Comparative Analysis of Protein-Bound Ligand Conformations with Respect to Catalyst's Conformational Space Subsampling Algorithms

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

In Silico-Guided Rational Drug Design and Synthesis of Novel 4-(Thiophen-2-yl)butanamides as Potent and Selective TRPV1 Agonists

We describe an in silico-guided rational drug design and the synthesis of the suggested ligands, aimed at improving the TRPV1-ligand binding properties and the potency of N-(4-hydroxy-3-methoxybenzyl)-4-(thiophen-2-yl) butanamide I, a previously identified TRPV1 agonist. The docking experiments followed by molecular dynamics simulations and thermodynamic analysis led the drug design toward both the introduction of a lipophilic iodine and a flat pyridine/benzene at position 5 of the thiophene nucleus. Most of the synthesized compounds showed high TRPV1 efficacy and potency as well as selectivity. The molecular modeling analysis highlighted crucial hydrophobic interactions between Leu547 and the iodo-thiophene nucleus, as in amide 2a, or between Phe543 and the pyridinyl moiety, as in 3a. In the biological evaluation, both compounds showed protective properties against oxidative stress-induced ROS formation in human keratinocytes. Additionally, while 2a showed neuroprotective effects in both neurons and rat brain slices, 3a exhibited potent antinociceptive effect in vivo..

Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.

Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials

Computational programs accelerate the chemical discovery processes but often need proper three-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the Python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automatize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization, and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereoconfiguration that yields the lowest-energy conformer. With Auto3D, we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich data set. ANI-2xt is benchmarked with DFT methods on geometry optimization and electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies and is several orders of magnitude faster than DFT methods.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Targeting the actin nucleation promoting factor WASp provides a therapeutic approach for hematopoietic malignancies

Cancer cells depend on actin cytoskeleton rearrangement to carry out hallmark malignant functions including activation, proliferation, migration and invasiveness. Wiskott–Aldrich Syndrome protein (WASp) is an actin nucleation-promoting factor and is a key regulator of actin polymerization in hematopoietic cells. The involvement of WASp in malignancies is incompletely understood. Since WASp is exclusively expressed in hematopoietic cells, we performed in silico screening to identify small molecule compounds (SMCs) that bind WASp and promote its degradation. We describe here one such identified molecule; this WASp-targeting SMC inhibits key WASp-dependent actin processes in several types of hematopoietic malignancies in vitro and in vivo without affecting naïve healthy cells. This small molecule demonstrates limited toxicity and immunogenic effects, and thus, might serve as an effective strategy to treat specific hematopoietic malignancies in a safe and precisely targeted manner.

Cancer cells proliferate and invade via cytoskeletal proteins such as WASp, exclusively expressed in hematopoietic cells. Here the authors show a specific small molecule compound inhibiting cancer cell activity by WASp degradation and demonstrating its therapeutic potential in vitro and in vivo.

Multiple in vitro biological effects of phenolic compounds from Terminalia chebula var. tomentella

ETHNOPHARMACOLOGICAL RELEVANCE

Terminalia chebula (TC), a well-known Indian Ayurvedic medicine introduced into China in the Sui and Tang Dynasties, has been recorded and used medicinally as Fructus Chebulae, together with its variety tomentella (TCT) in the Chinese Pharmacopoeia. They have been also used commonly for the treatment of diabetes mellitus by Tibetan medicine.

AIM OF THE STUDY

To investigate the main bioactive and therapeutic principles in the fruits of TCT, based on the extensive evaluation of their anti-inflammatory and hypoglycemic activities.

MATERIALS AND METHODS

The TCT fresh fruits were analyzed by HPLC and separated further by column chromatography and preparative HPLC. The isolated compounds were identified by extensive spectroscopic analyses, including 1D/2D NMR, MS, UV, IR and ECD. Anti-inflammatory activity was evaluated by inhibition of NO production in RAW264.7 cells. The specific iNOS (PDB ID: 3E7G) structure was prepared by Discovery Studio 4.0, and the molecular docking simulation was performed on GOLD (version 5.2.2). Hypoglycemic activity was measured using the substrate solution of 4-nitrophenyl-α-d-glucopyranoside enzyme and buffer solution.

RESULTS

The HPLC analysis method of polyphenols in the fruits of TCT was established, and 13 main chromatographic peaks were identified, including six hydrolyzable tannins (2, 4-7, 10-11), three simple phenols (12-14), and one oleanane pentacyclic triterpene, arjungenin. Extensive chromatographic separation of TCT fresh fruits yielded 14 compounds, including one new natural hydrolyzable tannin, 2,3-(S)-HHDP-6-O-galloyl-d-glucose (1). The known compounds were identified as 10 hydrolyzable tannins (2-11) and three simple phenols (12-14). Compounds 10 (IC₅₀ = 36.43 ± 0.21 μM), 11 (IC₅₀ = 42.28 ± 0.09 μM) displayed stronger NO inhibitory activity than the positive control L-NMMA (IC₅₀ = 42.34 ± 0.66 μM), while 2, 4, and 9 showed moderate inhibitory activity against NO production. Further molecular docking simulation of specific iNOS on 10 and 11, as well as five previously isolated lignans 15-19 showed that there were no obvious rules between docking results and the in vitro NO inhibitory activity for hydrolyzable tannins (10 and 11), while the mechanism of anti-inflammatory activity for lignans was related to the substitution of conjugated aldehyde groups. Moreover, most of the hydrolyzable tannins (1-2, 4-5, 9-11) and simple phenol (12) displayed stronger inhibitory effects on α-glucosidase than the positive control, quercetin (IC₅₀ = 6.118 ± 0.071 μM), with IC₅₀ values ranging from 0.079 to 16.494 μM. Among these bioactive isolates, the hydrolyzable tannins 2, 4-5, and 9-11, and simple phenol 12 are major chemical components in TCT fruit.

CONCLUSIONS

The results showed that lignans and hydrolyzed tannins are the main active ingredients of TCT fruits, responsible for the traditional treatment of sore throat and cough. Moreover, hydrolyzed tannins and simple phenolic compounds with potential hypoglycemic activity are closely related to the ethno-pharmacological uses of TCT fruits on diabetes in Tibetan medicine.

Large-Scale Analysis of Bioactive Ligand Conformational Strain Energy by Ab Initio Calculation

Ligand conformational strain energy (LCSE) plays an important role in virtual screening and lead optimization. While various studies have provided insights into LCSE for small-molecule ligands in the Protein Data Bank (PDB), conclusions are inconsistent mainly due to small datasets, poor quality control of crystal structures, and molecular mechanics (MM) or low-level quantum mechanics (QM) calculations. Here, we built a high-quality dataset (LigBoundConf) of 8145 ligand-bound conformations from PDB crystal structures and calculated LCSE at the M062X-D3/ma-TZVPP (SMD)//M062X-D3/def2-SVP(SMD) level for each case in the dataset. The mean/median LCSE is 4.6/3.7 kcal/mol for 6672 successfully calculated cases, which is significantly lower than the estimates based on molecular mechanics in many previous analyses. Especially, when removing ligands with nonaromatic ring(s) that are prone to have large LCSEs due to electron density overfitting, the mean/median LCSE was reduced to 3.3/2.5 kcal/mol. We further reveal that LCSE is correlated with several ligand properties, including formal atomic charge, molecular weight, number of rotatable bonds, and number of hydrogen-bond donors and acceptors. In addition, our results show that although summation of torsion strains is a good approximation of LCSE for most cases, for a small fraction (about 6%) of our dataset, it underestimates LCSEs if ligands could form nonlocal intramolecular interactions in the unbound state. Taken together, our work provides a comprehensive profile of LCSE for ligands in PDB, which could help ligand conformation generation, ligand docking pose evaluation, and lead optimization.

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks

The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.

Comparative anti-proliferative effects of potential HER2 inhibitors on a panel of breast cancer cell lines

BACKGROUND

Breast cancer is one of the most lethal types of cancer in women worldwide. The human epidermal growth factor receptor 2 (HER2) is considered as a validated target in breast cancer therapy. Previously, we have used quantitative structure activity relationship QSAR equations and their associated pharmacophore models to screen for new promising HER2 structurally diverse inhibitory leads which were tested against HER2-overexpressing SKOV3 ovarian cancer cell line.

OBJECTIVE

In this study, we sought to explore the effect of most active ligands against different normal and breast cancer cell lines that represent different breast cancer subtypes with distinguished expression levels in HER2 and HER1.

METHODS

We have tested the promising compounds against SKBR3, MDA-MB-231, MCF7, human fibroblast, and MCF10 cell lines. To understand the inhibitory effects of the active ligands against HER2 over expressed breast cancer cell lines, all inhibitors and the control compound, lapatinib, were docked into the active site of HER2 enzyme performed using Ligand Fit docking engine and PMF scoring function.

RESULTS

Five ligands exhibited promising results with relatively low IC₅₀ values on cells that amplify HER2 and high IC₅₀ on those that do not express such a receptor. The most potent compound (compound 13) showed an IC₅₀ of 0.046 µM. To test their toxicity against normal cells, the active compounds were tested against both normal fibroblast and normal breast cancer cell MCF-10 and relatively high IC₅₀ values were scored. The IC₅₀ values on HER2 over-expressed breast cancer and normal fibroblast cells provided a promising safety index. Docking results showed the highest similarity in the binding site between the most active ligand and the lapatinib.

CONCLUSION

Our pharmacophore model resulted in a high potent ligand that shows high potency against HER2 positive breast cancer and relatively low toxicity towards the normal human cells.

A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum Annealer

Discovering the low-energy conformations of a molecule is of great interest to computational chemists, with applications in in silico materials design and drug discovery. In this paper, we propose a variable neighbourhood search heuristic for the conformational search problem. Using the structure of a molecule, neighbourhoods are chosen to allow for the efficient use of a binary quadratic optimizer for conformational search. The method is flexible with respect to the choice of molecular force field and the number of discretization levels in the search space, and can be further generalized to take advantage of higher-order binary polynomial optimizers. It is well-suited for the use of devices such as quantum annealers. After carefully defining neighbourhoods, the method easily adapts to the size and topology of these devices, allowing for seamless scaling alongside their future improvements.

Pseudomonas aeruginosa Lipoxygenase LoxA Contributes to Lung Infection by Altering the Host Immune Lipid Signaling

Pseudomonas aeruginosa is an opportunistic bacteria and a major cause of nosocomial pneumonia. P. aeruginosa has many virulence factors contributing to its ability to colonize the host. LoxA is a lipoxygenase enzyme secreted by P. aeruginosa that oxidizes polyunsaturated fatty acids. Based on previous in vitro biochemical studies, several biological roles of LoxA have been hypothesized, including interference of the host lipid signaling, and modulation of bacterial invasion properties. However, the contribution of LoxA to P. aeruginosa lung pathogenesis per se remained unclear. In this study, we used complementary in vitro and in vivo approaches, clinical strains of P. aeruginosa as well as lipidomics technology to investigate the role of LoxA in lung infection. We found that several P. aeruginosa clinical isolates express LoxA. When secreted in the lungs, LoxA processes a wide range of host polyunsaturated fatty acids, which further results in the production of bioactive lipid mediators (including lipoxin A₄). LoxA also inhibits the expression of major chemokines (e.g., MIPs and KC) and the recruitment of key leukocytes. Remarkably, LoxA promotes P. aeruginosa persistence in lungs tissues. Hence, our study suggests that LoxA-dependent interference of the host lipid pathways may contribute to P. aeruginosa lung pathogenesis.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water

Prediction of compound properties from structure via quantitative structure-activity relationship and machine-learning approaches is an important computational chemistry task in small-molecule drug research. Though many such properties are dependent on three-dimensional structures or even conformer ensembles, the majority of models are based on descriptors derived from two-dimensional structures. Here we present results from a thorough benchmark study of force field, semiempirical, and density functional methods for the calculation of conformer energies in the gas phase and water solvation as a foundation for the correct identification of relevant low-energy conformers. We find that the tight-binding ansatz GFN-xTB shows the lowest error metrics and highest correlation to the benchmark PBE0-D3(BJ)/def2-TZVP in the gas phase for the computationally fast methods and that in solvent OPLS3 becomes comparable in performance. MMFF94, AM1, and DFTB+ perform worse, whereas the performance-optimized but far more expensive functional PBEh-3c yields energies almost perfectly correlated to the benchmark and should be used whenever affordable. On the basis of our findings, we have implemented a reliable and fast protocol for the identification of low-energy conformers of drug-like molecules in water that can be used for the quantification of strain energy and entropy contributions to target binding as well as for the derivation of conformer-ensemble-dependent molecular descriptors.

xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

Strategies of Virtual Screening in Medicinal Chemistry

Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.

Synthesis and characterization of water-soluble macrocyclic peptides stabilizing protein α-turn

Macrocyclic peptides mimic tight “non-classical” α-turn type II-α_LS found in proteins, as shown by spectroscopic and computational analysis of their equilibrating conformations.

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.

Identification of the Beer Component Hordenine as Food-Derived Dopamine D2 Receptor Agonist by Virtual Screening a 3D Compound Database

The dopamine D2 receptor (D2R) is involved in food reward and compulsive food intake. The present study developed a virtual screening (VS) method to identify food components, which may modulate D2R signalling. In contrast to their common applications in drug discovery, VS methods are rarely applied for the discovery of bioactive food compounds. Here, databases were created that exclusively contain substances occurring in food and natural sources (about 13,000 different compounds in total) as the basis for combined pharmacophore searching, hit-list clustering and molecular docking into D2R homology models. From 17 compounds finally tested in radioligand assays to determine their binding affinities, seven were classified as hits (hit rate = 41%). Functional properties of the five most active compounds were further examined in β-arrestin recruitment and cAMP inhibition experiments. D2R-promoted G-protein activation was observed for hordenine, a constituent of barley and beer, with approximately identical ligand efficacy as dopamine (76%) and a Ki value of 13 μM. Moreover, hordenine antagonised D2-mediated β-arrestin recruitment indicating functional selectivity. Application of our databases provides new perspectives for the discovery of bioactive food constituents using VS methods. Based on its presence in beer, we suggest that hordenine significantly contributes to mood-elevating effects of beer.

Natural Prenylchalconaringenins and Prenylnaringenins as Antidiabetic Agents: α-Glucosidase and α-Amylase Inhibition and in Vivo Antihyperglycemic and Antihyperlipidemic Effects

Inhibition of α-glucosidase and α-amylase decreases postprandial blood glucose levels and delays glucose absorption, making it a treatment strategy for type 2 diabetes. This study examined in vivo and in vitro antidiabetic activities of natural prenylchalconaringenins 1 and 2 and prenylnaringenins 3 and 4, found in hops and beer. 3'-Geranylchalconaringenin (2) competitively and irreversibly inhibited α-glucosidase (IC₅₀ = 1.08 μM) with activity 50-fold higher than that of acarbose (IC₅₀ = 51.30 μM) and showed moderate inhibitory activity against α-amylase (IC₅₀ = 20.46 μM). Docking analysis substantiated these findings. In addition, compound 2 suppressed the increase in postprandial blood glucose levels and serum levels of total cholesterol and triglycerides in streptozotocin-induced diabetic mice. Taken together, these results suggest that 2 has dual inhibitory activity against α-glucosidase and α-amylase and alleviates diabetic hyperglycemia and hyperlipidemia, making it a potential functional food ingredient and drug candidate for management of type 2 diabetes.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

Dihedral Angle-Based Sampling of Natural Product Polyketide Conformations: Application to Permeability Prediction

Macrocycles pose challenges for computer-aided drug design due to their conformational complexity. One fundamental challenge is identifying all low-energy conformations of the macrocyclic ring, which is important for modeling target binding, passive membrane permeation, and other conformation-dependent properties. Macrocyclic polyketides are medically and biologically important natural products characterized by structural and functional diversity. Advances in synthetic biology and semisynthetic methods may enable creation of an even more diverse set of non-natural product polyketides for drug discovery and other applications. However, the conformational sampling of these flexible compounds remains demanding. We developed and optimized a dihedral angle-based macrocycle conformational sampling method for macrocycles of arbitrary structure, and here we apply it to diverse polyketide natural products. First, we evaluated its performance using a data set of 37 polyketides with available crystal structures, with 9-22 rotatable bonds in the macrocyclic ring. Our optimized protocol was able to reproduce the crystal structure of polyketides' aglycone backbone within 0.50 Å RMSD for 31 out of 37 polyketides. Consistent with prior structural studies, our analysis suggests that polyketides tend to have multiple distinct low-energy structures, including the bioactive (target-bound) conformation as well as others of unknown significance. For this reason, we also introduce a strategy to improve both efficiency and accuracy of the conformational search by utilizing torsional restraints derived from NMR vicinal proton couplings to restrict the conformational search. Finally, as a first application of the method, we made blinded predictions of the passive membrane permeability of a diverse set of polyketides, based on their predicted structures in low- and high-dielectric media.

Design and Synthesis of New Transient Receptor Potential Vanilloid Type-1 (TRPV1) Channel Modulators: Identification, Molecular Modeling Analysis, and Pharmacological Characterization of the N-(4-Hydroxy-3-methoxybenzyl)-4-(thiophen-2-yl)butanamide, a Small Molecule Endowed with Agonist TRPV1 Activity and Protective Effects against Oxidative Stress

4-(Thiophen-2-yl)butanoic acid was identified as a cyclic substitute of the unsaturated alkyl chain of the natural ligand, capsaicin. Accordingly, a new class of amides was synthesized in good yield and high purity and their molecular recognition against the target was investigated by means of docking experiments followed by molecular dynamics simulations, in order to rationalize their geometrical and thermodynamic profiles. The pharmacological properties of these new compounds were expressed as activation (EC50) and desensitization (IC50) potencies. Several compounds were found to activate TRPV1 channels, and in particular, derivatives 1 and 10 behaved as TRPV1 agonists endowed with good efficacy as compared to capsaicin. The most promising compound 1 was also evaluated for its protective role against oxidative stress on keratinocytes and differentiated human neuroblastoma cell lines expressing the TRPV1 receptor as well as for its cytotoxicity and analgesic activity in vivo.

Hit Identification of a Novel Dual Binder for h-telo/c-myc G-Quadruplex by a Combination of Pharmacophore Structure-Based Virtual Screening and Docking Refinement

It is well known that G-quadruplexes are targets of great interest for their roles in crucial biological processes, such as aging and cancer. Hence, a promising strategy for anticancer drug therapy is the stabilization of these structures by small molecules. We report a high-throughput in silico screening of commercial libraries from several different vendors by means of a combined structure-based pharmacophore model approach followed by docking simulations. The compounds selected by the virtual screening procedure were then tested for their ability to interact with human telomeric G-quadruplex folding by circular dichroism, fluorescence spectroscopy, and fluorescence intercalator displacement. Our approach resulted in the identification of a 13-[(dimethylamino)methyl]-12-hydroxy-8H-benzo[c]indolo[3,2,1-ij][1,5]naphthyridin-8-one derivative as a novel promising stabilizer of G-quadruplex structures within the human telomeric and the c-myc promoter sequences.

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

There has been an explosion of structural information for pharmaceutical compounds bound to biological targets, but the conformations and dynamics of compounds free in solution are poorly characterized, if at all. Yet, knowledge of the unbound state is essential to understand the fundamentals of molecular recognition, including the much debated conformational intramolecular reorganization energy of a compound upon binding (ΔEReorg). Also, dependable observation of the unbound compounds is important for ligand-based drug discovery, e.g. with pharmacophore modelling. Here, these questions are addressed with long (⩾0.5μs) state-of-the-art molecular dynamics (MD) simulations of 26 compounds (including 7 approved drugs) unbound in explicit solvent. These compounds were selected to be chemically diverse, with a range of flexibility, and good quality bioactive X-ray structures. The MD-simulated free compounds are compared to their bioactive structure and conformers generated with ad hoc sampling in vacuo or with implicit generalized Born (GB) aqueous solvation models. The GB conformational models clearly depart from those obtained in explicit solvent, and suffer from conformational collapse almost as severe as in vacuo. Thus, the global energy minima in vacuo or with GB are not suitable representations of the unbound state, which can instead be extensively sampled by MD simulations. Many, but not all, MD-simulated compounds displayed some structural similarity to their bioactive structure, supporting the notion of conformational pre-organization for binding. The ligand-protein complexes were also simulated in explicit solvent, to estimate ΔEReorg as an enthalpic difference ΔHReorg between the intramolecular energies in the bound and unbound states. This fresh approach yielded ΔHReorg values⩽6kcal/mol for 18 out of 26 compounds. For three particularly polar compounds 15⩽ΔHReorg⩽20kcal/mol, supporting the notion that ΔHReorg can be substantial. Those large ΔHReorg values correspond to a redistribution of electrostatic interactions upon binding. Overall, the study illustrates how MD simulations offer a promising avenue to characterize the unbound state of medicinal compounds.

BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure–activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as ‘rotamers’ in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::Conf was benchmarked against other conformer generator methods including Confgen, Moe, Omega and RDKit in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::Conf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the Vernalis benchmark dataset. The ‘rotamer’ approach will allow integration of BCL::Conf into respective computational biology programs such as Rosetta.Graphical abstract:

5'-Methylene-triazole-substituted-aminoribosyl uridines as MraY inhibitors: synthesis, biological evaluation and molecular modeling

The straightforward synthesis of 5'-methylene-[1,4]-triazole-substituted aminoribosyl uridines is described. Two families of compounds were synthesized from a unique epoxide which was regioselectively opened by acetylide ions (for compounds II) or azide ions (for compounds III). Sequential diastereoselective glycosylation with a ribosyl fluoride derivative, Cu(i)-catalyzed azide-alkyne cycloaddition (CuAAC) with various complementary azide and alkyne partners afforded the targeted compounds after final deprotection. The biological activity of the 16 resulting compounds together with that of 14 previously reported compounds I, lacking the 5' methylene group, was evaluated on the MraY transferase activity. Out of the 30 tested compounds, 18 compounds revealed MraY inhibition with IC50 ranging from 15 to 150 μM. A molecular modeling study was performed to rationalize the observed structure-activity relationships (SAR), which allowed us to correlate the activity of the most potent compounds with an interaction involving Leu191 of MraYAA. The antibacterial activity was also evaluated and seven compounds exhibited a good activity against Gram-positive bacterial pathogens with MIC ranging from 8 to 32 μg mL(-1), including the methicillin resistant Staphylococcus aureus (MRSA).

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

A knowledge-based approach for identification of drugs against vivapain-2 protein of Plasmodium vivax through pharmacophore-based virtual screening with comparative modelling

Malaria is one of the most infectious diseases in the world. Plasmodium vivax, the pathogen causing endemic malaria in humans worldwide, is responsible for extensive disease morbidity. Due to the emergence of resistance to common anti-malarial drugs, there is a continuous need to develop a new class of drugs for this pathogen. P. vivax cysteine protease, also known as vivapain-2, plays an important role in haemoglobin hydrolysis and is considered essential for the survival of the parasite. The three-dimensional (3D) structure of vivapain-2 is not predicted experimentally, so its structure is modelled by using comparative modelling approach and further validated by Qualitative Model Energy Analysis (QMEAN) and RAMPAGE tools. The potential binding site of selected vivapain-2 structure has been detected by grid-based function prediction method. Drug targets and their respective drugs similar to vivapain-2 have been identified using three publicly available databases: STITCH 3.1, DrugBank and Therapeutic Target Database (TTD). The second approach of this work focuses on docking study of selected drug E-64 against vivapain-2 protein. Docking reveals crucial information about key residues (Asn281, Cys283, Val396 and Asp398) that are responsible for holding the ligand in the active site. The similarity-search criterion is used for the preparation of our in-house database of drugs, obtained from filtering the drugs from the DrugBank database. A five-point 3D pharmacophore model is generated for the docked complex of vivapain-2 with E-64. This study of 3D pharmacophore-based virtual screening results in identifying three new drugs, amongst which one is approved and the other two are experimentally proved. The ADMET properties of these drugs are found to be in the desired range. These drugs with novel scaffolds may act as potent drugs for treating malaria caused by P. vivax.

Development of pharmacophore similarity-based quantitative activity hypothesis and its applicability domain: applied on a diverse data-set of HIV-1 integrase inhibitors

Quantitative pharmacophore hypothesis combines the 3D spatial arrangement of pharmacophore features with biological activities of the ligand data-set and predicts the activities of geometrically and/or pharmacophoric similar ligands. Most pharmacophore discovery programs face difficulties in conformational flexibility, molecular alignment, pharmacophore features sampling, and feature selection to score models if the data-set constitutes diverse ligands. Towards this focus, we describe a ligand-based computational procedure to introduce flexibility in aligning the small molecules and generating a pharmacophore hypothesis without geometrical constraints to define pharmacophore space, enriched with chemical features necessary to elucidate common pharmacophore hypotheses (CPHs). Maximal common substructure (MCS)-based alignment method was adopted to guide the alignment of carbon molecules, deciphered the MCS atom connectivity to cluster molecules in bins and subsequently, calculated the pharmacophore similarity matrix with the bin-specific reference molecules. After alignment, the carbon molecules were enriched with original atoms in their respective positions and conventional pharmacophore features were perceived. Distance-based pharmacophoric descriptors were enumerated by computing the interdistance between perceived features and MCS-aligned 'centroid' position. The descriptor set and biological activities were used to develop support vector machine models to predict the activities of the external test set. Finally, fitness score was estimated based on pharmacophore similarity with its bin-specific reference molecules to recognize the best and poor alignments and, also with each reference molecule to predict outliers of the quantitative hypothesis model. We applied this procedure to a diverse data-set of 40 HIV-1 integrase inhibitors and discussed its effectiveness with the reported CPH model.

DRUG TARGET PRIORITIZATION IN PLASMODIUM FALCIPARUM THROUGH METABOLIC NETWORK ANALYSIS, AND INHIBITOR DESIGNING USING VIRTUAL SCREENING AND DOCKING APPROACH

The genome sequence of Plasmodium falciparum reveals that many metabolic pathways are unique as compared to its human host. Metabolic Network Analysis was carried out to find the essential enzymes critical for the survival of the pathogen. In the present study, choke point and load point analysis was used to locate putative targets. The identified targets were further checked to confirm that no alternate pathway or human homolog exists. Among the top 15 enzymes obtained from this analysis, we have selected P. falciparum orotidine-5'-monophosphate decarboxylase (PfODCase) enzyme as it is sequentially and structurally different from that of humans, for searching novel inhibitors. A five-point 3D pharmacophore was generated for the crystal structure of PfODCase complexes with uridine-5'-monophosphate (U5P). The binding site environment shows three H-bond acceptors, one H-bond donor and one negative ionizable feature. This pharmacophore model was used as a 3D query to perform virtual screening experiments against 2,664,779 standard lead compounds obtained from the freely available ZINC database. Top 10 hits obtained from virtual screening were selected for molecular docking experiments against PfODCase in order to verify their results and to have a better insight into their binding modes. Here, docking of U5P with PfODCase is used as a control. We have identified six compounds, among them, few are U5P analogs and others are novel ones with diverse scaffolds. The key residues: Lys42, Asp20, Lys72, Ser127, Ala184, Gln185 and Arg203 at the main binding pocket of PfODCase are responsible for better stability of diverse ligands. These compounds according to their free energy of binding could serve as potent leads for designing novel inhibitors against malarial ODCase enzyme.

State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions

During the first edition of the Computationally Driven Drug Discovery meeting, held in November 2011 at Dompé Pharma (L'Aquila, Italy), a questionnaire regarding the diffusion and the use of computational tools for drug-design purposes in both academia and industry was distributed among all participants. This is a follow-up of a previously reported investigation carried out among a few companies in 2007. The new questionnaire implemented five sections dedicated to: research group identification and classification; 18 different computational techniques; software information; hardware data; and economical business considerations. In this article, together with a detailed history of the different computational methods, a statistical analysis of the survey results that enabled the identification of the prevalent computational techniques adopted in drug-design projects is reported and a profile of the computational medicinal chemist currently working in academia and pharmaceutical companies in Italy is highlighted.

Identification of novel breast cancer resistance protein (BCRP) inhibitors by virtual screening

Breast cancer resistance protein (BCRP; ABCG2) is an efflux transporter that plays an important role in multidrug resistance to antineoplastic drugs. The identification of drugs as BCRP inhibitors could aid in designing better therapeutic strategies for cancer treatment and will be critical for identifying potential drug-drug interactions. In the present study, we applied ligand-based virtual screening combined with experimental testing for the identification of novel drugs that can possibly interact with BCRP. Bayesian and pharmacophore models generated with known BCRP inhibitors were validated with an external test set. The resulting models were applied to predict new potential drug candidates from a database with more than 2000 FDA-approved drugs. Thirty-three drugs were tested in vitro for their inhibitory effects on BCRP-mediated transport of [(3)H]-mitoxantrone in MCF-7/AdrVp cells. Nineteen drugs were identified with significant inhibitory effect on BCRP transport function. The combined strategy of computational and experimental approaches in this paper has suggested potential drug candidates and thus represents an effective tool for rational identification of modulators of other proteins.

Identification of novel activators of constitutive androstane receptor from FDA-approved drugs by integrated computational and biological approaches

PURPOSE

The constitutive androstane receptor (CAR, NR1I3) is a xenobiotic sensor governing the transcription of numerous hepatic genes associated with drug metabolism and clearance. Recent evidence suggests that CAR also modulates energy homeostasis and cancer development. Thus, identification of novel human (h) CAR activators is of both clinical importance and scientific interest.

METHODS

Docking and ligand-based structure-activity models were used for virtual screening of a database containing over 2000 FDA-approved drugs. Identified lead compounds were evaluated in cell-based reporter assays to determine hCAR activation. Potential activators were further tested in human primary hepatocytes (HPHs) for the expression of the prototypical hCAR target gene CYP2B6.

RESULTS

Nineteen lead compounds with optimal modeling parameters were selected for biological evaluation. Seven of the 19 leads exhibited moderate to potent activation of hCAR. Five out of the seven compounds translocated hCAR from the cytoplasm to the nucleus of HPHs in a concentration-dependent manner. These compounds also induce the expression of CYP2B6 in HPHs with rank-order of efficacies closely resembling that of hCAR activation.

CONCLUSION

These results indicate that our strategically integrated approaches are effective in the identification of novel hCAR modulators, which may function as valuable research tools or potential therapeutic molecules.

Conformer generation with OMEGA: learning from the data set and the analysis of failures

We recently published a high quality validation set for testing conformer generators, consisting of structures from both the PDB and the CSD (Hawkins, P. C. D. et al. J. Chem. Inf. Model. 2010, 50, 572.), and tested the performance of our conformer generator, OMEGA, on these sets. In the present publication, we focus on understanding the suitability of those data sets for validation and identifying and learning from OMEGA's failures. We compare, for the first time we are aware of, the coverage of the applicable property spaces between the validation data sets we used and the parent compound sets to determine if our data sets adequately sample these property spaces. We also introduce the concept of torsion fingerprinting and compare this method of dissimilation to the more traditional graph-centric diversification methods we used in our previous publication. To improve our ability to programmatically identify cases where the crystallographic conformation is not well reproduced computationally, we introduce a new metric to compare conformations, RMSTanimoto. This new metric is used alongside those from our previous publication to efficiently identify reproduction failures. We find RMSTanimoto to be particularly effective in identifying failures for the smallest molecules in our data sets. Analysis of the nature of these failures, particularly those for the CSD, sheds further light on the issue of strain in crystallographic structures. Some of the residual failure cases not resolved by simple changes in OMEGA's defaults present significant challenges to conformer generation engines like OMEGA and are a source of new avenues to further improve their performance, while others illustrate the pitfalls of validating against crystallographic ligand conformations, particularly those from the PDB.

Biasing conformational ensembles towards bioactive-like conformers for ligand-based drug design

IMPORTANCE OF THE FIELD

In silico or virtual screening has become a common practice in contemporary computer-aided drug discovery efforts and currently constitutes a reasonably mature paradigm. Application of ligand-based approaches to virtual screening requires the ability to identify the bioactive conformers of drug-like compounds as these conformers are expected to elicit the biological activity. However, given the complexity of the energy potential surfaces of such ligands and in particular those exhibiting some degree of flexibility and the limitation of contemporary energy functions, this is not an easy task.

AREAS COVERED IN THIS REVIEW

The current contribution provides an in-depth review of recent developments in the field of generating conformational ensembles of drug-like compounds with a particular emphasis of focusing such ensembles on bioactive conformers using both energy and structural criteria. The literature reviewed in this manuscript roughly covers the last decade.

WHAT THE READER WILL GAIN

Readers of this review will gain an appreciation for the complexity of identifying bioactive conformers of drug-like compounds and an exposure to the different computational methods which were developed in order to tackle this problem as well as to the remaining challenges in this field.

TAKE HOME MESSAGE

The identification of ensembles of bioactive conformers of drug-like compounds is far from being a solved problem. Recent research has advanced the field to the point where bioactive conformers could be readily identified from within conformational ensembles generated by contemporary computational tools. However, as such conformers are inevitably accompanied by many other non-relevant conformations, a focusing mechanism is required. New methods in this field are showing promise but more work is clearly needed. New research lines are proposed which are believed to enhance the performances and with it the usefulness of 3D ligand-based methods in drug discovery and development.

QSAR studies on imidazopyrazine derivatives as Aurora A kinase inhibitors

Aurora kinases have emerged as attractive targets for the development of novel anti-cancer agents. A combined study of molecular docking, pharmacophore modelling and 3D-QSAR was performed on a series of imidazo [1, 2-a] pyrazines as novel Aurora kinase inhibitors to gain insights into the structural determinants and their structure-activity relationship. An ensemble of conformations based on molecular docking was used for PHASE pharmacophore studies. The developed best-fitted pharmacophore model was validated by diverse chemotypes of Aurora A kinase inhibitors and was consistent with the structural requirements for the docked binding mechanism. Subsequently, the pharmacophore-based alignment was used to develop PHASE and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models. The best CoMSIA model showed good statistics (q (2 )= 0.567, r (2 )= 0.992), and the predictive ability of the model was validated using an external test set of 13 compounds giving a satisfactory prediction ([Formula: see text]). The 3D contour maps provided insight into the binding mechanism and highlighted key structural features that are essential to the inhibitory activity. Based on the PHASE and CoMSIA 3D-QSAR results, a set of novel Aurora A inhibitors were designed that showed excellent potencies.