Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4

Identification of potential anti-tumor targets and mechanisms of HuaChanSu injection using network pharmacology and cytological experiments in Breast cancer

HuaChanSu (HCS) or Cinobufacini injection is an aqueous extract of the dried skin of Bufo bufo gargarigans, and has anti-tumor effects. The aim of this study was to evaluate the possible therapeutic effect of HCS against breast cancer (BRCA) using cytology, network pharmacology, and molecular biology approaches. The half-inhibitory concentration (IC50) of HCS in the BRCA cells was determined by cytotoxicity assay, and were accordingly treated with high and low doses HCS in the TUNEL and scratch assays. The potential targets of HCS in the BRCA cells were identified through functional enrichment analysis and protein-protein interaction (PPI) networks, and verified by molecular docking. The expression levels of key signaling pathways-related proteins in HCS-treated BRCA cells by western blotting. HCS inhibited the proliferation and migration of MCF-7 and MDA-MB-231 cells, and induced apoptosis in a dose-dependent manner. Furthermore, we screened 289 core HCS targets against BRCA, which were primarily enriched in the PI3K-AKT, MAPK chemokines, and other. signaling pathways. In addition, PIK3CA, PIK3CD, and MTOR were confirmed as HCS targets by molecular docking. Consistent with this, we observed a reduction in the expression levels of phosphorylated PI3K, AKT, and MTOR in the HCS-treated BRCA cells. Taken together, our findings suggest that HCS inhibits the growth of BRCA cells by targeting the PI3K-AKT pathway, and warrants further investigation as a therapeutic agent for treating patients with BRCA.

Nature-Inspired 1-Phenylpyrrolo[2,1-a]isoquinoline Scaffold for Novel Antiproliferative Agents Circumventing P-Glycoprotein-Dependent Multidrug Resistance

Previous studies have shown that some lamellarin-resembling annelated azaheterocyclic carbaldehydes and related imino adducts, sharing the 1-phenyl-5,6-dihydropyrrolo[2,1-a]isoquinoline (1-Ph-DHPIQ) scaffold, are cytotoxic in some tumor cells and may reverse multidrug resistance (MDR) mediated by P-glycoprotein (P-gp). Herein, several novel substituted 1-Ph-DHPIQ derivatives were synthesized which carry carboxylate groups (COOH, COOEt), nitrile (CN) and Mannich bases (namely, morpholinomethyl derivatives) in the C2 position, as replacements of the already reported aldehyde group. They were evaluated for antiproliferative activity in four tumor cell lines (RD, HCT116, HeLa, A549) and for the ability of selectively inhibiting P-gp-mediated MDR. Lipophilicity descriptors and molecular docking calculations helped us in rationalizing the structure-activity relationships in the P-gp inhibition potency of the investigated 1-Ph-DHPIQs. As a main outcome, a morpholinomethyl Mannich base (8c) was disclosed which proved to be cytotoxic to all the tested tumor cell lines in the low micromolar range (IC50 < 20 μM) and to inhibit in vitro the efflux pumps P-gp and MRP1 responsible for MDR, with IC50s of 0.45 and 12.1 μM, respectively.

Novel 6-alkyl-bridged 4-arylalkylpiperazin-1-yl derivatives of azepino[4,3-b]indol-1(2H)-one as potent BChE-selective inhibitors showing protective effects against neurodegenerative insults

Due to the putative role of butyrylcholinesterase (BChE) in regulation of acetylcholine levels and functions in the late stages of the Alzheimer's disease (AD), the potential of selective inhibitors (BChEIs) has been envisaged as an alternative to administration of acetylcholinesterase inhibitors (AChEIs). Starting from our recent findings, herein the synthesis and in vitro evaluation of cholinesterase (ChE) inhibition of a novel series of some twenty 3,4,5,6-tetrahydroazepino[4,3-b]indol-1(2H)-one derivatives, bearing at the indole nitrogen diverse alkyl-bridged 4-arylalkylpiperazin-1-yl chains, are reported. The length of the spacers, as well as the type of arylalkyl group affected the enzyme inhibition potency and BChE/AChE selectivity. Two compounds, namely 14c (IC50 = 163 nM) and 14d (IC50 = 65 nM), bearing at the nitrogen atom in position 6 a n-pentyl- or n-heptyl-bridged 4-phenethylpiperazin-1-yl chains, respectively, proved to be highly potent mixed-type inhibitors of both equine and human BChE isoforms, showing more than two order magnitude of selectivity over AChE. The study of binding kinetics through surface plasmon resonance (SPR) highlighted differences in their BChE residence times (8 and 47 s for 14c and 14d, respectively). Moreover, 14c and 14d proved to hit other mechanisms known to trigger neurodegeneration underlying AD and other CNS disorders. Unlike 14c, compound 14d proved also capable of inhibiting by more than 60% the in vitro self-induced aggregation of neurotoxic amyloid-β (Aβ) peptide at 100 μM concentration. On the other hand, 14c was slightly better than 14d in counteracting, at 1 and 10 μM concentration, glutamate excitotoxicity, due to over-excitation of NMDA receptors, and hydrogen peroxide-induced oxidative stress assessed in neuroblastoma cell line SH-SY5Y. This paper is dedicated to Prof. Marcello Ferappi, former dean of the Faculty of Pharmacy of the University of Bari, in the occasion of his 90th birthday.

Structure-Based Optimization of Carbendazim-Derived Tubulin Polymerization Inhibitors through Alchemical Free Energy Calculations

Carbendazim derivatives, commonly used as antiparasitic drugs, have shown potential as anticancer agents due to their ability to induce cell cycle arrest and apoptosis in human cancer cells by inhibiting tubulin polymerization. Crystallographic structures of α/β-tubulin multimers complexed with nocodazole and mebendazole, two carbendazim derivatives with potent anticancer activity, highlighted the possibility of designing compounds that occupy both benzimidazole- and colchicine-binding sites. In addition, previous studies have demonstrated that the incorporation of a phenoxy group at position 5/6 of carbendazim increases the antiproliferative activity in cancer cell lines. Despite the significant progress made in identifying new tubulin-targeting anticancer compounds, further modifications are needed to enhance their potency and safety. In this study, we explored the impact of modifying the phenoxy substitution pattern on antiproliferative activity. Alchemical free energy calculations were used to predict the binding free energy difference upon ligand modification and define the most viable path for structure optimization. Based on these calculations, seven compounds were synthesized and evaluated against lung and colon cancer cell lines. Our results showed that compound 5a, which incorporates an α-naphthyloxy substitution, exhibits the highest antiproliferative activity against both cancer lines (SK-LU-1 and SW620, IC50 < 100 nM) and induces morphological changes in the cells associated with mitotic arrest and mitotic catastrophe. Nevertheless, the tubulin polymerization assay showed that 5a has a lower inhibitory potency than nocodazole. Molecular dynamics simulations suggested that this low antitubulin activity could be associated with the loss of the key H-bond interaction with V236. This study provides insights into the design of novel carbendazim derivatives with anticancer activity.

Synthesis, computational and experimental pharmacological studies for (thio)ether-triazine 5-HT6R ligands with noticeable action on AChE/BChE and chalcogen-dependent intrinsic activity in search for new class of drugs against Alzheimer's disease

Alzheimer's disease is becoming a growing problem increasing at a tremendous rate. Serotonin 5-HT6 receptors appear to be a particularly attractive target from a therapeutic perspective, due to their involvement not only in cognitive processes, but also in depression and psychosis. In this work, we present the synthesis and broad biological characterization of a new series of 18 compounds with a unique 1,3,5-triazine backbone, as potent 5-HT6 receptor ligands. The main aim of this research is to compare the biological activity of the newly synthesized sulfur derivatives with their oxygen analogues and their N-demethylated O- and S-metabolites obtained for the first time. Most of the new triazines displayed high affinity (Ki < 200 nM) and selectivity towards 5-HT6R, with respect to 5-HT2AR, 5-HT7R, and D2R, in the radioligand binding assays. For selected, active compounds crystallographic studies, functional bioassays, and ADME-Tox profile in vitro were performed. The exciting novelty is that the sulfur derivatives exhibit an agonistic mode of action contrary to all other compounds obtained to date in this chemical class herein and previously reported. Advanced computational studies indicated that this intriguing functional shift might be caused by presence of chalcogen bonds formed only by the sulfur atom. In addition, the N-demethylated derivatives have emerged highly potent antioxidants and, moreover, show a significant improvement in metabolic stability compared to the parent structures. The cholinesterase study present micromolar inhibitory AChE and BChE activity for both 5-HT6 agonist 19 and potent antagonist 5. Finally, the behavioral experiments of compound 19 demonstrated its antidepressant-like properties and slight ability to improve cognitive deficits, without inducing memory impairments by itself. Described pharmacological properties of both compounds (5 and 19) allow to give a design clue for the development of multitarget compounds with 5-HT6 (both agonist and antagonist)/AChE and/or BChE mechanism in the group of 1,3,5-triazine derivatives.

Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors

Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.

Discovery of Potential Noncovalent Inhibitors of Dehydroquinate Dehydratase from Methicillin-Resistant Staphylococcus aureus through Computational-Driven Drug Design

Bacteria resistance to antibiotics is a concerning global health problem; in this context, methicillin-resistant Staphylococcus aureus (MRSA) is considered as a high priority by the World Health Organization. Furthermore, patients with a positive result for COVID-19 received early antibiotic treatment, a fact that potentially encourages the increase in antibiotic resistance. Therefore, there is an urgency to develop new drugs with molecular mechanisms different from those of the actual treatments. In this context, enzymes from the shikimate pathway, a route absent in humans, such as dehydroquinate dehydratase (DHQD), are considered good targets. In this work, a computer-aided drug design strategy, which involved exhaustive virtual screening and molecular dynamics simulations with MM-PBSA analysis, as well as an in silico ADMETox characterization, was performed to find potential noncovalent inhibitors of DHQD from MRSA (SaDHQD). After filtering the 997 million compounds from the ZINC database, 6700 compounds were submitted to an exhaustive virtual screening protocol. From these data, four molecules were selected and characterized (ZINC000005753647 (1), ZINC000001720488 (2), ZINC000082049768 (3), and ZINC000644149506 (4)). The results indicate that the four potential inhibitors interacted with residues important for substrate binding and catalysis, with an estimated binding free energy like that of the enzyme's substrate. Their ADMETox-predicted properties suggest that all of them support the structural characteristics to be considered good candidates. Therefore, the four compounds reported here are excellent option to be considered for future in vitro studies to design new SaDHQD noncovalent inhibitors and contribute to the search for new drugs against MRSA.

Putting the Puzzle Together To Get the Whole Picture: Molecular Basis of the Affinity of Two Steroid Derivatives to Acetylcholinesterase

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that has no cure because its etiology is still unknown, and its main treatment is the administration of acetylcholinesterase (AChE) inhibitors. The study of the mechanism of action of this family of compounds is critical for the design of new more potent and specific inhibitors. In this work, we study the molecular basis of an uncompetitive inhibitor (compound 1, 2β, 3α-dihydroxy-5α-cholestan-6-one disulfate), which we have proved to be a peripheral anionic site (PAS)-binding AChE inhibitor. The pipeline designed in this work is key to the development of other PAS inhibitors that not only inhibit the esterase action of the enzyme but could also modulate the non-cholinergic functions of AChE linked to the process of amylogenesis. Our studies showed that 1 inhibits the enzyme not simply by blocking the main gate but by an allosteric mechanism. A detailed and careful analysis of the ligand binding position and the protein dynamics, particularly regarding their secondary gates and active site, was necessary to conclude this. The same analysis was executed with an inactive analogue (compound 2, 2β, 3α-dihydroxy-5α-cholestan-6-one). Our first computational results showed no differences in affinity to AChE between both steroids, making further analysis necessary. This work highlights the variables to be considered and develops a refined methodology, for the successful design of new potent dual-action drugs for AD, particularly PAS inhibitors, an attractive strategy to combat AD.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Novel Allosteric Effectors Targeting Human Transcription Factor TEAD

The Hippo pathway is an evolutionary conserved signaling network involved in several cellular regulatory processes. Dephosphorylation and overexpression of Yes-associated proteins (YAPs) in the Hippo-off state are common in several types of solid tumors. YAP overexpression results in its nuclear translocation and interaction with transcriptional enhanced associate domain 1-4 (TEAD1-4) transcription factors. Covalent and non-covalent inhibitors have been developed to target several interaction sites between TEAD and YAP. The most targeted and effective site for these developed inhibitors is the palmitate-binding pocket in the TEAD1-4 proteins. Screening of a DNA-encoded library against the TEAD central pocket was performed experimentally to identify six new allosteric inhibitors. Inspired by the structure of the TED-347 inhibitor, chemical modification was performed on the original inhibitors by replacing secondary methyl amide with a chloromethyl ketone moiety. Various computational tools, including molecular dynamics, free energy perturbation, and Markov state model analysis, were employed to study the effect of ligand binding on the protein conformational space. Four of the six modified ligands were associated with enhanced allosteric communication between the TEAD4 and YAP1 domains indicated by the relative free energy perturbation to original molecules. Phe229, Thr332, Ile374, and Ile395 residues were revealed to be essential for the effective binding of the inhibitors.

Recent breakthroughs in computational structural biology harnessing the power of sequences and structures

Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics

Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Fragmented blind docking: a novel protein-ligand binding prediction protocol

In the present paper we propose a novel blind docking protocol based on Autodock-Vina. The developed docking protocol can provide binding site identification and binding pose prediction at the same time, by a systematical exploration of the protein volume performed with several preliminary docking calculations. In our opinion, this protocol can be successfully applied during the first steps of the virtual screening pipeline, because it provides binding site identification and binding pose prediction at the same time without visual evaluation of the binding site. After the binding pose prediction, MM/GBSA re-scoring rescoring procedures has been applied to improve the accuracy of the protein-ligand bound state. The FRAD protocol has been tested on 116 protein-ligand complexes of the Heat Shock Protein 90 - alpha, on 176 of Human Immunodeficiency virus protease 1, and on more than 100 protein-ligand system taken from the PDBbind dataset. Overall, the FRAD approach combined to MM/GBSA re-scoring can be considered as a powerful tool to increase the accuracy and efficiency with respect to other standard docking approaches when the ligand-binding site is unknown.Communicated by Ramaswamy H. Sarma.

Drug repurposing combined with MM/PBSA based validation strategies towards MEK inhibitors screening

Emergence of oncogenic mutations in the MAPK pathway gaining more impact in the recent years. Importantly, MEK is a core element of this pathway as it is easy to inhibit and is a gatekeeper of multiple malignancies. Therefore, we performed in-silico strategy to screen repurposed candidate for MEK protein using a library of 11,808 compounds from different clusters in the DrugBank database. Glide docking, Prime-MM/GBSA and QikProp analysis were implemented to retrieve the hits with high precision. The stability of the binding mode and binding affinity of the resultant hit were explored using molecular dynamic simulations and MM/PBSA approach. The results highlight that Nebivolol (DB04861) not only achieved a stable conformation in the MEK binding pocket but also displayed highest binding affinity than the other molecules investigated in our study. Taken together, we hypothesized that Nebivolol is an excellent candidate for the inhibition of MEK in NSCLC patients in future.Communicated by Ramaswamy H. Sarma.

Exploring novel and potent molecules for disrupting DEPTOR-mTOR interaction through structure-steered screening, extra-exactitude molecular docking, prime binding free energy estimation and voguish molecular dynamics

Dep domain containing mTOR interacting protein (DEPTOR) has critical implications in the development and progression of human malignancies. Increased expression of DEPTOR promotes the growth of tumor cells by inhibiting the mTORC1, which alleviates the negative feedback inhibition by mTORC1 downstream target S6Ks on PI3K/AKT pathway thereby promotes cell survival and prevents apoptosis. This clearly suggests that targetting DEPTOR-mTOR interactions through small molecules may prove as an effective strategy for circumventing distinct cancers. In this study, we employed a top-down approach for finding three novel molecules which may prove effective in disrupting Deptor-mTOR interaction. Following DEPTOR modelling and validation we performed grid-directed structure-based screening by specifying the residues of DEPTOR known to interact with mTOR. A library of 10,000 protein-protein disrupting molecules was screened against the defined region of DEPTOR. From the screened molecules, 30 molecules with highest binding affinity were chosen for molecular docking. Thirty (30) extra-precision molecular docking experiments and 30 molecular mechanics generalized born surface area (MMGBSA) assays were performed. Following this top 10 molecules in terms of binding affinity were selected and the interaction profile of their corresponding docked files was generated. The top three molecules were finally selected after taking all the three parameters including docking score, binding energy value and interaction profile into consideration. For atomistic insights regarding DEPTOR-topmost hit interactions, molecular dynamics was performed for 100 ns. This molecule after further evaluation may prove as promising candidate for anticancer therapy.Communicated by Ramaswamy H. Sarma.

Lubeluzole Repositioning as Chemosensitizing Agent on Multidrug-Resistant Human Ovarian A2780/DX3 Cancer Cells

In a previous paper, we demonstrated the synergistic action of the anti-ischemic lubeluzole (Lube S) on the cytotoxic activity of doxorubicin (Dox) and paclitaxel in human ovarian cancer A2780 and lung cancer A549 cells. In the present paper, we extended in vitro the study to the multi-drug-resistant A2780/DX3 cell line to verify the hypothesis that the Dox and Lube S drug association may potentiate the antitumor activity of this anticancer compound also in the context of drug resistance. We also evaluated some possible mechanisms underlying this activity. We analyzed the antiproliferative activity in different cancer cell lines. Furthermore, apoptosis, Dox accumulation, MDR1 downregulation, ROS, and NO production in A2780/DX3 cells were also evaluated. Our results confirm that Lube S improves Dox antiproliferative and apoptotic activities through different mechanisms of action, all of which may contribute to the final antitumor effect. Moderate stereoselectivity was found, with Lube S significantly more effective than its enantiomer (Lube R) and the corresponding racemate (Lube S/R). Docking simulation studies on the ABCB1 Cryo-EM structure supported the hypothesis that Lube S forms a stable MDR1-Dox-Lube S complex, which hampers the protein transmembrane domain flipping and blocks the efflux of Dox from resistant A2780/DX3 cells. In conclusion, our in vitro studies reinforce our previous hypothesis for repositioning the anti-ischemic Lube S as a potentiating agent in anticancer chemotherapy.

Novel Phenothiazine/Donepezil-like Hybrids Endowed with Antioxidant Activity for a Multi-Target Approach to the Therapy of Alzheimer’s Disease

Alzheimer’s disease (AD) is a complex multi-factorial neurodegenerative disorder for which only few drugs (including donepezil, DPZ) are available as symptomatic treatments; thus, researchers are focusing on the development of innovative multi-target directed ligands (MTDLs), which could also alter the course of the disease. Among other pathological factors, oxidative stress has emerged as an important factor in AD that could affect several pathways involved in the onset and progression of the pathology. Herein, we propose a new series of hybrid molecules obtained by linking a phenothiazine moiety, known for its antioxidant properties, with N-benzylpiperidine or N-benzylpiperazine fragments, mimicking the core substructure of DPZ. The investigation of the resulting hybrids showed, in addition to their antioxidant properties, their activity against some AD-related targets, such as the inhibition of cholinesterases (both AChE and BChE) and in vitro Aβ1-40 aggregation, as well as the inhibition of the innovative target fatty acid amide hydrolase (FAAH). Furthermore, the drug-likeness properties of these compounds were assessed using cheminformatic tools. Compounds 11d and 12d showed the most interesting multi-target profiles, with all the assayed activities in the low micromolar range. In silico docking calculations supported the obtained results. Compound 13, on the other hand, while inactive in the DPPH assay, showed the best results in the in vitro antioxidant cell assays conducted on both HepG2 and SHSY-5Y cell lines. These results, paired with the low or absent cytotoxicity of these compounds at tested concentrations, allow us to aim our future research at the study of novel and effective drugs and pro-drugs with similar structural characteristics.

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions

Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function Δ_{Lin_F9}XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of Δ_{Lin_F9}XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of Δ_{Lin_F9}XGB in virtual screening application. The Δ_{Lin_F9}XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).

Design, Synthesis, Biological Evaluation, and Computational Studies of Novel Ureidopropanamides as Formyl Peptide Receptor 2 (FPR2) Agonists to Target the Resolution of Inflammation in Central Nervous System Disorders

Formyl peptide receptor 2 (FPR2) agonists can boost the resolution of inflammation and can offer alternative approaches for the treatment of pathologies with underlying chronic neuroinflammation, including neurodegenerative disorders. Starting from the FPR2 agonist 2 previously identified in our laboratory and through fine-tuning of FPR2 potency and metabolic stability, we have identified a new series of ureidopropanamide derivatives endowed with a balanced combination of such properties. Computational studies provided insights into the key interactions of the new compounds for FPR2 activation. In mouse microglial N9 cells and in rat primary microglial cells stimulated with lipopolysaccharide, selected compounds inhibited the production of pro-inflammatory cytokines, counterbalanced the changes in mitochondrial function, and inhibited caspase-3 activity. Among the new agonists, (S)-11l stands out also for the ability to permeate the blood-brain barrier and to accumulate in the mouse brain in vivo, thus representing a valuable pharmacological tool for studies in vivo.

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

A New Series of Aryloxyacetic Acids Endowed with Multi-Target Activity towards Peroxisome Proliferator-Activated Receptors (PPARs), Fatty Acid Amide Hydrolase (FAAH), and Acetylcholinesterase (AChE)

A new series of aryloxyacetic acids was prepared and tested as peroxisome proliferator-activated receptors (PPARs) agonists and fatty acid amide hydrolase (FAAH) inhibitors. Some compounds exhibited an interesting dual activity that has been recently proposed as a new potential therapeutic strategy for the treatment of Alzheimer’s disease (AD). AD is a multifactorial pathology, hence multi-target agents are currently one of the main lines of research for the therapy and prevention of this disease. Given that cholinesterases represent one of the most common targets of recent research, we decided to also evaluate the effects of our compounds on the inhibition of these specific enzymes. Interestingly, two of these compounds, (S)-5 and 6, showed moderate activity against acetylcholinesterase (AChE) and even some activity, although at high concentration, against Aβ peptide aggregation, thus demonstrating, in agreement with the preliminary dockings carried out on the different targets, the feasibility of a simultaneous multi-target activity towards PPARs, FAAH, and AChE. As far as we know, these are the first examples of molecules endowed with this pharmacological profile that might represent a promising line of research for the identification of novel candidates for the treatment of AD.

Discovery of Novel Tubulin Polymerization Inhibitors by Utilizing 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Tubulin is a potential therapeutic target for cancer. Compounds inhibit the polymerization of tubulin or promote the polymerization of tubulin to interfere with the mitotic process of cells, resulting in...

Performance evaluation of flexible macrocycle docking in AutoDock

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Combined Network Pharmacology and Cytology Experiments to Identify Potential Anti-Breast Cancer Targets and Mechanisms of Delphinidin

Delphinidin is a type of anthocyanin monomer with antioxidant, anti-inflammatory, and anti-tumor effects. However, the biological mechanisms underlying its anti-breast cancer activity have not been thoroughly studied. We further studied the effect of delphinidin on breast cancer cells through comprehensive network pharmacology, cellular and molecular experiments. We acquired the know therapeutic targets of delphinidin and obtained differentially expressed genes (DEGs) of breast cancer using RTCGA. We used topological analysis to screen out the 106 core targets of delphinium anti-breast cancer and performed functional analysis. These genes were mainly enriched in the pathways in cancer, Progesterone-mediated oocyte maturation and cell cycle. Then, by taking the intersection of the three analyzed data sets, important core targets (EGFR, TOP2A and PTGS2) were obtained and molecular-docking was performed to validate the result. Additionally, In Vitro experiments, MCF-7 and BT-474 cell proliferation was inhibited in a dose-dependent manner by delphinidin and the expressions of EGFR, TOP2A and PTGS were reduced. Moreover, delphinidin influenced cell cycle, the expressions of cdk1 and cyclin B1 were reduced. Furthermore, delphinidin induced apoptosis by activating the MAPK-Signaling pathway. Collectively, our findings suggested that delphinidin may offer effective approaches in breast cancer prevention and therapy.Supplemental data for this article is available online at http://dx.doi.org/10.1080/01635581.2021.2012582.

Bioisosteric Modification of To042: Synthesis and Evaluation of Promising Use-Dependent Inhibitors of Voltage-Gated Sodium Channels

Three analogues of To042, a tocainide-related lead compound recently reported for the treatment of myotonia, were synthesized and evaluated in vitro as skeletal muscle sodium channel blockers possibly endowed with enhanced use-dependent behavior. Patch-clamp experiments on hNav1.4 expressed in HEK293 cells showed that N-[(naphthalen-1-yl)methyl]-4-[(2,6-dimethyl)phenoxy]butan-2-amine, the aryloxyalkyl bioisostere of To042, exerted a higher use-dependent block than To042 thus being able to preferentially block the channels in over-excited membranes while preserving healthy tissue function. It also showed the lowest active transport across BBB according to the results of P-glycoprotein (P-gp) interacting activity evaluation and the highest cytoprotective effect on HeLa cells. Quantum mechanical calculations and dockings gave insights on the most probable conformation of the aryloxyalkyl bioisostere of To042 in solution and the target residues involved in the binding, respectively. Both approaches indicated the conformations that might be adopted in both the unbound and bound state of the ligand. Overall, N-[(naphthalen-1-yl)methyl]-4-[(2,6-dimethyl)phenoxy]butan-2-amine exhibits an interesting toxico-pharmacological profile and deserves further investigation.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer's Disease: Computational Refining and Biochemical Evaluation

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

Mechanism of Shuang-Huang-Lian Oral Liquid for Treatment of Mycoplasmal Pneumonia in Children on Network Pharmacology

BACKGROUND AND OBJECTIVE

Mycoplasmal pneumonia (MP) can lead to inflammation, multiple system immune damage, and mixed infection in children. The pathogenesis is still unclear. Shuang-Huang-Lian (SHL) oral liquid can treat acute upper respiratory tract infection, acute bronchitis and light pneumonia. However, our current understanding of the molecular mechanisms supporting its clinical application still lags behind due to the lack of researches. It is difficult to understand the overall sensitization mechanism of SHL oral liquid. The purpose is to explain the mechanism of action of drugs in this study, which is useful to ensure the safety of medication for children.

METHODS

The therapeutic mechanism of SHL oral liquid was investigated by a system pharmacology approach integrating drug-likeness evaluation, oral bioavailability prediction, ADMET, protein-protein interaction worknet, Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes database pathway performance, C-T-P network construction and molecular docking.

RESULTS

A total of 18 active ingredients contained in SHL oral liquid and 53 major proteins were screened out as effective players in the treatment of M. pneumoniae disease through some related pathways and molecular docking. The majority of targets, hubs and pathways were highly related to anti-mycoplasma therapy, immunity and inflammation process.

CONCLUSION

This study shows that the anti-bacterial effect of SHL oral liquid has multicomponent, multi-target and multi-pathway phenomena. The proposed approach may provide a feasible tool to clarify the mechanism of traditional Chinese medicines and further develop their therapeutic potentials.

Derivatives of Tenuazonic Acid as Potential New Multi-Target Anti-Alzheimer's Disease Agents

Alzheimer's disease (AD) is generally recognized as a multifactorial neurodegenerative pathology with an increasing impact on society. Tenuazonic acid (TA) is a natural compound that was recently identified as a potential multitarget ligand with anti-cholinesterase, anti-amyloidogenic and antioxidant activities. Using its structure as a chemical scaffold, we synthesized and evaluated new derivatives (1-5), including tenuazonic-donepezil (TA-DNP) hybrids (4 and 5) due to the clinical importance of the anti-AD drug donepezil. These novel compounds all achieved activity in the micromolar range towards all selected targets and demonstrated to be potentially orally absorbed. Moreover, a selected compound (1) was further investigated as a chelating agent towards copper (II), zinc (II) and iron (III) and showed good chelating ability (pFe = 16.6, pCu = 11.6, pZn = 6.0 at pH 7.4). Therefore, the TA motif can be considered an interesting building block in the search for innovative multi-functional anti-neurodegenerative drugs, as exemplified by hybrid 5, a promising non-cytotoxic lead compound adequate for the early stages of AD, and capable of ameliorating the oxidative status of SH-SY5Y human neuroblastoma cells.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

N-Terminus of the Third PDZ Domain of PSD-95 Orchestrates Allosteric Communication for Selective Ligand Binding

PDZ domains constitute common models to study single-domain allostery without significant structural changes. The third PDZ domain of PSD-95 (PDZ3) is known to have selective structural features that confer unique modulatory roles to this unit. In this model system, two residues, H372 directly connected to the binding site and G330 holding an off-binding-site position, were designated to assess the effect of mutations on binding selectivity. It has been observed that the H372A and G330T-H372A mutations change ligand preferences from class I (T/S amino acid at position -2 of the ligand) to class II (hydrophobic amino acid at the same position). Alternatively, the G330T single mutation leads to the recognition of both ligand classes. We have performed a series of molecular dynamics (MD) simulations for wild-type, H372A, and G330T single mutants and a double mutant of PDZ3 in the absence and presence of both types of ligands. With the combination of free-energy difference calculations and a detailed analysis of MD trajectories, "class switching" and "class bridging" behavior of PDZ3 mutants, as well as their effects on ligand selection and binding affinities are explained. We show that the dynamics of the charged N-terminus plays a fundamental role in determining the binding preferences in PDZ3 by altering the electrostatic energy. These findings are corroborated by simulations on N-terminus-truncated versions of these systems. The dynamical allostery orchestrated by the N-terminus offers a fresh perspective to the study of communication pathways in proteins.

Evaluation of Water-Soluble Mannich Base Prodrugs of 2,3,4,5-Tetrahydroazepino[4,3-b]indol-1(6H)-one as Multitarget-Directed Agents for Alzheimer's Disease

Different Mannich base derivatives have been studied with the aim of addressing the poor aqueous solubility of the recently disclosed 6-phenethyl-2,3,4,5-tetrahydroazepino[4,3-b]indol-1(6H)-one (1), a human butyrylcholinesterase inhibitor (hBChE, IC₅₀ 13 nM) and protective agent in NMDA-induced neurotoxicity, in in vivo assays. The N-(4-methylpiperazin-1-yl)methyl derivative 2 c showed a 50-fold increase in solubility in pH 7.4-buffered solution, high stability in serum and (half-life >24 h) and rapid (<3 min) conversion to 1 at acidic pH. Although less active than 1, 2 c retained moderate hBChE inhibition (IC₅₀ =3.35 μM) and a significant protective effect against NMDA-induced neurotoxicity at 0.1 μM. Moreover, 2 c resulted a weaker serum albumin binder than 1, could pass the blood-brain barrier, and exerted negligible cytotoxicity on HepG2 cells. These findings suggest that 2 c could be a water-soluble prodrug candidate of 1 for oral administration or a slow-release injectable derivative in in vivoAlzheimer's disease models.

Beyond the Canonical Endocannabinoid System. A Screening of PPAR Ligands as FAAH Inhibitors

In recent years, Peroxisome Proliferator-Activated Receptors (PPARs) have been connected to the endocannabinoid system. These nuclear receptors indeed mediate the effects of anandamide and similar substances such as oleoyl-ethanolamide and palmitoyl-ethanolamide. An increasing body of literature describing the interactions between the endocannabinoid system and PPARs has slowly but surely been accumulating over the past decade, and a multitarget approach involving these receptors and endocannabinoid degrading enzyme FAAH has been proposed for the treatment of inflammatory states, cancer, and Alzheimer’s disease. The lack of knowledge about compounds endowed with such an activity profile therefore led us to investigate a library of readily available, well-characterized PPAR agonists that we had synthesized over the years in order to find a plausible lead compound for further development. Moreover, we propose a rationalization of our results via a docking study, which sheds some light on the binding mode of these PPAR agonists to FAAH and opens the way for further research in this field.

Benchmarking the performance of MM/PBSA in virtual screening enrichment using the GPCR-Bench dataset

Recent breakthroughs in G protein-coupled receptor (GPCR) crystallography and the subsequent increase in number of solved GPCR structures has allowed for the unprecedented opportunity to utilize their experimental structures for structure-based drug discovery applications. As virtual screening represents one of the primary computational methods used for the discovery of novel leads, the GPCR-Bench dataset was created to facilitate comparison among various virtual screening protocols. In this study, we have benchmarked the performance of Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) in improving virtual screening enrichment in comparison to docking with Glide, using the entire GPCR-Bench dataset of 24 GPCR targets and 254,646 actives and decoys. Reranking the top 10% of the docked dataset using MM/PBSA resulted in improvements for six targets at EF_1% and nine targets at EF_5%, with the gains in enrichment being more pronounced at the EF_1% level. We additionally assessed the utility of rescoring the top ten poses from docking and the ability of short MD simulations to refine the binding poses prior to MM/PBSA calculations. There was no clear trend of the benefit observed in both cases, suggesting that utilizing a single energy minimized structure for MM/PBSA calculations may be the most computationally efficient approach in virtual screening. Overall, the performance of MM/PBSA rescoring in improving virtual screening enrichment obtained from docking of the GPCR-Bench dataset was found to be relatively modest and target-specific, highlighting the need for validation of MM/PBSA-based protocols prior to prospective use.

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

We present a supercomputer-driven pipeline for in-silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the [Formula: see text]-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.