TBBPA regulates calcium-mediated lysosomal exocytosis and thereby promotes invasion and migration in hepatocellular carcinoma

Tetrabromobisphenol A (TBBPA) and its derivatives are the common flame-retardants that may increase the risk of development of many types of cancers, including liver cancer. However, the effects of TBBPA in the development and progression of liver cancer remains unknown. This study investigated the potential effects of TBBPA on a metastatic phenotype of hepatocellular carcinoma cell line-HepG2. Our results revealed that TBBPA significantly promoted the migration and invasion via affecting the number and distribution of lysosomes in HepG2 cells in a dose-dependent manner. Moreover, TBBPA decreased the intracellular protein levels of Beta-Hexosaminidase (HEXB), Cathepsin B (CTSB) and Cathepsin D (CTSD) while increased the extracellular CTSB and CTSD. It entailed that TBBPA exposure could promote the lysosomal exocytosis in cancer cells. The reversal results were obtained after adding lysosomal exocytosis inhibitor vacuolin-1. Docking results suggested that TBBPA could bind to TRPML1. It was consistent with the binding position of agonist ML-SA1. TRPML1 knockdown significantly decreased the invasion and migration, and the results were reversed when TBBPA was added. The results were indicated that TRPML1 was critical in lysosomal exocytosis. In addition, our results showed that TBBPA-TRPML1 complex regulated the calcium-mediated lysosomal exocytosis, thereby promoting the metastasis in liver cancer cells. It was expected that our data could provide important basis for understanding the molecular mechanism(s) of TBBPA promoting invasion and migration of hepatoma cells and give rise to profound concerns of TBBPA exposure on human health.

Unveiling the Structural Insights into the Selective Inhibition of Protein Kinase D1

BACKGROUND

Although protein kinase D1 (PKD1) has been proved to be an efficient target for anticancer drug development, lack of structural details and substrate binding mechanisms are the main obstacles for the development of selective inhibitors with therapeutic benefits.

OBJECTIVE

The present study described the in silico dynamics behaviors of PKD1 in binding with selective and non-selective inhibitors and revealed the critical binding site residues for the selective kinase inhibition.

METHODS

Here, the three dimensional model of PKD1 was initially constructed by homology modeling along with binding site characterization to explore the non-conserved residues. Subsequently, two known inhibitors were docked to the catalytic site and the detailed ligand binding mechanisms and post binding dyanmics were investigated by molecular dynamics simulation and binding free energy calculations.

RESULTS

According to the binding site analysis, PKD1 serves several non-conserved residues in the G-loop, hinge and catalytic subunits. Among them, the residues including Leu662, His663, and Asp665 from hinge region made polar interactions with selective PKD1 inhibitor in docking simulation, which were further validated by the molecular dynamics simulation. Both inhibitors strongly influenced the structural dynamics of PKD1 and their computed binding free energies were in accordance with experimental bioactivity data.

CONCLUSION

The identified non-conserved residues likely to play critical role on molecular reorganization and inhibitor selectivity. Taken together, this study explained the molecular basis of PKD1 specific inhibition, which may help to design new selective inhibitors for better therapies to overcome cancer and PKD1 dysregulated disorders.

Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study

The recent outbreak of novel coronavirus disease -19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D-structures of key virous proteins are resolved. Taking the advantage of a recently released crystal structure of COVID-19 protease in complex with a covalently-bonded inhibitor, N3,1 I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an endpoint method called MM-PBSA-WSAS.2-4 Several promising known drugs stand out as potential inhibitors of COVID-19 protease, including Carfilzomib, Eravacycline, Valrubicin, Lopinavir and Elbasvir. Carfilzomib, an approved anti-cancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.82 kcal/mol. Streptomycin, an antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.82 kcal/mol) is not nearly as low as that of the neutral form (-7.92 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.86 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hotspot residue HIS41, is a conserved residue across many viruses including COVID-19, SARS, MERS, and HCV. The findings of this study can facilitate rational drug design targeting the COVID-19 protease.

How Different Substitution Positions of F, Cl Atoms in Benzene Ring of 5-Methylpyrimidine Pyridine Derivatives Affect the Inhibition Ability of EGFRL858R/T790M/C797S Inhibitors: A Molecular Dynamics Simulation Study

Lung cancer is the most frequent cause of cancer-related deaths worldwide, and mutations in the kinase domain of the epidermal growth factor receptor (EGFR) are a common cause of non-small-cell lung cancers, which is a major subtype of lung cancers. Recently, a series of 5-methylpyrimidine-pyridinone derivatives have been designed and synthesized as novel selective inhibitors of EGFR and EGFR mutants. However, the binding-based inhibition mechanism has not yet been determined. In this study, we carried out molecular dynamic simulations and free-energy calculations for EGFR derivatives to fill this gap. Based on the investigation, the three factors that influence the inhibitory effect of inhibitors are as follows: (1) The substitution site of the Cl atom is the main factor influencing the activity through steric effect; (2) The secondary factors are repulsion between the F atom (present in the inhibitor) and Glu762, and the blocking effect of Lys745 on the phenyl ring of the inhibitor. (3) The two factors function synergistically to influence the inhibitory capacity of the inhibitor. The theoretical results of this study can provide further insights that will aid the design of oncogenic EGFR inhibitors with high selectivity.

3D-QSAR, molecular docking, and molecular dynamics simulation study of thieno[3,2-b]pyrrole-5-carboxamide derivatives as LSD1 inhibitors

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and becomes a new target for anticancer drugs. In recent years, small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamide LSD1 inhibitors using molecular docking, and three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA q 2 = 0.783, r 2 = 0.944, r pred 2 = 0.851) and comparative molecular similarity indices analysis (CoMSIA q 2 = 0.728, r 2 = 0.982, r pred 2 = 0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

Insight into Ginkgo biloba L. Extract on the Improved Spatial Learning and Memory by Chemogenomics Knowledgebase, Molecular Docking, Molecular Dynamics Simulation, and Bioassay Validations

Epilepsy is a common cause of serious cognitive disorders and is known to have impact on patients' memory and executive functions. Therefore, the development of antiepileptic drugs for the improvement of spatial learning and memory in patients with epileptic cognitive dysfunction is important. In the present work, we systematically predicted and analyzed the potential effects of Ginkgo terpene trilactones (GTTL) on cognition and pathologic changes utilizing in silico and in vivo approaches. Based on our established chemogenomics knowledgebase, we first conducted the network systems pharmacology analysis to predict that ginkgolide A/B/C may target 5-HT 1A, 5-HT 1B, and 5-HT 2B. The detailed interactions were then further validated by molecular docking and molecular dynamics (MD) simulations. In addition, status epilepticus (SE) was induced by lithium-pilocarpine injection in adult Wistar male rats, and the results of enzyme-linked immunosorbent assay (ELISA) demonstrated that administration with GTTL can increase the expression of brain-derived neurotrophic factor (BDNF) when compared to the model group. Interestingly, recent studies suggest that the occurrence of a reciprocal involvement of 5-HT receptor activation along with the hippocampal BDNF-increased expression can significantly ameliorate neurologic changes and reverse behavioral deficits in status epilepticus rats while improving cognitive function and alleviating neuronal injury. Therefore, we evaluated the effects of GTTL (bilobalide, ginkgolide A, ginkgolide B, and ginkgolide C) on synergistic antiepileptic effect. Our experimental data showed that the spatial learning and memory abilities (e.g., electroencephalography analysis and Morris water maze test for behavioral assessment) of rats administrated with GTTL were significantly improved under the middle dose (80 mg/kg, GTTL) and high dose (160 mg/kg, GTTL). Moreover, the number of neurons in the hippocampus of the GTTL group increased when compared to the model group. Our studies showed that GTTL not only protected rat cerebral hippocampal neurons against epilepsy but also improved the learning and memory ability. Therefore, GTTL may be a potential drug candidate for the prevention and/or treatment of epilepsy.

Glucose-6-Phosphate Upregulates Txnip Expression by Interacting With MondoA

The major metabolic fates of glucose in cells are glycolysis and the pentose phosphate pathway, and they share the first step: converting glucose to glucose-6-phosphate (G6P). Here, we show that G6P can be sensed by the transcription factor MondoA/Mlx to modulate Txnip expression. Endogenous knockdown and EMSA (gel migration assay) analyses both confirmed that G6P is the metabolic intermediate that activates the heterocomplex MondoA/Mlx to elicit the expression of Txnip. Additionally, the three-dimensional structure of MondoA is modeled, and the binding mode of G6P to MondoA is also predicted by in silico molecular docking and binding free energy calculation. Finally, free energy decomposition and mutational analyses suggest that certain residues in MondoA, GKL139-141 in particular, mediate its binding with G6P to activate MondoA, which signals the upregulation of the expression of Txnip.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization

X-chromosome-linked inhibitor of apoptosis (XIAP) inhibits cell apoptosis. Overexpression of XIAP is widely found in human cancers. Second mitochondria-derived activator of caspase (SMAC) protein inhibits XIAP through binding with Baculovirus Inhibitor of apoptosis protein Repeat (BIR) 3 or BIR2 domain of XIAP. In this study, molecular dynamics (MD) simulations and the alanine scanning calculations by MM-GBSA_IE method were used to investigate the protein-peptide interaction between BIR3 and BIR2 domains of XIAP and SMAC peptide. Energetic contribution of each binding residue is calculated and hotspots on both XIAP and SMAC were identified using computational alanine scanning with interaction entropy method. We found that electrostatic polarization is important in stabilizing the protein-protein complex structure in MD simulation. By using polarized protein-specific charges, much better agreement with experimental result is obtained for calculated binding free energies compared to those using standard (nonpolarizable) AMBER force field. In particular, excellent correlation between calculated binding free energies in alanine scanning with mutational experimental data was obtained for BIR3/SMAC binding.Communicated by Ramaswamy H. Sarma.

Discrimination of Naturally-Occurring 2-Arylbenzofurans as Cyclooxygenase-2 Inhibitors: Insights into the Binding Mode and Enzymatic Inhibitory Activity

2-arylbenzofuran-containing compounds are chemical entities that can be naturally produced by several organisms. A wide-range of activities is described for several compounds of this kind and they are, therefore, valuable moieties for a lead finding from nature. Although there are in-vitro data about the activity of 2-arylbenzofuran-related compounds against cyclooxygenase (COX) enzymes, the molecular level of these COX-inhibiting constituents had not been deeply explored. Thus, 58 2-arylbenzofurans were initially screened through molecular docking within the active site of nine COX-2 crystal structures. The resulting docking scores were statistically analyzed and good reproducibility and convergence were found to discriminate the best-docked compounds. Discriminated compounds exhibited the best performance in molecular dynamics simulations as well as the most-favorable binding energies and the lowest in-vitro IC50 values for COX-2 inhibition. A three-dimensional quantitative activity-structure relationship (3D-QSAR) was also demonstrated, which showed some crucial structural requirements for enhanced enzyme inhibition. Therefore, four hits are proposed as lead structures for the development of COX-2 inhibitors based on 2-arylbenzofurans in further studies.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations

Riboswitches can regulate gene expression by direct and specific interactions with ligands and have recently attracted interest as potential drug targets for antibacterial. In this work, molecular dynamics (MD) simulations, free energy perturbation (FEP) and molecular mechanics generalized Born surface area (MM-GBSA) methods were integrated to probe the effect of mutations on the binding of ligands to guanine riboswitch (GR). The results not only show that binding free energies predicted by FEP and MM-GBSA obtain an excellent correlation, but also indicate that mutations involved in the current study can strengthen the binding affinity of ligands GR. Residue-based free energy decomposition was applied to compute ligand-nucleotide interactions and the results suggest that mutations highly affect interactions of ligands with key nucleotides U22, U51 and C74. Dynamics analyses based on MD trajectories indicate that mutations not only regulate the structural flexibility but also change the internal motion modes of GR, especially for the structures J12, J23 and J31, which implies that the aptamer domain activity of GR is extremely plastic and thus readily tunable by nucleotide mutations. This study is expected to provide useful molecular basis and dynamics information for the understanding of the function of GR and possibility as potential drug targets for antibacterial.

Revealing the binding and drug resistance mechanism of amprenavir, indinavir, ritonavir, and nelfinavir complexed with HIV-1 protease due to double mutations G48T/L89M by molecular dynamics simulations and free energy analyses

MD simulations, MM-PBSA, and SIE analyses were used to investigate the drug resistance mechanisms of two mutations G48T and L89M in HIV-1 protease toward four inhibitors.

Improving the performance of the MM/PBSA and MM/GBSA methods in recognizing the native structure of the Bcl-2 family using the interaction entropy method

Correct discrimination of native structure plays an important role in drug design. IE method significantly improves the performance of MM/PB(GB)SA method in discriminating native and decoy structures in protein–ligand/protein systems of Bcl-2 family.

Molecular mechanism with regard to the binding selectivity of inhibitors toward FABP5 and FABP7 explored by multiple short molecular dynamics simulations and free energy analyses

Recently, fatty acid binding proteins 5 and 7 (FABP5 and FABP7) have been regarded as the prospective targets for clinically treating multiple diseases related to FABPs.

Indole-Containing Phytoalexin-Based Bioisosteres as Antifungals: In Vitro and In Silico Evaluation against Fusarium oxysporum

There is a continuous search for more reliable and effective alternatives to control phytopathogens through different strategies. In this context, indole-containing phytoalexins are stimuli-induced compounds implicated in plant defense against plant pathogens. However, phytoalexins' efficacy have been limited by fungal detoxifying mechanisms, thus, the research on bioisosteres-based analogs can be a friendly alternative regarding the control of Fusarium phytopathogens, but there are currently few studies on it. Thus, as part of our research on antifungal agents, a set of 21 synthetic indole-containing phytoalexin analogs were evaluated as inhibitors against the phyopathogen Fusarium oxysporum. Results indicated that analogs of the N,N-dialkylthiourea, N,S-dialkyldithiocarbamate and substituted-1,3-thiazolidin-5-one groups exhibited the best docking scores and interaction profiles within the active site of Fusarium spp. enzymes. Vina scores exhibited correlation with experimental mycelial growth inhibition using supervised statistics, and this antifungal dataset correlated with molecular interaction fields after CoMFA. Compound 24 (tert-butyl (((3-oxo-1,3-diphenylpropyl)thio)carbonothioyl)-l-tryptophanate), a very active analog against F. oxysporum, exhibited the best interaction with lanosterol 14α-demethylase according to molecular docking, molecular dynamics and molecular mechanic/poisson-boltzmann surface area (MM/PBSA) binding energy performance. After data analyses, information on mycelial growth inhibitors, structural requirements and putative enzyme targets may be used in further antifungal development based on phytoalexin analogs for controlling phytopathogens.

Identify old drugs as selective bacterial β-GUS inhibitors by structural-based virtual screening and bio-evaluations

Irinotecan (CPT-11) is a cytotoxic drug that has wide applicability and usage in cancer treatment. Despite its success, patients suffer dose-dependent diarrhea, limiting the drug's efficacy. No effective therapy is available for this unmet medical need. The bacterial β-glucuronidase (β-GUS) plays pivotal role in CPT-11-induced diarrhea (CID) via activating the non-toxic SN-38G to toxic SN-38 inside intestine. By using structural-based virtual screening, three old drugs (N-Desmethylclozapine, Aspartame, and Gemifloxacin) were firstly identified as selective bacterial β-GUS inhibitors. The IC₅₀ values of the compounds in the enzyme-based and cell-based assays range from 0.0389 to 3.6040 and 0.0105 to 5.3730 μM, respectively. The compounds also showed good selectivity against mammalian β-GUS and no significant cytotoxicity in bacteria. Molecular docking and molecular dynamics simulations were performed to further investigate the binding modes of compounds with bacterial β-GUS. Binding free energy decomposition revealed that the compounds formed strong interactions with E413 in catalytic trail from primary monomer and F365' on the bacterial loop from the other monomer of bacterial β-GUS, explaining the selectivity against mammalian β-GUS. The old drugs identified here may be used as bacterial β-GUS inhibitors for CID or other bacterial β-GUS-related disorders.

Probing the Druggablility on the Interface of the Protein-Protein Interaction and Its Allosteric Regulation Mechanism on the Drug Screening for the CXCR4 Homodimer

Modulating protein–protein interactions (PPIs) with small drug-like molecules targeting it exhibits great promise in modern drug discovery. G protein-coupled receptors (GPCRs) are the largest family of targeted proteins and could form dimers in living biological cells through PPIs. However, compared to drug development of the orthosteric site, there has been lack of investigations on the druggability of the PPI interface for GPCRs and its functional implication on experiments. Thus, in order to address these issues, we constructed a novel computational strategy, which involved in molecular dynamics simulation, virtual screening and protein structure network (PSN), to study one representative GPCR homodimer (CXCR4). One druggable pocket was identified in the PPI interface and one small molecule targeting it was screened, which could strengthen PPI mainly through hydrophobic interaction between the benzene rings of the PPI molecule and TM4 of the receptor. The PSN results further reveals that the PPI molecule could increase the number of the allosteric regulation pathways between the druggable pocket of the dimer interface to the orthostatic site for the subunit A but only play minor role for the other subunit B, leading to the asymmetric change in the volume of the binding pockets for the two subunits (increase for the subunit A and minor change for the subunit B). Consequently, the screening performance of the subunit A to the antagonists is enhanced while the subunit B is unchanged nearly, implying that the PPI molecule may be beneficial to enhance the drug efficacies of the antagonists. In addition, one main regulation pathway with the highest frequency was identified for the subunit A, which consists of Trp195^5.34–Tyr190^ECL2–Val196^5.35–Gln200^5.39–Asp262^6.58–Cys28^N-term, revealing their importance in the allosteric regulation from the PPI molecule. The observations from the work could provide valuable information for the development of the PPI drug-like molecule for GPCRs.

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

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

Revealing binding selectivity of ligands toward murine double minute 2 and murine double minute X based on molecular dynamics simulations and binding free energy calculations

It is well known that the interactions of p53 with murine double minute 2 and murine double minute X, namely MDM2 and MDMX, have been significant targets of efficient anti-cancer drug design. In this study, molecular dynamics (MD) simulations, principal component (PC) analysis and binding free energy calculations are combined to recognize binding selectivity of three ligands to MDM2 and MDMX. The binding free energies were estimated by using molecular mechanics generalized Born surface area (MM-GBSA) method and the obtained results display that the increase in the binding enthalpy of three ligands to MDM2 relative to MDMX mainly drives the binding selectivity of them toward MDM2 and MDMX. The information obtained from PC analysis shows that the associations of ligands exert important impacts on internal dynamics of MDM2 and MDMX. Meanwhile, the calculations of residue-based free energy decomposition not only identify the hot interaction spots of ligands with MDM2 and MDMX, but also show the residues (L54, M53), (Y67, Y66), (V93, V92), (H96, P95), (I99, I98) and (Y100, Y99) in (MDM2, MDMX) are responsible for most contributions to the binding selectivity of three ligands toward MDM2 and MDMX. It is believed that this work can provide useful information for design of highly selective and dual inhibitors targeting MDM2 and MDMX.Communicated by Ramaswamy H. Sarma.

Comprehensive evaluation of the MM-GBSA method on bromodomain-inhibitor sets

MM-PB/GBSA methods represent a higher-level scoring theory than docking. This study reports an extensive testing of different MM-GBSA scoring schemes on two bromodomain (BRD) datasets. The first set is composed of 24 BRPF1 complexes, and the second one is a nonredundant set constructed from the PDBbind and composed of 28 diverse BRD complexes. A variety of MM-GBSA schemes were analyzed to evaluate the performance of four protocols with different numbers of minimization and MD steps, 10 different force fields and three different water models. Results showed that neither additional MD steps nor unfixing the receptor atoms improved scoring or ranking power. On the contrary, our results underscore the advantage of fixing receptor atoms or limiting the number of MD steps not only for a reduction in the computational costs but also for boosting the prediction accuracy. Among Amber force fields tested, ff14SB and its derivatives rather than ff94 or polarized force fields provided the most accurate scoring and ranking results. The TIP3P water model yielded the highest scoring and ranking power compared to the others. Posing power was further evaluated for the BRPF1 set. A slightly better posing power for the protocol which uses both minimization and MD steps with a fixed receptor than the one which uses only minimization with a fully flexible receptor-ligand system was observed. Overall, this study provides insights into the usage of the MM-GBSA methods for screening of BRD inhibitors, substantiating the benefits of shorter protocols and latest force fields and maintaining the crystal waters for accuracy.

Deciphering Molecular Mechanism of the Neuropharmacological Action of Fucosterol through Integrated System Pharmacology and In Silico Analysis

Fucosterol is an algae-derived unique phytosterol having several medicinal properties, including antioxidant, anti-inflammatory, anticholinesterase, neuroprotective, and so on. Accumulated evidence suggests a therapeutic promise of fucosterol in neurodegeneration; however, the in-depth pharmacological mechanism of its neuroprotection is poorly understood. Here, we employed system pharmacology and in silico analysis to elucidate the underlying mechanism of neuropharmacological action of fucosterol against neurodegenerative disorders (NDD). Network pharmacology revealed that fucosterol targets signaling molecules, receptors, enzymes, transporters, transcription factors, cytoskeletal, and various other proteins of cellular pathways, including tumor necrosis factor (TNF), mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt), neurotrophin, and toll-like receptor (TLR) signaling, which are intimately associated with neuronal survival, immune response, and inflammation. Moreover, the molecular simulation study further verified that fucosterol exhibited a significant binding affinity to some of the vital targets, including liver X-receptor-beta (LXR-β), glucocorticoid receptor (GR), tropomyosin receptor kinase B (TrkB), toll-like receptor 2/4 (TLR2/4), and β-secretase (BACE1), which are the crucial regulators of molecular and cellular processes associated with NDD. Together, the present system pharmacology and in silico findings demonstrate that fucosterol might play a significant role in modulating NDD-pathobiology, supporting its therapeutic application for the prevention and treatment of NDD.

In Silico Predictions of Endocrine Disruptors Properties

Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

Exploration of the selective binding mechanism of protein kinase Aurora A selectivity via a comprehensive molecular modeling study

Background

The kinase of Aurora A has been regarded as a promising therapeutic target due to its altered expression in various human cancers. However, given the high similarity of the active binding site of Aurora A to other kinases, designing highly selective inhibitors towards Aurora A remains a challenge. Recently, two potential small-molecule inhibitors named AT9283 and Danusertib were reported to exhibit significant selectivity to Aurora A, but not to Gleevec. It was argued that protein dynamics is crucial for drug selectivity to Aurora A. However, little computational research has been conducted to shed light on the underlying mechanisms.

Methods

In this study, MM/GBSA calculations based on conventional molecular dynamics (cMD) simulations and enhanced sampling simulations including Gaussian accelerated MD (GaMD) simulations and umbrella sampling were carried out to illustrate the selectivity of inhibitors to Aurora A.

Results

The calculation results from cMD simulation showed that the binding specificity is primarily controlled by conformational change of the kinase hinge. The protein dynamics and energetic differences were further supported by the GaMD simulations. Umbrella sampling further proved that AT9283 and Danusertib have similar potential of mean force (PMF) profiles toward Aurora A in terms of PMF depth. Compared with AT9283 and Danusertib, Gleevec has much lower PMF depth, indicating that Gleevec is more easily dissociated from Aurora A than AT9283 and Danusertib. These results not only show the selective determinants of Aurora A, but also provide valuable clues for the further development of novel potent Aurora A selective inhibitors.

Insight into the Interaction Mechanism of Nicotine, NNK, and NNN with Cytochrome P450 2A13 Based on Molecular Dynamics Simulation

Tobacco smoke contains various cancer-causing toxic substances, including nicotine and nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N'-nitrosonornicotine (NNN). The cytochrome 2A13 is involved in nicotine metabolism and in the activation of the pro-carcinogenic agents NNK and NNN, by means of α-hydroxylation reactions. Despite the significance of cytochrome 2A13 in the biotransformation of these molecules, its conformational mechanism and the molecular basis involved in the process are not fully understood. In this study, we used molecular dynamics and principal component analysis simulations for an in-depth analysis of the essential protein motions involved in the interaction of cytochrome 2A13 with its substrates. We also evaluated the interaction of these substrates with the amino acid residues in the binding pocket of cytochrome 2A13. Furthermore, we quantified the nature of these chemical interactions from free energy calculations using the Molecular Mechanics/Generalized Born Surface Area method. The ligands remained favorably oriented toward compound I (cytochrome P450 O═Fe^IV state), to undergo α-hydroxylation. The hydrogen bond with asparagine 297 was essential to maintaining the substrates in a favorable catalytic orientation. The plot of first principal motion vs second principal motion revealed that the enzyme's interaction with nicotine and NNK involved different conformational subgroups, whereas the conformational subgroups in the interaction with NNN are more similar. These results provide new mechanistic insights into the mode of interaction of the substrates with the active site of cytochrome 2A13, in the presence of compound I, which is essential for α-hydroxylation.

Molecular Mechanism of Binding Selectivity of Inhibitors toward BACE1 and BACE2 Revealed by Multiple Short Molecular Dynamics Simulations and Free-Energy Predictions

The β-amyloid cleaving enzymes 1 and 2 (BACE1 and BACE2) have been regarded as the prospective targets for clinically treating Alzheimer's disease (AD) in the last two decades. Thus, insight into the binding differences of inhibitors to BACE1 and BACE2 is of significance for designing highly selective inhibitors toward the two proteins. In this work, multiple short molecular dynamics (MSMD) simulations are coupled with the molecular mechanics generalized Born surface area (MM-GBSA) method to probe the binding selectivity of three inhibitors DBO, CS9, and SC7 on BACE1 over BACE2. The results show that the entropy effect plays a key role in selectivity identification of inhibitors toward BACE1 and BACE2, which determines that DBO has better selectivity toward BACE2 over BACE1, while CS9 and CS7 can more favorably bind to BACE1 than BACE2. The hierarchical clustering analysis based on energetic contributions of residues suggests that BACE1 and BACE2 share the common hot interaction spots. The residue-based free-energy decomposition method was applied to compute the inhibitor-residue interaction spectrum, and the results recognize four common binding subpockets corresponding to the different groups of inhibitors, which can be used as efficient targets for designing highly selective inhibitors toward BACE1 and BACE2. Therefore, these results provide a useful molecular basis and dynamics information for development of highly selective inhibitors targeting BACE1 and BACE2.

Bio-Guided Fractionation of Ethanol Extract of Leaves of Esenbeckia alata Kunt (Rutaceae) Led to the Isolation of Two Cytotoxic Quinoline Alkaloids: Evidence of Selectivity Against Leukemia Cells

Bio-guided fractionation performed on the leaves-derived ethanol extract of Esenbeckia alata (Rutaceae), a plant used in traditional medicine, led to the isolation of two alkaloids, kokusaginine 1 and flindersiamine 2, as main cytotoxic agents. Primary ethanolic extract and raw fractions exhibited cell inhibition against five cancer cell lines at different levels (25-97% inhibition at 50 µg/mL) as well as isolated alkaloids 1-2 (30-90% inhibition at 20 µM). Although alkaloid 2 generally was the most active compound, both alkaloids showed a selective effect on K562, a human chronic myelogenous leukemia cell line. The E1-like ubiquitin-activating enzymes (e.g., UBA5) have been recently described as important targets for future treatment of cancer progression, such as leukemia, among others. Therefore, as a rationale to the observed cytotoxic selectivity, an in-silico evaluation by molecular docking and molecular dynamics was also explored. Compounds 1-2 exhibited good performance on the interaction within the active site of UBA5.

Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis

Serotonin (5-HT) receptors are proteins involved in various neurological and biological processes, such as aggression, anxiety, appetite, cognition, learning, memory, mood, sleep, and thermoregulation. They are commonly associated with drug abuse and addiction due to their importance as targets for various pharmaceutical and recreational drugs. However, due to a high sequence similarity/identity among 5-HT receptors and the unavailability of the 3D structure of the different 5-HT receptor, no report was available so far regarding the systematical comparison of the key and selective residues involved in the binding pocket, making it difficult to design subtype-selective serotonergic drugs. In this work, we first built and validated three-dimensional models for all 5-HT receptors based on the existing crystal structures of 5-HT_1B, 5-HT_2B, and 5-HT_2C. Then, we performed molecular docking studies between 5-HT receptors agonists/inhibitors and our 3D models. The results from docking were consistent with the known binding affinities of each model. Sequentially, we compared the binding pose and selective residues among 5-HT receptors. Our results showed that the affinity variation could be potentially attributed to the selective residues located in the binding pockets. Moreover, we performed MD simulations for 12 5-HT receptors complexed with ligands; the results were consistent with our docking results and the reported data. Finally, we carried out off-target prediction and blood-brain barrier (BBB) prediction for Captagon using our established hallucinogen-related chemogenomics knowledgebase and in-house computational tools, with the hope to provide more information regarding the use of Captagon. We showed that 5-HT_2C, 5-HT_5A, and 5-HT₇ were the most promising targets for Captagon before metabolism. Overall, our findings can provide insights into future drug discovery and design of medications with high specificity to the individual 5-HT receptor to decrease the risk of addiction and prevent drug abuse.

Discovery of novel natural compound inhibitors targeting estrogen receptor α by an integrated virtual screening strategy

Estrogen receptor (ER) is a nuclear hormone receptor and plays an important role in mediating the cellular effects of estrogen. ER can be classified into two receptors: estrogen receptor alpha (ERα) and beta (ERβ), and the former is expressed in 50~80% of breast tumors and has been extensively investigated in breast cancer for decades. Excessive exposure to estrogen can obviously stimulate the growth of breast cancers primarily mediated by ERα, and thus anti-estrogen therapies by small molecules are of concern to clinicians and pharmaceutical industry in the treatment of ERα-positive breast cancers. Although a series of estrogen receptor modulators have been developed, these drugs can lead to resistance and side effects. Therefore, the development of small molecule inhibitors with high target specificity has been intensified. In this pursuit, an integrated computer-aided virtual screening technique, including molecular docking and pharmacophore model screening, was used to screen traditional Chinese medicine (TCM) databases. The compounds with high docking scores and fit values were subjected to ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction, and ten hits were identified as potential inhibitors targeting ERα. Molecular docking was used to investigate the binding modes between ERα and three most potent hits, and molecular dynamic simulations were chosen to explore the stability of these complexes. The rank of the predicted binding free energies evaluated by MM/GBSA is consistent with the docking score. These novel scaffolds discovered in the present study can be used as critical starting point in the drug discovery process for treating ERα-positive breast cancer. Graphical abstract .

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein-protein Interactions

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein-protein recognition. In this study, we evaluated the impact of enhanced sampling methods and solute dielectric constants on the overall accuracy of the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) approaches for the protein-protein binding free energy calculations. Here, two widely used enhanced sampling methods, including aMD and GaMD, and conventional molecular dynamics (cMD) simulations with two AMBER force fields (ff03 and ff14SB) were used to sample the conformations for 21 protein-protein complexes. The MM/PBSA and MM/GBSA calculation results illustrate that the standard MM/GBSA based on the cMD simulations yields the best Pearson correlation (r_p = -0.523) between the predicted binding affinities and the experimental data, which is much higher than that given by MM/PBSA (r_p = -0.212). Two enhanced sampling methods (aMD and GaMD) are indeed more efficient for conformational sampling, but they did not improve the binding affinity predictions for protein-protein systems, suggesting that the aMD or GaMD sampling (at least in short timescale simulations) may not be a good choice for the MM/PBSA and MM/GBSA predictions of protein-protein complexes. The solute dielectric constant of 1.0 is recommended to MM/GBSA, but a higher solute dielectric constant is recommended to MM/PBSA, especially for the systems with higher polarity on the protein-protein binding interfaces. Then, a preliminary assessment of the MM/GBSA calculations based on a variable dielectric generalized Born (VDGB) model was conducted. The results highlight the potential power of VDGB in the free energy predictions for protein-protein systems, but more thorough studies should be done in the future.

Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose

The United States of America is fighting against one of its worst-ever drug crises. Over 900 people a week die from opioid- or heroin-related overdoses, while millions more suffer from opioid prescription addiction. Recently, drug overdoses caused by fentanyl-laced cocaine specifically are on the rise. Due to drug synergy and an increase in side effects, polydrug addiction can cause more risk than addiction to a single drug. In the present work, we systematically analyzed the overdose and addiction mechanism of cocaine and fentanyl. First, we applied our established chemogenomics knowledgebase and machine-learning-based methods to map out the potential and known proteins, transporters, and metabolic enzymes and the potential therapeutic target(s) for cocaine and fentanyl. Sequentially, we looked into the detail of (1) the addiction to cocaine and fentanyl by binding to the dopamine transporter and the μ opioid receptor (DAT and μOR, respectively), (2) the potential drug-drug interaction of cocaine and fentanyl via p-glycoprotein (P-gp) efflux, (3) the metabolism of cocaine and fentanyl in CYP3A4, and (4) the physiologically based pharmacokinetic (PBPK) model for two drugs and their drug-drug interaction at the absorption, distribution, metabolism, and excretion (ADME) level. Finally, we looked into the detail of JWH133, an agonist of cannabinoid 2-receptor (CB2) with potential as a therapy for cocaine and fentanyl overdose. All these results provide a better understanding of fentanyl and cocaine polydrug addiction and future drug abuse prevention.

In Silico Identification of Novel Aromatic Compounds as Potential HIV-1 Entry Inhibitors Mimicking Cellular Receptor CD4

Despite recent progress in the development of novel potent HIV-1 entry/fusion inhibitors, there are currently no licensed antiviral drugs based on inhibiting the critical interactions of the HIV-1 envelope gp120 protein with cellular receptor CD4. In this connection, studies on the design of new small-molecule compounds able to block the gp120-CD4 binding are still of great value. In this work, in silico design of drug-like compounds containing the moieties that make the ligand active towards gp120 was performed within the concept of click chemistry. Complexes of the designed molecules bound to gp120 were then generated by molecular docking and optimized using semiempirical quantum chemical method PM7. Finally, the binding affinity analysis of these ligand/gp120 complexes was performed by molecular dynamic simulations and binding free energy calculations. As a result, five top-ranking compounds that mimic the key interactions of CD4 with gp120 and show the high binding affinity were identified as the most promising CD4-mimemic candidates. Taken together, the data obtained suggest that these compounds may serve as promising scaffolds for the development of novel, highly potent and broad anti-HIV-1 therapeutics.

Binding interactions of epididymal protease inhibitor and semenogelin-1: a homology modeling, docking and molecular dynamics simulation study

Epididymal protease inhibitor (EPPIN) that is located on the sperm surface and specific to the male reproductive system is a non-hormonal contraceptive target, since the binding of EPPIN with the seminal plasma protein semenogelin-1 (SEMG1) causes a loss of sperm function. Here, we investigated the binding interactions between EPPIN and SEMG1 by homology modeling, docking and molecular dynamics simulation. Since no crystal structure was reported for EPPIN, its 3D structure was constructed by homology modeling and refined by dynamics simulation, illustrating the C-terminus domain of EPPIN could bind with its N-terminus domain through the residues 30–32 and 113–116. The binding interaction of SEMG1^10-8 peptide and EPPIN was investigated by Z-DOCK and dynamics simulation. After evaluating the models according to the calculated binding free energies, we demonstrated that C-terminus domain of EPPIN was important for the binding of SEMG1 via residues Tyr107, Gly112, Asn116, Gln118 and Asn122, while residue Arg32 in N-terminus domain also had contribution for their binding interaction. Additionally, the binding pocket of EPPIN was defined according to these key residues and verified by molecular docking with reported inhibitor EP055, suggesting that the pocket formed by Arg32, Asn114, Asn116, Phe117 and Asn122 could be important for the design of new ligands. This study might be helpful for the understanding of biological function of EPPIN and would encourage the discovery of non-hormonal contraceptive leads/drugs in the future.

Evaluation of Scoring Function Performance on DNA-ligand Complexes

Background:

DNA has been a pharmacological target for different types of treatment, such as antibiotics and chemotherapy agents, and is still a potential target in many drug discovery processes. However, most docking and scoring approaches were parameterised for protein-ligand interactions; their suitability for modelling DNA-ligand interactions is uncertain.

Objective:

This study investigated the performance of four scoring functions on DNA-ligand complexes.

Material & Methods:

Here, we explored the ability of four docking protocols and scoring functions to discriminate the native pose of 33 DNA-ligand complexes over a compiled set of 200 decoys for each DNA-ligand complexes. The four approaches were the AutoDock, ASP@GOLD, ChemScore@GOLD and GoldScore@GOLD.

Results:

Our results indicate that AutoDock performed the best when predicting binding mode and that ChemScore@GOLD achieved the best discriminative power. Rescoring of AutoDock-generated decoys with ChemScore@GOLD further enhanced their individual discriminative powers. All four approaches have no discriminative power in some DNA-ligand complexes, including both minor groove binders and intercalators.

Conclusion:

This study suggests that the evaluation for each DNA-ligand complex should be performed in order to obtain meaningful results for any drug discovery processes. Rescoring with different scoring functions can improve discriminative power.

Molecular mechanisms of the protein-protein interaction-regulated binding specificity of basic-region leucine zipper transcription factors

It is well known that the DNA-binding specificity of transcription factors (TFs) is influenced by protein-protein interactions (PPIs). However, the underlying molecular mechanisms remain largely unknown. In this work, we adopted the cAMP-response element-binding protein (CREB) of the basic leucine zipper (bZIP) TF family as a model system, and a workflow of combined bioinformatics and molecular modeling analysis of protein-DNA interaction was tested. First, the multiple sequence alignment and SDPsite method were used to find potential bZIP family binding specificity determining positions (SDPs) within the protein-protein interaction region. Second, the mutation system was analyzed using molecular dynamics simulation. Molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) free energy calculations confirmed the enhancement of the binding affinity of the mutation, which was in agreement with experimental results. The root mean square fluctuation (RMSF) and hydrogen bonding changes suggested an open and close protein dimerization process after the system was mutated, which resulted in the change of the hydrogen bonding of the protein-DNA interface and a slight conformational change. We believe that this work will contribute to understanding the protein-protein interaction-regulated binding specificity of bZIP transcription factors.

Refinement and Rescoring of Virtual Screening Results

High-throughput docking is an established computational screening approach in drug design. This methodology enables a rapid identification of biologically active hit compounds, providing an efficient and cost-effective complement or alternative to experimental high-throughput screenings. However, limitations inherent to the methodology make docking results inevitably approximate. Two major Achille's heels include the use of approximated scoring functions and the limited sampling of the ligand-target complexes. Therefore, docking results require careful evaluation and further post-docking analyses. In this article, we will overview our approach to post-docking analysis in virtual screenings. BEAR (Binding Estimation After Refinement) was developed as a post-docking processing tool that refines docking poses by means of molecular dynamics (MD) and then rescores the ligands based on more accurate scoring functions (MM-PB(GB)SA). The tool has been validated and used prospectively in drug discovery applications. Future directions regarding refinement and rescoring in virtual screening are discussed.

Rational Design of Novel Phosphoinositide 3-Kinase Gamma (PI3Kγ) Selective Inhibitors: A Computational Investigation Integrating 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation

Phosphoinositide 3-kinase gamma (PI3Kγ) draws an increasing attention due to its link with deadly cancer, chronic inflammation and allergy. But the development of PI3Kγ selective inhibitors is still a challenging endeavor because of the high sequence homology with the other PI3K isoforms. In order to acquire valuable information about the interaction mechanism between potent inhibitors and PI3Kγ, a series of PI3Kγ isoform-selective inhibitors were analyzed by a systematic computational method, combining 3D-QSAR, molecular docking, molecular dynamic (MD) simulations, free energy calculations and decomposition. The general structure-activity relationships were revealed and some key residues relating to selectivity and high activity were highlighted. It provides precious guidance for rational virtual screening, modification and design of selective PI3Kγ inhibitors. Finally, ten novel inhibitors were optimized and P10 showed satisfactory predicted bioactivity, demonstrating the feasibility to develop potent PI3Kγ inhibitors through this computational modeling and optimization.

Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes

A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (εin) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and εin = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (εin = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.

Virtual Screening Guided Design, Synthesis and Bioactivity Study of Benzisoselenazolones (BISAs) on Inhibition of c-Met and Its Downstream Signalling Pathways

c-Met is a transmembrane receptor tyrosine kinase and an important therapeutic target for anticancer drugs. In this study, we designed a small library containing 300 BISAs molecules that consisted of carbohydrates, amino acids, isothiourea, tetramethylthiourea, guanidine and heterocyclic groups and screened c-Met targeting compounds using docking and MM/GBSA. Guided by virtual screening, we synthesised a series of novel compounds and their activity on inhibition of the autophosphorylation of c-Met and its downstream signalling pathway proteins were evaluated. We found a panel of benzisoselenazolones (BISAs) obtained by introducing isothiourea, tetramethylthiourea and heterocyclic groups into the C-ring of Ebselen, including 7a, 7b, 8a, 8b and 12c (with IC50 values of less than 20 μM in MET gene amplified lung cancer cell line EBC-1), exhibited more potent antitumour activity than Ebselen by cell growth assay combined with in vitro biochemical assays. In addition, we also tested the antitumour activity of three cancer cell lines without MET gene amplification/activation, including DLD1, MDA-MB-231 and A549. The neuroblastoma SK-N-SH cells with HGF overexpression which activates MET signalling are sensitive to MET inhibitors. The results reveal that our compounds may be nonspecific multitarget kinase inhibitors, just like type-II small molecule inhibitors. Western blot analysis showed that these inhibitors inhibited autophosphorylation of c-MET, and its downstream signalling pathways, such as PI3K/AKT and MARK/ERK. Results suggest that bensoisoselenones can be used as a scaffold for the design of c-Met inhibiting drug leads, and this study opens up new possibilities for future antitumour drug design.

How Does Chirality Determine the Selective Inhibition of Histone Deacetylase 6? A Lesson from Trichostatin A Enantiomers Based on Molecular Dynamics

Histone deacetylase 6 (HDAC6) plays a key role in a variety of neurological disorders, which makes it attractive drug target for the treatment of Alzheimer's disease, Parkinson's disease, and memory/learning impairment. The selectivity of HDAC6 inhibitors (sHDAC6Is) are widely considered to be susceptible to the sizes of their Cap group and the physicochemical properties of their linker or zinc-binding group, which makes the discovery of new sHDAC6Is extremely difficult. With the discovery of the distinct selectivity between Trichostatin A (TSA) enantiomers, the chirality residing in the connective units between TSA's Cap and linker shows a great impact on its selectivity. However, the mechanism underlining ( S)-TSA's selectivity is still elusive, and the way chirality switches the selective ( S)-TSA to nonselective ( R)-TSA is unknown. In this study, multiple computational approaches were collectively applied to explore, validate, and differentiate the binding modes of two TSA enantiomers in HDACs (especially the HDAC6) at atomic level. First, two nonconservative residues (G200/M205 and Y197/F202 in HDAC1/6) in loop3 and four conservative residues deep inside the hydrophobic binding pocket were discovered as the decisive residues of ( S)-TSA's selectivity toward HDAC6. Then, a novel mechanism underlying the selectivity of ( S)-TSA toward HDAC6 was proposed, which was composed of the trigger by two nonconservative residues F202 and M205 in HDAC6 and a subsequently improved fit of ( S)-TSA deep inside HDAC6's hydrophobic binding pocket. TSA enantiomers were used as a molecular probe to explore the mechanism underlying sHDAC6Is' selectivity in this study. Because of their decisive roles in ( S)-TSA's selectivity to HDAC6, both F202 and M205 in HDAC6 should be especially considered in the discovery of novel sHDAC6Is.

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent.