and PERK. In addition to UPR, many ER chaperones have evolved to optimize the output of properly folded secretory and membrane proteins. Glucose-regulated protein 94 (GRP94), an ER chaperone of heat shock protein HSP90 family, directs protein folding through interaction with other components of the ER protein folding machinery and assists in ER-associated degradation (ERAD). Activation of GRP94 would increase the efficacy of protein folding machinery and regulate the UPR pathway toward homeostasis. The present study aims to screen for novel agonists for GRP94 based on Core hopping, pharmacophore hypothesis, 3D-QSAR, and virtual screening with small-molecule compound libraries in order to improve the efficiency of native protein folding by enhancing GRP94 chaperone activity, therefore to reduce protein misfolding and aggregation. In this study, we have employed the strategy of small molecule-dependent ER programming to enhance the chaperone activity of GRP94 through scaffold hopping-based screening approach to identify specific GRP94 agonists. New scaffolds generated by altering the cores of NECA, the known GRP94 agonist, were validated by employing pharmacophore hypothesis testing, 3D-QSAR modeling, and molecular dynamics simulations. This facilitated the identification of small molecules to improve the efficiency of native protein folding by enhancing GRP94 activity. High-throughput virtual screening of the selected pharmacophore hypothesis against Selleckchem and ZINC databases retrieved a total of 2,27,081 compounds. Further analysis on docking and ADMET properties revealed Epimedin A, Narcissoside, Eriocitrin 1,2,3,4,6-O-Pentagalloylglucose, Secoisolariciresinol diglucoside, ZINC92952357, ZINC67650204, and ZINC72457930 as potential lead molecules. The stability and interaction of these small molecules were far better than the known agonist, NECA indicating their efficacy in selectively alleviating ER stress-associated pathogenesis. These results substantiate the fact that small molecule-dependent ER reprogramming would activate the ER chaperones and therefore reduce the protein misfolding as well as aggregation associated with ER stress in order to restore cellular homeostasis.

Deciphering Interactions between Potential Inhibitors and the Plasmodium falciparum DHODH Enzyme: A Computational Perspective

Malaria is a parasitic disease that, in its most severe form, can even lead to death. Insect-resistant vectors, insufficiently effective vaccines, and drugs that cannot stop parasitic infestations are making the fight against the disease increasingly difficult. It is known that the enzyme dihydroorotate dehydrogenase (DHODH) is of paramount importance for the synthesis of pyrimidine from the Plasmodium precursor, that is, for its growth and reproduction. Therefore, its blockade can lead to disruption of the parasite's life cycle in the vertebrate host. In this scenario, PfDHODH inhibitors have been considered candidates for a new therapy to stop the parasitic energy source. Given what is known, in this work, we applied molecular fractionation with conjugated caps (MFCC) in the framework of the quantum formalism of density functional theory (DFT) to evaluate the energies of the interactions between the enzyme and the different triazolopyrimidines (DSM483, DMS557, and DSM1), including a complex carrying the mutation C276F. From these results, it was possible to identify the main features of each system, focusing on the wild-type and mutant PfDHODH and examining the major amino acid residues that are part of the four complexes. Our analysis provides new information that can be used to develop new drugs that could prove to be more effective alternatives to present antimalarial drugs.

A specific inhibitor of ALDH1A3 regulates retinoic acid biosynthesis in glioma stem cells

Elevated aldehyde dehydrogenase (ALDH) activity correlates with poor outcome for many solid tumors as ALDHs may regulate cell proliferation and chemoresistance of cancer stem cells (CSCs). Accordingly, potent, and selective inhibitors of key ALDH enzymes may represent a novel CSC-directed treatment paradigm for ALDH+ cancer types. Of the many ALDH isoforms, we and others have implicated the elevated expression of ALDH1A3 in mesenchymal glioma stem cells (MES GSCs) as a target for the development of novel therapeutics. To this end, our structure of human ALDH1A3 combined with in silico modeling identifies a selective, active-site inhibitor of ALDH1A3. The lead compound, MCI-INI-3, is a selective competitive inhibitor of human ALDH1A3 and shows poor inhibitory effect on the structurally related isoform ALDH1A1. Mass spectrometry-based cellular thermal shift analysis reveals that ALDH1A3 is the primary binding protein for MCI-INI-3 in MES GSC lysates. The inhibitory effect of MCI-INI-3 on retinoic acid biosynthesis is comparable with that of ALDH1A3 knockout, suggesting that effective inhibition of ALDH1A3 is achieved with MCI-INI-3. Further development is warranted to characterize the role of ALDH1A3 and retinoic acid biosynthesis in glioma stem cell growth and differentiation.

Design of Inhibitors for Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH) Enzyme of <i>Leishmania mexicana</i>

BACKGROUND

Leishmaniosis is a neglected tropical disease and glyceraldehyde 3- phosphate dehydrogenase (GAPDH) is a key enzyme in the design of new drugs to fight this disease.

OBJECTIVE

The present study aimed to evaluate potential inhibitors of GAPDH enzyme found in Leishmania mexicana (L. mexicana).

METHODS

A search for novel antileishmanial molecules was carried out based on similarities from the pharmacophoric point of view related to the binding site of the crystallographic enzyme using the ZINCPharmer server. The molecules selected in this screening were subjected to molecular docking and molecular dynamics simulations.

RESULTS

Consensual analysis of the docking energy values was performed, resulting in the selection of ten compounds. These ligand-receptor complexes were visually inspected in order to analyze the main interactions and subjected to toxicophoric evaluation, culminating in the selection of three compounds, which were subsequently submitted to molecular dynamics simulations. The docking results showed that the selected compounds interacted with GAPDH from L. mexicana, especially by hydrogen bonds with Cys166, Arg249, His194, Thr167, and Thr226. From the results obtained from molecular dynamics, it was observed that one of the loop regions, corresponding to the residues 195-222, can be related to the fitting of the substrate at the binding site, assisting in the positioning and the molecular recognition via residues responsible for the catalytic activity.

CONCLUSION

The use of molecular modeling techniques enabled the identification of promising compounds as inhibitors of the GAPDH enzyme from L. mexicana, and the results obtained here can serve as a starting point to design new and more effective compounds than those currently available.

Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach

The ongoing pandemic caused by the novel coronavirus has been the greatest global health crisis since the Spanish flu pandemic of 1918. Thus far, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in over 1 million deaths, and there is no cure or vaccine to date. The recently solved crystal structure of the SARS-CoV-2 main protease has been a major focus for drug-discovery efforts. Here, we present a fragment-guided approach using ZINCPharmer, where 17 active fragments known to bind to the catalytic centre of the SARS-CoV-2 main protease (SARS-CoV-2 M^pro) were used as pharmacophore queries to search the ZINC databases of natural compounds and natural derivatives. This search yielded 134 hits that were then subjected to multiple rounds of in silico analyses, including blind and focused docking against the 3D structure of the main protease. We scrutinised the poses, scores, and protein-ligand interactions of 15 hits and selected 7. The scaffolds of the seven hits were structurally distinct from known inhibitor scaffolds, thus indicating scaffold novelty. Our work presents several novel scaffolds as potential candidates for experimental validation against SARS-CoV-2 M^pro.

Hit identification and drug repositioning of potential non-nucleoside reverse transcriptase inhibitors by structure-based approach using computational tools (part II)

AIDS is a global infection involving several complications and its increasing prevalence every year has prioritized our study. Therapy associated with HIV has led to emergence of multidrug resistance and toxicity. Thus, the development of a potent, affordable and safe anti-HIV drug is a global concern. Among the different targets developed, inhibition of non-nucleoside reverse transcriptase (NNRT) is found to be effective and promising. Etravirine, efavirenz, nevirapine, rilpivirine and delavirdine are the marketed NNRTIs available. This study is focused on computational prediction of hit molecules as well as repurposing of various FDA-approved drugs as potential NNRTIs. A synthetic database from ZINCpharmer, publicly available natural databases of coumarins, chromones and chalcones, and two databases of FDA-approved drugs for repurposing were screened to check for the possibility of these compounds to possess anti-HIV activity. Study utilizes a structure-based approach with the generated pharmacophore of target protein (PDB ID: 3MEC), screening of selected datasets is carried out using the Phase tool of Schrodinger. The top filtered compounds with good fitness score were proceeded to molecular docking studies to study their binding affinity to the target. Energy-based calculations using Prime MM-GBSA of Schrodinger was performed to determine free binding energy of the complexes. Prediction of pharmacokinetic parameters of top compounds is further carried out and reported. All the results obtained from different databases are compiled, interpreted and five molecules were subjected to molecular dynamic studies to further confirm the prediction and identified hit molecules for in vitro screening as potential NNRTIs.Communicated by Ramaswamy H. Sarma.

Predicting protein targets for drug-like compounds using transcriptomics

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).

Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

Cannabinoid Type 1 Receptor (CB1) Ligands with Therapeutic Potential for Withdrawal Syndrome in Chemical Dependents of Cannabis sativa

Cannabis sativa withdrawal syndrome is characterized mainly by psychological symptoms. By using computational tools, the aim of this study was to propose drug candidates for treating withdrawal syndrome based on the natural ligands of the cannabinoid type 1 receptor (CB1). One compound in particular, 2-n-butyl-5-n-pentylbenzene-1,3-diol (ZINC1730183, also known as stemphol), showed positive predictions as a human CB1 ligand and for facile synthetic accessibility. Therefore, ZINC1730183 is a favorable candidate scaffold for further research into pharmacotherapeutic alternatives to treat C. sativa withdrawal syndrome.

Pharmacophore feature-based virtual screening for finding potent GSK-3 inhibitors using molecular docking and dynamics simulations

Glycogen synthase kinase-3 (GSK-3) is a multitasking serine/threonine protein kinase, which is associated with the pathophysiology of several diseases such as diabetes, cancer, psychiatric and neurodegenerative diseases. Tideglusib is a potent, selective, and irreversible GSK-3 inhibitor that has been investigated in phase II clinical trials for the treatment of progressive supranuclear palsy and Alzheimer's disease. In the present study, we performed pharmacophore feature-based virtual screening for identifying potent targetspecific GSK-3 inhibitors. We found 64 compounds that show better GSK-3 binding potentials compared with those of Tideglusib. We further validated the obtained binding potentials by performing 20-ns molecular dynamics simulations for GSK-3 complexed with Tideglusib and with the best compound found via virtual screening in this study. Several interesting molecular-level interactions were identified, including a covalent interaction with Cys199 residue at the entrance of the GSK-3 active site. These findings are expected to play a crucial role in the binding of target-specific GSK-3 inhibitors.

Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.