3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates

TRPV1 (Transient Receptor Potential Vanilloid Type 1) receptor, a member of Transient Receptor Potential Vanilloid subfamily of ion channels, occurs in the peripheral and central nervous system, and plays a key role in transmission of pain. Consequently, this has been the target for discovery of several pain relieving agents which have undergone clinical trials. Though several TRPV1 antagonists have progressed to become clinical candidates, many are known to cause temperature elevation in humans, halting their further advancement, and signifying the need for new chemotypes. Different chemical classes of TRPV1 antagonists share three important features: an amide or an isostere flanked by an aromatic (or fused aromatic) ring with polar substitutions on one side, and a hydrophobic group on the other. Recent work identified new series of compounds with these and additional features, leading to improvement of properties, and development of clinical candidates. Herein, we describe a 3D-QSAR model (n = 62; R² = 0.9 and Q² = 0.75) developed from the piperazinyl-aryl series of compounds and a novel 5-point pharmacophore model is shown to fit several diverse scaffolds, six clinical candidates, five pre-clinical candidates and three lead compounds. The pharmacophore model can aid in finding new chemotypes as starting points that can be developed further.     

Investigating detailed interactions between novel PAR1 antagonist F16357 and the receptor using docking and molecular dynamic simulations

Currently, Vorapaxar is the only recently FDA-approved antiplatelet drug targeting Protease-activated receptor 1 (PAR1). However, a novel antagonist, F16357, has been shown to prevent painful bladder syndrome, also known as interstitial cystitis (IC). Unfortunately, there is no high resolution structure of the F16357-receptor complex, hindering its optimization as a therapeutic agent. In this study, we used docking and molecular dynamic (MD) simulations to investigate the detailed interactions between F16357 and PAR1 at a molecular level. The recently solved crystal structure of human PAR1 complexed with Vorapaxar was used in our docking of F16357 into the binding pocket of the receptor. To enhance binding pose selection, F16357 was docked first without constraints and then with a positional constraint to invert its orientation to become similar to that of Vorapaxar. The three systems, with crystal Vorapaxar, F16357 and an inverted F16357, were subjected to 3.0 μs MD simulations. The MM-GBSA binding energy analysis showed that F16357 binds more strongly in a pose obtained from an unrestrained docking than in the inverted pose from a restrained docking; and Vorapaxar binds more strongly than F17357. This ordering is consistent with the experimental pIC₅₀ values. Our structural data showed subtle changes in the binding pose between Vorapaxar and F16357. Transmembrane helices 1, 2, 5, and 7 were most significantly affected; most notably a large kink at F279^5.47 in TM helix 5 of the Vorapaxar complex was completely absent in the F16357 complex. The results of this study facilitate the future development of other therapeutic PAR1 antagonists.     

On the mechanism of substrate/non-substrate recognition by P-glycoprotein

P-Glycoprotein (P-gp, multi-drug resistance protein, MDR1) plays a gatekeeper role, interfering delivery of multiple pharmaceuticals to the target tissues and cells. We performed Molecular Dynamics (MD) simulations to generate fifty side-chain variants for P-gp (PDB ID: 4Q9H-L) followed by docking of 31 drugs (0.6 ≤ ER ≤ 22.7) to the whole surface except the ATPase domains and the extracellular part. A selection of the most negative energy complex for each ligand followed. All compounds docked to the two areas – the main binding cavity at the top of P-gp (12.5% of compounds with ER < 1; 44.4% of 1 ≤ ER ≤ 2; and 100% of ER > 2), and the binding sites in the middle of P-gp (87.5% of ER < 1; 55.6% of 1 ≤ ER ≤ 2; and 0% of ER > 2). Our results show that anti-substrates (ER < 1), intermediate compounds (1 ≤ ER ≤ 2) and strong substrates (ER > 2) might behave differently in relation to the P-gp. According to our calculations, the anti-substrates almost do not bind the main binding cavity (MBC) of P-gp and rather approach the other binding sites on the protein; the substrates preferably bind the MBC; the intermediate compounds with 1 ≤ ER ≤ 2 bind both MBC and other binding sites almost equally. The modelling results are in line with the known hypothesis that binding the MBC is prerequisite for the pumping the compound off the P-gp.     

Molecular docking and molecular dynamics simulation studies of GPR40 receptor–agonist interactions

In order to explore the agonistic activity of small-molecule agonists to GPR40, AutoDock and GROMACS software were used for docking and molecular dynamics studies. A molecular docking of eight structurally diverse agonists (six carboxylic acids (CAs) agonist, and two non-carboxylic acids (non-CAs) agonist) was performed and the differences in their binding modes were investigated. Moreover, a good linear relationship based on the predicted binding affinities (pK_i) determined by using AutoDock and experimental activity values (pEC50) was obtained. Then, the 10 ns molecular dynamics (MD) simulations of three obtained ligand–receptor complexes embedded into the phospholipid bilayer were carried out. The position fluctuations of the ligands located inside the transmembrane domain were explored, and the stable binding modes of the three studied agonists were determined. Furthermore, the residue-based decomposition of interaction energies in three systems identified several critical residues for ligand binding.     

Multi-step virtual screening to develop selective DYRK1A inhibitors

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase–ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC₅₀ values of several μM for DYRK1A and >100 μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.     

A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis,molecular docking,density functional and molecular dynamics simulation study

Deregulated epigenetic activity of Histone deacetylase 1 (HDAC1) in tumor development and carcinogenesis pronounces it as promising therapeutic target for cancer treatment. HDAC1 has recently captured the attention of researchers owing to its decisive role in multiple types of cancer. In the present study a multistep framework combining ligand based 3D-QSAR, molecular docking and Molecular Dynamics (MD) simulation studies were performed to explore potential compound with good HDAC1 binding affinity. Four different pharmacophore hypotheses Hypo1 (AADR), Hypo2 (AAAH), Hypo3 (AAAR) and Hypo4 (ADDR) were obtained. The hypothesis Hypo1 (AADR) with two hydrogen bond acceptors (A), one hydrogen bond donor (D) and one aromatics ring (R) was selected to build 3D-QSAR model on the basis of statistical parameter. The pharmacophore hypothesis produced a statistically significant QSAR model, with co-efficient of correlation r² = 0.82 and cross validation correlation co-efficient q² = 0.70. External validation result displays high predictive power with r² (o) value of 0.88 and r² (m) value of 0.58 to carry out further in silico studies. Virtual screening result shows ZINC70450932 as the most promising lead where HDAC1 interacts with residues Asp99, His178, Tyr204, Phe205 and Leu271 forming seven hydrogen bonds. A high docking score (−11.17 kcal/mol) and lower docking energy −37.84 kcal/mol) displays the binding efficiency of the ligand. Binding free energy calculation was done using MM/GBSA to access affinity of ligands towards protein. Density Functional Theory was employed to explore electronic features of the ligands describing intramolcular charge transfer reaction. Molecular dynamics simulation studies at 50 ns display metal ion (Zn)-ligand interaction which is vital to inhibit the enzymatic activity of the protein.     

Consensus scoring model for the molecular docking study of mTOR kinase inhibitor

The discovery of mammalian target of rapamycin (mTOR) kinase inhibitors has always been a research hotspot of antitumor drugs. Consensus scoring used in the docking study of mTOR kinase inhibitors usually improves hit rate of virtual screening. Herein, we attempt to build a series of consensus scoring models based on a set of the common scoring functions. In this paper, twenty-five kinds of mTOR inhibitors (16 clinical candidate compounds and 9 promising preclinical compounds) are carefully collected, and selected for the molecular docking study used by the Glide docking programs within the standard precise (SP) mode. The predicted poses of these ligands are saved, and revaluated by twenty-six available scoring functions, respectively. Subsequently, consensus scoring models are trained based on the obtained rescoring results by the partial least squares (PLS) method, and validated by Leave-one-out (LOO) method. In addition, three kinds of ligand efficiency indices (BEI, SEI, and LLE) instead of pIC₅₀ as the activity could greatly improve the statistical quality of build models. Two best calculated models 10 and 22 using the same BEI indice have following statistical parameters, respectively: for model 10, training set R² = 0.767, Q² = 0.647, RMSE = 0.024, and for test set R² = 0.932, RMSE = 0.026; for model 22, raining set R² = 0.790, Q² = 0.627, RMSE = 0.023, and for test set R² = 0.955, RMSE = 0.020. These two consensus scoring model would be used for the docking virtual screening of novel mTOR inhibitors.     

A comparative study based on docking and molecular dynamics simulations over HDAC-tubulin dual inhibitors

Nowadays the ability to prediction of complex behavior rationally based on the computational approaches has been a successful technique in drug discovery. In the present study interactions of a new series of hybrids, which were made by linking colchicine as a tubulin inhibitor and suberoylanilide hydroxamic acid (SAHA) as a HDAC inhibitor, with HDAC8 and HDAC1 were investigated and compared. This research has been facilitated by the availability of experimental information besides employing docking methodology as well as classical molecular dynamics simulations and binding free energy calculation were performed. The obtained findings indicate different modes of interactions and inhibition strengths of the studied inhibitors for HDAC8 and HDAC1. HDAC8 binding free energies (−34.35 to −26.27 kcal/mol) revealed higher binding affinity to HDAC8 compared to HDAC1 (−33.17 to −7.99 kcal/mol). The binding energy contribution of each residue with the hybrid compounds 4a-4e within the active site of HDAC1 and HDAC8 was analyzed and the results confirmed the rule of key amino acids in interaction with the hybrid compounds.     

A multi-drug resistant HIV-1 protease is resistant to the dimerization inhibitory activity of TLF-PafF

Human immunodeficiency virus type-1 (HIV-1) protease, a homodimeric aspartyl protease, is a critical drug target in designing anti-retroviral drugs to treat HIV/AIDS. Multidrug-resistant (MDR) clinical isolate-769 HIV-1 protease (PDB ID: 3PJ6) has been shown to exhibit expanded active site cavity with wide-open conformation of flaps (Gly48–Gly52) due to the accumulation of multiple mutations. In this study, an HIV-1 protease dimerization inhibitor (PDI)–TLF-PafF, was evaluated against MDR769 HIV-1 protease using X-ray crystallography. It was hypothesized that co-crystallization of MDR769 HIV-1 protease in complex with TLF-PafF would yield either a monomeric or a disrupted dimeric structure. However, crystal structure of MDR769 I10V HIV-1 protease co-crystallized with TLF-PafF revealed an undisrupted dimeric protease structure (PDB ID: 4NKK) that is comparable to the crystal structure of its corresponding apo-protease (PDB ID: 3PJ6). In order to understand the binding profile of TLF-PafF as a PDI, docking analysis was performed using monomeric protease (prepared from the dimeric crystal structure, PDB ID: 4NKK) as docking receptor. Docking analysis revealed that TLF-PafF binds at the N and C termini (dimerization domain) in a clamp shape for the monomeric wild type receptor but not the MDR769 monomeric receptor. TLF-PafF preferentially showed higher binding affinity to the expanded active site cavity of MDR769 HIV-1 protease than to the termini. Irrespective of binding location, the binding affinity of TLF-PafF against wild type receptor (−6.7 kcal/mol) was found to be higher compared to its corresponding binding affinity against MDR receptor (−4.6 kcal/mol) suggesting that the MDR769 HIV-1 protease could be resistant to the PDI-activity of TLF-PafF, thus supporting the dimeric crystal structure (PDB ID: 4NKK).     

Designing of potential inhibitors against Staphylococcus aureus sortase A: Combined analogue and structure based approach with in vitro validation

Staphylococcus aureus sortase A is an attractive target of Gram-positive bacteria that plays a crucial role in anchoring of surface proteins to peptidoglycan present in bacterial cell wall. Inhibiting sortase A is an elementary and essential effort in preventing the pathogenesis. In this context, in silico virtual screening of in-house database was performed using ligand based pharmacophore model as a filter. The developed pharmacophore model AAHR 11 consists of two acceptors, one hydrophobic and one ring aromatic feature. Top ranked molecule KKR1 was docked into the active site of the target. After profound analysis, it was analyzed and optimized based on the observations from its binding pose orientation. Upgraded version of KKR1 was KKR2 and has improved docking score, binding interactions and best fit in the binding pocket. KKR1 along with KKR2 were further validated using 100 ns molecular dynamic studies. Both KKR1 and KKR2 contain Indole-thiazolidine moiety and were synthesized. The disk diffusion assay has good initial results (ZI of KKR1, KKR2 were 24, 38 mm at 10 μg/mL and ZI of Ampicillin was 22 at 10 μg/mL) and calculated MICs of the molecules (KKR1 5.56 ± 0.28 μg/mL, KKR2 1.32 ± 0.12 μg/mL, Ampicillin 8 ± 1.1 μg/mL) were in good agreement with standard drug Ampicillin. KKR1 has shown IC₅₀ of 1.23 ± 0.14 μM whereas the optimized lead molecule KKR2 show IC₅₀ of 0.008 ± 0.07 μM. Results from in silico were validated by in vitro studies and proved that indole-thiazolidine molecules would be useful for future development as lead molecules against S. aureus sortase A.     

Novel binding patterns between ganoderic acids and neuraminidase: Insights from docking,molecular dynamics and MM/PBSA studies

Recently, ganoderic acids (GAs) give rise to the attractive candidates of novel neuraminidase (NA) inhibitors. However, there is still no evident conclusion about their binding patterns. To this end, docking, molecular dynamics and MM/PBSA methods were combined to study the binding profiles of GAs with the N1 protein and familiar H274Y and N294S mutations (A/Vietnam/1203/04 stain). It was found that the binding affinities of ganoderic acid DM and Z (ΔG_bind, −16.83 and −10.99 kcal mol⁻¹) are comparable to that of current commercial drug oseltamivir (−23.62 kcal mol⁻¹). Electrostatic interaction is the main driving force, and should be one important factor to evaluate the binding quality and rational design of NA inhibitors. The 150-loop residues Asp151 and Arg152 played an important role in the binding processes. Further analysis revealed that ganoderic acid DM is a potential source of anti-influenza ingredient, with novel binding pattern and advantage over oseltamivir. It had steric hindrance on the 150 cavity of N1 protein, and exerted activities across the H274Y and N294S mutations. This work also pointed out how to effectively design dual-site NA inhibitors and reinforce their affinities. These findings should prove valuable for the in-depth understanding of interactions between NA and GAs, and warrant the experimental aspects to design novel anti-influenza drugs.     

Application of the independent molecule model to elucidate the dynamics of structure I methane hydrate: A second report

Two model systems of methane hydrate are constructed. One has a small cage surrounded by 12 large cages. The other has a large cage surrounded by four small cages and ten large cages. Three different H-bonding network patterns between waters are formed, and three random configurations of methane in each cage are chosen. A new method called the surface water fixed model is presented in which the energy minimum conformations for both model systems are preserved close to the X-ray crystallized structure. With normal mode analysis, we calculated frequencies of 2916.6 cm⁻¹ for a small cage at a centre, 2915.9 cm⁻¹ not at a centre, and 2911.7 cm⁻¹ for a large cage at a centre, and 2911.3 cm⁻¹ not at a centre. These frequencies are in moderate agreement with the corresponding Raman spectra, though not adequate. With our new method, however, it should be possible to improve agreement with the Raman spectra, if a model system vastly larger than the present model systems were constructed.     

Compound Linguistic Scale

Rating scales are the essential interfaces for many research areas such as in decision making and recommendation. Some issues concerning syntactic and sematic structures are still open to discuss. This research proposes a Compound Linguistic Scale (CLS), a two dimension rating scale, as a promising rating interface. The CLS is comprised of Compound Linguistic Variable (CLV) and Deductive Rating Strategy (DRS). CLV can ideally produce 21 to 73 ((7 ± 2)((7 ± 2) − 1) + 1) ordinal-in-ordinal rating items, which extends the classic rating scales usually on the basis of 7 ± 2 principle, to better reflect the raters’ preferences whilst DRS is a double step rating approach for a rater to choose a compound linguistic term among two dimensional options on a dynamic rating interface. The numerical analyses show that the proposed CLS can address rating dilemma for a single rater and more accurately reflects consistency among various raters. CLS can be applied to surveys, questionnaires, psychometrics, recommender systems and decision analysis of various application domains.     

Experimental blood glucose interval identification of patients with type 1 diabetes

Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty.The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each.Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.     

Synthesis and evaluation of resveratrol derivatives as new chemical entities for cancer

Resveratrol has been shown to be active in inhibiting multistage carcinogenesis. The potential use of resveratrol in cancer chemoprevention or chemotherapy settings has been hindered by its short half-life and low bioavailability. Considering the above remarks, using resveratrol as a prototype, we have synthesized two derivatives of resveratrol. Their activity was evaluated using in vitro and in silico analysis. Biological evaluation of resveratrol analogues on U937 cells had shown that two synthesized analogues of resveratrol had higher rates of inhibition than the parental molecule at 10 μM concentration. EMSA conducted for NF-kB revealed that these molecules significantly interfered in the DNA binding ability of NF-kB. It was found that these molecules suppressed the expression of TNFα, TNFR, IL-8, actin and activated the expression of FasL, FasR genes. To understand possible molecular mechanism of the action we performed docking and dynamic studies, using NF-kB as a receptor. Results showed that resveratrol, RA1 and RA2 interacted with the residues involved in DNA binding. Resveratrol analogues by interacting NF-kB might have prevented its translocation and also by interacting with the residues involved in DNA binding might have prevented the binding of NF-kB to DNA. This may be the reason for suppression of NF-kB binding to DNA.     

Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code

One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.     

Design and synthesis of a novel class of carbonic anhydrase-IX inhibitor 1-(3-(phenyl/4-fluorophenyl)-7-imino-3H-[1,2,3]triazolo[4,5d]pyrimidin 6(7H)yl)urea

Carbonic anhydrase IX (CAIX) is a promising target in cancer therapy especially in the case of hypoxia-induced tumors. The selective inhibition of CA isozymes is a challenging task in drug design and discovery process. Here, we performed fluorescence-binding studies and inhibition assay combined with molecular docking and molecular dynamics (MD) simulation analyses to determine the binding affinity of two synthesized triazolo-pyrimidine urea derived (TPUI and TPUII) compounds with CAIX and CAII. Fluorescence binding results are showing that molecule TPUI has an excellent binding-affinity for CAIX (k_D = 0.048 μM). The TPUII also exhibits an appreciable binding affinity (k_D = 7.52 μM) for CAIX. TPUI selectively inhibits CAIX as compared to TPUII in the 4-NPA assay. Docking studies show that TPUI is spatially well-fitted in the active site cavity of CAIX, and is involve in H-bond interactions with His94, His96, His119, Thr199 and Thr200. MD simulation studies revealed that TPUI efficiently binds to CAIX and essential active site residual interaction is consistent during the entire simulation of 40 ns. These studies suggest that TPUI appeared as novel class of CAIX inhibitor, and may be used as a lead molecule for the development of potent and selective CAIX inhibitor for the hypoxia-induced cancer therapy.