High Throughput Virtual Screening Studies Research Articles

Identification of FDA-approved drugs with triple targeting mode of action for the treatment of monkeypox: a high throughput virtual screening study.

According to the Center for Disease Control and Prevention, as of August 23, 94 countries had confirmed 42,954 Monkeypox Virus cases. As specific monkeypox drugs are not yet developed, the treatment depends on repurposed FDA-approved drugs. According to a recent study, the Monkeypox outbreak is caused by a strain with a unique mutation, raising the likelihood that the virus will develop resistance to current drugs by acquiring mutations in the targets of currently used drugs. The probability of multiple mutations in two or more drug targets at a time is always low than mutation in a single drug target. Therefore, we identified 15 triple-targeting FDA-approved drugs that can inhibit three viral targets, including topoisomerase1, p37, and thymidylate kinase, using high throughput virtual screening approach. Further, the molecular dynamics simulation analysis of the top hits such as Naldemedine and Saquinavir with their respective targets reveals the formation of stable conformational changes of the ligand-protein complexes inside the dynamic biological environment. We suggest further research on these triple-targeting molecules to develop an effective therapy for the currently spreading Monkeypox.

Identification of FDA-Approved Drugs with Triple Targeting Mode of Action for the Treatment of Monkeypox: A High Throughput Virtual Screening Study

Identification of FDA-Approved Drugs with Triple Targeting Mode of Action for the Treatment of Monkeypox: A High Throughput Virtual Screening Study

True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better.

Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, ΔvinaRF20, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.

High throughput virtual screening reveals SARS-CoV-2 multi-target binding natural compounds to lead instant therapy for COVID-19 treatment

The present-day world is severely suffering from the recently emerged SARS-CoV-2. The lack of prescribed drugs for the deadly virus has stressed the likely need to identify novel inhibitors to alleviate and stop the pandemic. In the present high throughput virtual screening study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), and ADME properties to screen natural compounds. It has been documented that many natural compounds display antiviral activities, including anti–SARS-CoV effect. The present study deals with compounds of Natural Product Activity and Species Source (NPASS) database with known biological activity that probably impedes the activity of six essential enzymes of the virus. Promising drug-like compounds were identified, demonstrating better docking score and binding energy for each druggable targets. After an extensive screening analysis, three novel multi-target natural compounds were predicted to subdue the activity of three/more major drug targets simultaneously. Concerning the utility of natural compounds in the formulation of many therapies, we propose these compounds as excellent lead candidates for the development of therapeutic drugs against SARS-CoV-2.

Identification of novel serotonin reuptake inhibitors targeting central and allosteric binding sites: A virtual screening and molecular dynamics simulations study

The serotonin (5-hydroxytryptamine, 5HT) transporter (SERT) is a member of neurotransmitter sodium symporter (NSS) family, which maintains neurotransmitter by reuptaking 5HT into synapses. Decrease in serotonin concentrations in synaptic clefts have been reported to cause psychological and neurological disorders. Therefore, inhibition of SERT is a potent strategy for the treatment of related diseases such as depression. In this study, approximately 260,000 small molecules from an available chemical database have been virtually screened both at central and allosteric binding sites of SERT to identify potent novel candidate SERT inhibitors. A set of docking algorithms were used to predict binding modes and energies of compounds. Screening analyses led three top-ranked hit compounds (160234, Otava ID: 7118020138; 159166, Otava ID: 7117171303; and 69419, Otava ID: 118671819) for central binding site (S1) and one compound (93507, Otava ID: 6248262) for allosteric binding site (S2). These promising compounds are then subjected to long multiple molecular dynamics (MD) simulations to elucidate their structural and dynamical profiles at the binding cavities of SERT. Higher predicted binding affinities of identified compounds were also confirmed with binding free energy calculations (MM/GBSA) in comparison with the reference central and allosteric binding site inhibitors, paroxetine (8PR) and escitalopram (68P), respectively. To the best of our knowledge, the present work is the first structure-based high throughput virtual screening study reported using recently revealed crystal structure of SERT for screening inhibitors from chemical databases on S1 and S2 binding sites. Small molecule library screening study yielded candidate compounds both at central and allosteric binding site of SERT, and further experimentation may pave the way for developing novel strong inhibitors.

Human Rab8b Protein as a Cancer Target - An In Silico Study

Testicular cancer develops in one or both of the testicles in young men. Rab8b is a member of the Rab small G protein family, participates in intracellular trafficking events at the site of the adherence junction dynamics in the testis. Overexpression of Rab8b and loss of functioning adherence junction accelerates the tumorigenesis in testis. In the present work, the computer aided high throughput virtual screening studies are applied to identify potent leads for human Rab8b protein. The homology model of Rab8b of 207 amino acid residues chain length was evaluated based on the crystal structure of an appropriate template, and reveals the presence of 6 α-helices and 6 β-strands. The energy of the generated model was minimized and the model was validated using ProSA PROCHECK and ERRAT server tools. The active site was identified using the computational binding site prediction tools like CASTp, efindsite seversand Sitemap of Schrodinger, which show that the residues (GLU33 to GLN60) are important for binding. The molecular interactions of Rab8b with its natural substrate Rabin 8, were examined by in silico protein-protein docking studies using patchDock tool, and the results were corroborated with the active site identified from the computational tools. Virtual screening studies were carried out with ligand databases using Glide docking program of Schrodinger suite. The ligands obtained from XP docking with high Glide score and Glide energy, were subjected to QikProp module to predict their ADME properties. These ligands, based on the pharmacokinetic properties, which are new entities, were considered as novel potent inhibitors in cancer therapy.

Fully Automated Molecular Mechanics Based Induced Fit Protein−Ligand Docking Method

We describe a method for docking a ligand into a protein receptor while allowing flexibility of the protein binding site. The method employs a multistep procedure that begins with the generation of protein and ligand conformations. An initial placement of the ligand is then performed by computing binding site hotspots. This initial placement is followed by a protein side-chain refinement stage that models protein flexibility. The final step of the process is an energy minimization of the ligand pose in the presence of the rigid receptor. Thus the algorithm models flexibility of the protein at two stages, before and after ligand placement. We validated this method by performing docking and cross docking studies of eight protein systems for which crystal structures were available for at least two bound ligands. The resulting rmsd values of the 21 docked protein-ligand complexes showed values of 2 A or less for all but one of the systems examined. The method has two critical benefits for high throughput virtual screening studies. First, no user intervention is required in the docking once the initial binding site selection has been made in the protein. Second, the initial protein conformation generation needs to be performed only once for a given binding region. Also, the method may be customized in various ways depending on the particular scenario in which dockings are being performed. Each of the individual steps of the method is fully independent making it straightforward to explore different variants of the high level workflow to further improve accuracy and performance.

Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors

Among the available methods for predicting free energies of binding of ligands to a protein, the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA) approaches have been validated for a relatively limited number of targets and compounds in the training set. Here, we report the results of an extensive study on a series of 28 inhibitors of aldose reductase with experimentally determined crystal structures and inhibitory activities, in which we evaluate the ability of MM-PBSA and MM-GBSA methods in predicting binding free energies using a number of different simulation conditions. While none of the methods proved able to predict absolute free energies of binding in quantitative agreement with the experimental values, calculated and experimental free energies of binding were significantly correlated. Comparing the predicted and experimental Δ G of binding, MM-PBSA proved to perform better than MM-GBSA, and within the MM-PBSA methods, the PBSA of Amber performed similarly to Delphi. In particular, significant relationships between experimental and computed free energies of binding were obtained using Amber PBSA and structures minimized with a distance-dependent dielectric function. Importantly, while free energy predictions are usually made on large collections of equilibrated structures sampled during molecular dynamics in water, we have found that a single minimized structure is a reasonable approximation if relative free energies of binding are to be calculated. This finding is particularly relevant, considering that the generation of equilibrated MD ensembles and the subsequent free energy analysis on multiple snapshots is computationally intensive, while the generation and analysis of a single minimized structure of a protein–ligand complex is relatively fast, and therefore suited for high-throughput virtual screening studies. At this aim, we have developed an automated workflow that integrates all the necessary steps required to generate structures and calculate free energies of binding. The procedure is relatively fast and able to screen automatically and iteratively molecules contained in databases and libraries of compounds. Taken altogether, our results suggest that the workflow can be a valuable tool for ligand identification and optimization, being able to automatically and efficiently refine docking poses, which sometimes may not be accurate, and rank the compounds based on more accurate scoring functions.