Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors.

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

Evaluating therapeutic potential of AYUSH-64 constituents against omicron variant of SARS-CoV-2 using ensemble docking and simulations.

The COVID-19 pandemic in the later phase showed the presence of the B.1.1.529 variant of the SARS-CoV-2 designated as Omicron. AYUSH-64 a poly herbal drug developed by Central Council for Research in Ayurvedic Sciences (CCRAS) has been recommended by Ministry of Ayush in asymptomatic, mild to moderate COVID-19 patients. One of the earlier, in-silico study has shown the binding of the constituents of AYUSH-64 to the main protease (Mpro) of the SARS-CoV-2. This study enlisted four phytochemicals of AYUSH-64, which were found to have significant binding with the Mpro. In continuation to the same, the current study proposes to understand the binding of these four phytochemicals to main protease (Mpro) and receptor binding domain (RBD) of spike protein of the Omicron variant. An enhanced molecular docking methodology, namely, ensemble docking has been used to find the most efficiently binding phytochemical. Using molecular dynamics (MD) simulations and clustering approach it was observed that the Mpro and RBD Spike of Omicron variant of SARS-CoV-2 in complex with human ACE2 tends to attain 4 and 8 conformational respectively. Based on the docking studies, the best binding phytochemical of the AYUSH-64, akummicine N-oxide was selected for MD simulations. MD simulations of akummicine N-oxide bound to omicron variant of Mpro and RBD Spike-ACE complex was performed. The conformational, interaction and binding energy analysis suggested that the akummicine N-oxide binds well with Mpro and RBD Spike-ACE2 complex. The interaction between RBD Spike and ACE2 was observed to weaken in the presence of akummicine N-oxide. Hence, it can be inferred that, these phytochemicals from AYUSH-64 formulation may have the potential to act against the Omicron variant of SARS-CoV-2.

Computer-aided discovery of novel aryl hydrocarbon receptor ligands to regulate CYP1A1 expression in inflammatory macrophages.

The environmental factor aryl hydrocarbon receptor (AhR), a key protein connecting the external environmental signals (e.g., environmental endocrine disruptor TCDD) to internal cellular processes, is involved in the activation of peripheral macrophages and inflammatory response in human body. Thus, there is widespread interest in finding compounds to anti-inflammatory response in macrophages by targeting human AhR. Here, ensemble docking based-virtual screening was first used to screen a library (~200,000 compounds) against human AhR ligand binding domain (LBD) and 25 compounds were identified as potential inhibitors. Then, 9 out of the 25 ligands were found to down-regulate the mRNA expression of CYP1A1 (a downstream gene of AhR signaling) in AhR overexpressing macrophages. The most potent compound AE-411/41415610 was selected for further study and found to reduce both mRNA and protein expressions level of CYP1A1 in mouse peritoneal macrophage. Moreover, protein chip signal pathway analysis indicated that AE-411/41415610 play a role in regulating JAK-STAT and AKT-mTOR pathways. In sum, the discovered hits with novel scaffolds provided a starting point for future design of more effective AhR-targeted lead compounds to regulate CYP1A1 expression of inflammatory peritoneal macrophages.

In Silico Search for Drug Candidates Targeting the PAX8-PPARγ Fusion Protein in Thyroid Cancer.

The PAX8/PPARγ rearrangement, producing the PAX8-PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson-Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

An Ensemble Docking Approach for Analyzing and Designing Aptamer Heterodimers Targeting VEGF165.

Vascular endothelial growth factor 165 (VEGF165) is a prominent isoform of the VEGF-A protein that plays a crucial role in various angiogenesis-related diseases. It is homodimeric, and each of its monomers is composed of two domains connected by a flexible linker. DNA aptamers, which have emerged as potent therapeutic molecules for many proteins with high specificity and affinity, can also work for VEGF165. A DNA aptamer heterodimer composed of monomers of V7t1 and del5-1 connected by a flexible linker (V7t1:del5-1) exhibits a greater binding affinity with VEGF165 compared to either of the two monomers alone. Although the structure of the complex formed between the aptamer heterodimer and VEGF165 is unknown due to the highly flexible linkers, gaining structural information will still be valuable for future developments. Toward this end of accessing structural information, we adopt an ensemble docking approach here. We first obtain an ensemble of structures for both VEGF165 and the aptamer heterodimer by considering both small- and large-scale motions. We then proceed through an extraction process based on ensemble docking, molecular dynamics simulations, and binding free energy calculations to predict the structures of the VEGF165/V7t1:del5-1 complex. Through the same procedures, we reach a new aptamer heterodimer that bears a locked nucleic acid-modified counterpart of V7t1, namely RNV66:del5-1, which also binds well with VEGF165. We apply the same protocol to the monomeric units V7t1, RNV66, and del5-1 to target VEGF165. We observe that V7t1:del5-1 and RNV66:del5-1 show higher binding affinities with VEGF165 than any of the monomers, consistent with experiments that support the notion that aptamer heterodimers are more effective anti-VEGF165 aptamers than monomeric aptamers. Among the five different aptamers studied here, the newly designed RNV66:del5-1 shows the highest binding affinity with VEGF165. We expect that our ensemble docking approach can help in de novo designs of homo/heterodimeric anti-angiogenic drugs to target the homodimeric VEGF165.

Novel 2-(hydrazinocarbonyl)-3-phenyl-1H-indole-5-sulfonamide based thiosemicarbazides as potent and selective inhibitors of tumor-associated human carbonic anhydrase IX and XII: Synthesis, cytotoxicity, and molecular modelling studies.

In the pursuit of discovering new selective carbonic anhydrase (CA, EC 4.2.1.1) inhibitors, a small collection of novel thiosemicarbazides (5a-5t) were designed and synthesized starting from 2-(hydrazinocarbonyl)-3-phenyl-1H-indole-5-sulfonamide which was evaluated as a potent inhibitor of different CA isoforms in a previous study. The newly synthesized compounds were examined against four human carbonic anhydrases (hCA), namely transmembrane tumor-related hCA IX/XII and cytosolic widespread off-targets hCA I/II. In enzyme inhibition assays, all nineteen compounds display up to ∼340-fold selectivity for hCA IX/XII over off-target isoforms hCA I/II. Four compounds have enzyme inhibition values (Ki) lower than 10 nM against tumor-associated isoforms hCA IX/XII including two compounds in the subnanomolar range (5r and 5s; hCA XII; Ki: 0.69 and 0.87 nM). The potential binding interactions of the most potent compounds against hCA IX and XII, compounds 5s and 5r, respectively, were investigated using ensemble docking and molecular dynamics studies. Cell viability assays using human colorectal adenocarcinoma cell line HT-29 and healthy skin fibroblasts CCD-86Sk show that compound 5e selectively inhibits HT-29 cancer cell proliferation (IC50: 53.32 ± 7.74 µM for HT-29; IC50: 74.64 ± 14.15 µM for CCD-986Sk). Finally, Western blot assays show that compounds 5e and 5r significantly reduce the expression of hCA XII in HT-29 cells. Moreover, 5e shows better cytotoxic activity in hypoxia compared to normoxic conditions. Altogether, the newly designed compounds show stronger inhibition of the tumor-associated hCA IX and XII isoforms and several tested compounds show selective cytotoxicity as well as downregulation of hCA XII expression.

Discovery of novel and potent InhA direct inhibitors by ensemble docking-based virtual screening and biological assays.

Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with KD values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC50 value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with "extended" conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.

Ensemble-based virtual screening of African natural products to target human thymidylate synthase.

Human thymidylate synthase (hTS) is a validated drug target for chemotherapy. A virtual screening experiment was used to prioritize a list of compounds from African Natural Products Databases docked against the orthosteric binding pocket of hTS. Consensus scores of binding affinities from ensemble-based virtual screening, hydrated docking and MM-PBSA calculations ranked compounds NEA4433 and NEA4434 as the best candidates owing to binding affinity scores in the picomolar order, their excellent ADMET profiles and the good stability of the protein-ligand complexes formed. The current study demonstrates the role of water in small molecule binding to hTS in mediating protein-ligand interactions. Similarly, the robust ensemble docking (relaxed scheme complex) ranked NEA4433 and NEA4434 as the best candidates. Furthermore, the best candidates prioritized were shown to strongly interact with the same residues that interacted with hTS substrate and cofactor.

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach.

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.

Identification of Catechins' Binding Sites in Monomeric Aß42 through Ensemble Docking and MD Simulations.

The assembly of the amyloid-ß peptide (Aß) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aß monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aß, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aß42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aß42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aß. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aß.

Designing of 2,3-dihydrobenzofuran derivatives as inhibitors of PDE1B using pharmacophore screening, ensemble docking and molecular dynamics approach.

In recent years, the PDE1B enzyme has become a desirable drug target for the treatment of psychological and neurological disorders, particularly schizophrenia disorder, due to the expression of PDE1B in brain regions involved in volitional behaviour, learning and memory. Although several inhibitors of PDE1 have been identified using different methods, none of these inhibitors has reached the market yet. Thus, searching for novel PDE1B inhibitors is considered a major scientific challenge. In this study, pharmacophore-based screening, ensemble docking and molecular dynamics simulations have been performed to identify a lead inhibitor of PDE1B with a new chemical scaffold. Five PDE1B crystal structures have been utilised in the docking study to improve the possibility of identifying an active compound compared to the use of a single crystal structure. Finally, the structure-activity- relationship was studied, and the structure of the lead molecule was modified to design novel inhibitors with a high affinity for PDE1B. As a result, two novel compounds have been designed that exhibited a higher affinity to PDE1B compared to the lead compound and the other designed compounds.

Repurposing of drug molecules from FDA database against Hepatitis C virus E2 protein using ensemble docking approach.

Hepatitis C virus, a member of the Flaviviridae family and genus Hepacivirus, is an enveloped, positively single stranded RNA virus. Its surface consists of a heterodimer of E1 and E2 proteins which play a crucial role in receptor binding and membrane fusion. In this study we have used in silico virtual screening by utilizing ensemble docking on the approved drugs. These drugs can bind with high efficiency to the 36 prominent conformations of the CD81 binding site clustered from a total of 3 µs MD simulation data on the E2 protein. We started with 9213 compounds from the FDA list of drugs and progressively came down to 5 compounds which have been seen to bind with very high efficiency to not only all the conformations but also the two predicted druggable pockets that encompass the CD81 binding site. MM/PBSA binding energy calculations also point to the highly stable interaction of the compounds to the E2 protein. This study may in future broaden the arsenal of therapeutics for use against HCV infection and lead to more effective care against the virus.

Structural analysis and ensemble docking revealed the binding modes of selected progesterone receptor modulators.

Uterine fibroids (UF) are benign smooth muscle neoplasm of uterus that have a significant impact on a woman's quality of life as they perturb hormonal homeostasis resulting in heavy menstrual bleeding, impaired fertility, pregnancy complications and loss. UF can be surgically removed through invasive procedures, but their recurrence rate is often high. Progesterone receptor (PR) has an imperative role in UF management. Mifepristone, ulipristal acetate (UPA) and asoprisnil (ASO) are some selective progesterone receptor modulators (SPRMs), acts on PR, but due to their side effects in long term use, they were withdrawn from the market. Hence, there is a dire need for novel, highly efficient with least side effects, therapeutics for the treatment of UF. To contribute toward the drug discovery for UF, we made an extensive structural comparison of reported PR crystal structures, also elucidated the binding modes of four existing SPRMs against human PR through ensemble docking approach. Our studies revealed the presence of 5 highly repeating water molecules that has an important role in ligand binding and structural stability. Our ensemble docking and MD simulation revealed that studied ligands have preferential selectivity toward the specific conformation of PR. It is anticipated that our study will be a useful resource to all the drug discovery scientists who are engaged in the identification of lead molecules against UF.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water.

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation.

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to ß-sheet rich conformations.

PyPLIF HIPPOS and Receptor Ensemble Docking Increase the Prediction Accuracy of the Structure-Based Virtual Screening Protocol Targeting Acetylcholinesterase.

In this article, the upgrading process of the structure-based virtual screening (SBVS) protocol targeting acetylcholinesterase (AChE) previously published in 2017 is presented. The upgraded version of PyPLIF called PyPLIF HIPPOS and the receptor ensemble docking (RED) method using AutoDock Vina were employed to calculate the ensemble protein-ligand interaction fingerprints (ensPLIF) in a retrospective SBVS campaign targeting AChE. A machine learning technique called recursive partitioning and regression trees (RPART) was then used to optimize the prediction accuracy of the protocol by using the ensPLIF values as the descriptors. The best protocol resulting from this research outperformed the previously published SBVS protocol targeting AChE.

Consensus combining outcomes of multiple ensemble dockings: examples using dDAT crystalized complexes.

Docking using different programs provides more reliable information about the interaction of molecules than data obtained in a single program. An exponential consensus ranking (ECR) was developed to combine scoring functions across docking programs differing in efficiencies and scales of measurements. The ECR method was adapted to merge results of re- and cross-dockings (i.e., ensemble docking) made in multiple docking programs. Adapted ECR consisted of four consecutive steps: 1- determination of scoring functions for a ligand with a series of macromolecules in multiple docking programs; 2- ranking of the scoring functions per macromolecule in each program; 3- combining the ranking across the programs creating a ranking per macromolecule; 4- averaging the ranking per macromolecule creating a final ranking. This last step incorporated the heterogeneity of the macromolecule conformations in the consensual score. The final ranking based on the adapted ECR represents relative affinity of a series of ligands to a macromolecule on average. As an example, a ranking of the average affinity of antidepressants and other ligands to the Drosophila melanogaster dopamine transporter (dDAT) was presented. Adapted ECR generated a ranking similar to that based on the affinity constant of each ligand obtained from the literature. • A final ranking of the average relative affinity of different ligands to the dDAT. • A consensus method combining multiple ensemble dockings. • A complete protocol to make re-docking and cross-docking using Autodock Vina, Gold and DockThor.

Multifunctional Analysis of Chia Seed (Salvia hispanica L.) Bioactive Peptides Using Peptidomics and Molecular Dynamics Simulations Approaches.

Chia seed peptides (CSP) can be a source of multifunctional biopeptides to treat non-communicable diseases. However, interactions and binding affinity involved in targeting specific receptors remains unexplored. In this study, molecular simulation techniques were used as virtual screening of CSP to determine drug-like candidates using a multi-target-directed ligand approach. CSP fraction with the best bioactivities in vitro was sequenced. Then, a prediction model was built using physicochemical descriptors (hydrophobicity, hydrophilicity, intestinal stability, antiangiogenic, antihypertensive, and anti-inflammatory) to calculate potential scores and rank possible biopeptides. Furthermore, molecular dynamics simulations (MDS) and ensemble molecular docking analysis were carried out using four human protein targets (ACE, angiotensin converting enzyme; VEGF, vascular endothelial growth factor; GLUC, glucocorticoid and MINC, mineralocorticoid receptors). Five known-sequence peptides (NNVFYPF, FNIVFPG, SRPWPIDY, QLQRWFR, GSRFDWTR) and five de novo peptides (DFKF, DLRF, FKAF, FRSF, QFRF) had the lowest energy score and higher affinity for ACE and VEGF. The therapeutic effects of these selected peptides can be related to the inhibition of the enzymes involved in angiogenesis and hypertension, due to formation of stable complexes with VEGF and ACE binding sites, respectively. The application of MDS is a good resource for identifying bioactive peptides for future experimental validation.

Towards discovery of inhibitors of the undecaprenyl-pyrophosphate phosphatase BacA by virtual high-throughput screening.

Increasing resistance to common antibiotics is becoming a major challenge that requires the development of new antibacterial agents. Peptidoglycan is an essential heteropolymer of the bacterial envelope that ensures the integrity and shape of all bacteria and is also an important target for antibiotics. The biosynthesis of peptidoglycan depends on a lipid carrier, undecaprenyl phosphate. As a byproduct of peptidoglycan polymerization, the lipid carrier is released as undecaprenyl pyrophosphate, which must be recycled to allow new polymerization cycles. To this end, it undergoes a dephosphorylation process catalyzed by the membrane phosphatase BacA, which is specific and highly conserved in bacteria. In the present study, we identified small molecules displaying inhibitory potency towards BacA. We began by preparing a commercial compound library, followed by high-throughput virtual screening by ensemble docking using the 3D structure of BacA and molecular dynamics snapshots to account for the flexibility of the protein. Of 83 compounds computationally selected and tested in a biochemical assay, one sulfamoylthiophene molecule showed significant inhibition of the undecaprenyl pyrophosphate dephosphorylation activity catalyzed by BacA. Subsequently, an additional 33 scaffold analogs were selected and acquired, of which 6 compounds exhibited BacA inhibition. The IC50 values of these compounds ranged from 42 to 366 µM. In addition, significant antibacterial activity against Escherichia coli was observed in TolC/PAP2-depleted strains. We believe that the overall strategy followed in this study and the identified class of inhibitors provide a solid foundation for the further development of potent BacA-targeted inhibitors and the discovery of novel antibacterial compounds.

Benchmarking ensemble docking methods in D3R Grand Challenge 4.

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.