Dynamic Pharmacophore Models Research Articles

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB.

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

Multi-conformational frame from molecular dynamics as a structure-based pharmacophore model for mapping, screening and identifying ligands against PPAR-γ: a new protocol to develop promising candidates

Despite intensive research on clinical and molecular factors, the development of antidiabetic drugs in the last few decades is decelerating and as a result, the number of drugs approved by the US FDA is reduced. Hence, there is a persistent need for the innovative development of novel anti-diabetic drugs. Recent studies have provided ample proof that the peroxisome proliferator-activated receptor gamma (PPARγ), a ligand-activated transcription factor and its co-activator PGC-1 alpha may serve as good candidates for the treatment of several metabolic disorders. Therefore, in this study, 50 ns molecular dynamics (MD) simulations of the ligand-receptor complex were carried out and the most populated cluster of rosiglitazone bound to crucial amino acids during dynamics studies were selected to generate multi-conformation frame and further dynamic pharmacophore models. Finally, three pharmacophore models were generated, and 10 hits were retrieved as final lead candidates by virtual screening of ZINC database and molecular docking. The study reveals that the amino acids Met364, Lys367, His449, Leu453, Leu469, and Tyr473 play a crucial role in the binding of the compounds at the active site of PPARγ and the selected compounds from the ZINC database showed promising binding as compared to rosiglitazone. Further, ADMET studies were carried out to define the pharmacokinetic properties of promising PPARγ ligand candidates. Communicated by Ramaswamy H. Sarma

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket.

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.

Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors

Acetylcholinesterase is a critical enzyme that regulates neurotransmission by catalyzing the breakdown of neurotransmitter acetylcholine in synapses of the nervous system. It is an important target for therapeutic drugs that treat Alzheimer’s disease. Since, the degree of flexibility of the side chains of the residues in the active-site gorge of Acetylcholinesterase is diverse it results in different bound ligand conformations. The side-chain conformations of Ser293, Tyr341, Leu76, and Val73 are flexible, while the side-chain conformations of Tyr72, Tyr 124, Ser125, Phe295, and Arg296 appear to be fixed. In this study, multi-conformation dynamic pharmacophore models from the donepezyl-binding pocket based on highly populated structures chosen from molecular dynamics simulations were used for screening compounds that can properly bind acetylcholinesterase. Based on these structures, three pharmacophore models were generated. Consequently, 14 hits were retrieved as final candidates by utilizing virtual screening of ZINC database and molecular docking.

Towards Dynamic Pharmacophore Models by Coarse Grained Molecular Dynamics

Pharmacophore models play a crucial role in computer aided drug discovery e.g. in virtual screening, de novo drug design, and lead optimization. Due to the increased numbers of protein structures elucidated, structure-based methods for developing pharmacophore models have started gaining in popularity and are becoming of particular importance. There has been a number of studies combining such methods with the use of MD simulations to model protein flexibility. In the MARTINI forcefield, four heavy atoms are represented with the use of a single interaction centre. Four types of interactions have been parametrised in the model, polar, charged, non-polar and apolar. These interactions coincide with some of the typical features found in a pharmacophore model, allowing the CG atoms to be used as pharmacophoric probes. These probes are then used in CG MD simulations in order to explore protein interaction propensities. This approach, in combination with cavity detection methods, allows for the identification of potential ligand binding sites and the detection of ‘hotspot’ interactions that enhance ligand binding. Using CDK2 as a test case, we demonstrate the ability of this method to recapitulate the positions of moieties that contribute to binding interactions commonly observed for ligands known to bind CDK2 and for which crystal structures are available. When examining the ATP binding site of CDK2, a comprehensive and accurate map of the interactions which play a role in ligand binding is generated. The interaction sites identified are then compared with holo crystal structures and are shown to correctly identify the moieties which contribute to the binding interactions. By calculating the ΔGInt values of the interaction maps, we are able to further focus on the areas of interest and identify which moieties are the driving force behind binding.

A novel inhibitor of Plasmodium falciparum spermidine synthase: a twist in the tail.

BackgroundPlasmodium falciparum is the most pathogenic of the human malaria parasite species and a major cause of death in Africa. It’s resistance to most of the current drugs accentuates the pressing need for new chemotherapies. Polyamine metabolism of the parasite is distinct from the human pathway making it an attractive target for chemotherapeutic development. Plasmodium falciparum spermidine synthase (PfSpdS) catalyzes the synthesis of spermidine and spermine. It is a major polyamine flux-determining enzyme and spermidine is a prerequisite for the post-translational activation of P. falciparum eukaryotic translation initiation factor 5A (elF5A). The most potent inhibitors of eukaryotic SpdS’s are not specific for PfSpdS.Methods‘Dynamic’ receptor-based pharmacophore models were generated from published crystal structures of SpdS with different ligands. This approach takes into account the inherent flexibility of the active site, which reduces the entropic penalties associated with ligand binding. Four dynamic pharmacophore models were developed and two inhibitors, (1R,4R)-(N1-(3-aminopropyl)-trans-cyclohexane-1,4-diamine (compound 8) and an analogue, N-(3-aminopropyl)-cyclohexylamine (compound 9), were identified.ResultsA crystal structure containing compound 8 was solved and confirmed the in silico prediction that its aminopropyl chain traverses the catalytic centre in the presence of the byproduct of catalysis, 5′-methylthioadenosine. The IC50 value of compound 9 is in the same range as that of the most potent inhibitors of PfSpdS, S-adenosyl-1,8-diamino-3-thio-octane (AdoDATO) and 4MCHA and 100-fold lower than that of compound 8. Compound 9 was originally identified as a mammalian spermine synthase inhibitor and does not inhibit mammalian SpdS. This implied that these two compounds bind in an orientation where their aminopropyl chains face the putrescine binding site in the presence of the substrate, decarboxylated S-adenosylmethionine. The higher binding affinity and lower receptor strain energy of compound 9 compared to compound 8 in the reversed orientation explained their different IC50 values.ConclusionThe specific inhibition of PfSpdS by compound 9 is enabled by its binding in the additional cavity normally occupied by spermidine when spermine is synthesized. This is the first time that a spermine synthase inhibitor is shown to inhibit PfSpdS, which provides new avenues to explore for the development of novel inhibitors of PfSpdS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-0572-z) contains supplementary material, which is available to authorized users.

Molecular Modeling of the 3D Structure of 5-HT1AR: Discovery of Novel 5-HT1AR Agonists via Dynamic Pharmacophore-Based Virtual Screening

The serotonin receptor subtype 1A (5-HT(1A)R) has been implicated in several neurological conditions, and potent 5-HT(1A)R agonists have therapeutic potential for the treatment of depression, anxiety, schizophrenia, and Parkinson's disease. In the present study, a homology model of 5-HT(1A)R was built based on the latest released high-resolution crystal structure of the β₂AR in its active state (PDB: 3SN6). A dynamic pharmacophore model, which takes the receptor flexibility into account, was constructed, validated, and applied to our dynamic pharmacophore-based virtual screening approach with the aim to identify potential 5-5-HT(1A)R agonists. The obtained hits were subjected to 55-HT(1A)R binding and functional assays, and 10 compounds with medium or high K(i) and EC₅₀ values were identified. Among them, FW01 (K(i) = 51.9 nM, EC₅₀ = 7 nM) was evaluated as the strongest agonist for 5-HT(1A)R. The active 5-HT(1A)R model and dynamic pharmacophore model obtained from this study can be used for future discovery and design of novel 5-HT(1A)R agonists. Also, by integrating all computational and available experimental data, a stepwise 5-HT(1A)R signal transduction model induced by agonist FW01 was proposed.

Approximating Protein Flexibility through Dynamic Pharmacophore Models: Application to Fatty Acid Amide Hydrolase (FAAH)

A structure-based drug discovery method is described that incorporates target flexibility through the use of an ensemble of protein conformations. The approach was applied to fatty acid amide hydrolase (FAAH), a key deactivating enzyme in the endocannabinoid system. The resultant dynamic pharmacophore models are rapidly able to identify known FAAH inhibitors over drug-like decoys. Different sources of FAAH conformational ensembles were explored, with both snapshots from molecular dynamics simulations and a group of X-ray structures performing well. Results were compared to those from docking and pharmacophore models generated from a single X-ray structure. Increasing conformational sampling consistently improved the pharmacophore models, emphasizing the importance of incorporating target flexibility in structure-based drug design.

Dynamics-Based Discovery of Allosteric Inhibitors: Selection of New Ligands for the C-terminal Domain of Hsp90.

The study of allosteric functional modulation in dynamic proteins is attracting increasing attention. In particular, the discovery of new allosteric sites may generate novel opportunities and strategies for drug development, overcoming the limits of classical active-site oriented drug design. In this paper, we report on the results of a novel, ab initio, fully computational approach for the discovery of allosteric inhibitors based on the physical characterization of signal propagation mechanisms in proteins and apply it to the important molecular chaperone Hsp90. We first characterize the allosteric "hot spots" involved in interdomain communication pathways from the nucleotide-binding site in the N-domain to the distal C-domain. On this basis, we develop dynamic pharmacophore models to screen drug libraries in the search for small molecules with the functional and conformational properties necessary to bind these "hot spot" allosteric sites. Experimental tests show that the selected moelcules bind the Hsp90 C-domain, exhibit antiproliferative activity in different tumor cell lines, while not affecting proliferation of normal human cells, destabilize Hsp90 client proteins, and disrupt association with several cochaperones known to bind the N- and M-domains of Hsp90. These results prove that the hits alter Hsp90 function by affecting its conformational dynamics and recognition properties through an allosteric mechanism. These findings provide us with new insights on the discovery and development of new allosteric inhibitors that are active on important cellular pathways through computational biology. Though based on the specific case of Hsp90, our approach is general and can readily be extended to other target proteins and pathways.

Emerging Methods for Ensemble-Based Virtual Screening

Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.

Dynamic Pharmacophore Model Optimization: Identification of Novel HIV-1 Integrase Inhibitors

We extended the previously described dynamic pharmacophore model studies of HIV-1 integrase (IN) by considering more key residues in the active site, including Mg2+. First, we applied a Monte Carlo sampling method to map the complementary features of the IN binding surface. Two types of dynamic pharmacophore models were generated. One considers Mg2+ as part of the IN and therefore as an excluded volume, and the other treats Mg2+ as a positively charged feature, representing a new type of pharmacophore model aimed to identify compounds potentially preventing Mg2+ binding. Second, we validated the models with 385 known active (IC50 < 20 microM) and 235 (IC50 > 100 microM) inactive IN inhibitors. Third, we used the derived models to screen our small molecule database. Twenty-two structurally novel compounds were tested in an in vitro assay specific for IN, and two of them showed IC50 < or = 10 microM for strand transfer reaction.

Dynamic Receptor-Based Pharmacophore Model Development and Its Application in Designing Novel HIV-1 Integrase Inhibitors

We present here a dynamic receptor-based pharmacophore model representing the complementary features of the active site region of HIV-1 integrase (IN), which was developed from a series of representative conformations of IN. Conformations of IN were sampled through a molecular dynamics study of the catalytic domain of an IN monomer, and an ensemble of representative IN structures were collected via a probability-based representative conformer sampling method that considers both the potential energy and the structural similarity of the protein conformations. The dynamic pharmacophore model was validated by a set of 128 known inhibitors, and the results showed that over 72% of the active inhibitors (IC(50) lower than 20 microM) could be successfully identified by the dynamic model. Therefore, we screened our in-house database of commercially available compounds against this model and successfully identified a set of structurally novel IN inhibitors. Compounds 7 and 18 with IC(50)s of 8 microM and 15 microM, respectively, against the strand transfer reaction were the most potent. Moreover, 7, 8 and 20 showed a 5-fold selectivity for the strand transfer reaction over 3'-processing.

A structure-based design approach for the identification of novel inhibitors: application to an alanine racemase.

We report a new structure-based strategy for the identification of novel inhibitors. This approach has been applied to Bacillus stearothermophilus alanine racemase (AlaR), an enzyme implicated in the biosynthesis of the bacterial cell wall. The enzyme catalyzes the racemization of L- and D-alanine using pyridoxal 5'-phosphate (PLP) as a cofactor. The restriction of AlaR to bacteria and some fungi and the absolute requirement for D-alanine in peptidoglycan biosynthesis make alanine racemase a suitable target for drug design. Unfortunately, known inhibitors of alanine racemase are not specific and inhibit the activity of other PLP-dependent enzymes, leading to neurological and other side effects. This article describes the development of a receptor-based pharmacophore model for AllaR, taking into account receptor flexibility (i.e. a 'dynamic' pharmacophore model). In order to accomplish this, molecular dynamics (MD) simulations were performed on the full AlaR dimer from Bacillus stearothermophilus (PDB entry, 1 sft) with a D-alanine molecule in one active site and the non-covalent inhibitor, propionate, in the second active site of this homodimer. The basic strategy followed in this study was to utilize conformations of the protein obtained during MD simulations to generate a dynamic pharmacophore model using the property mapping capability of the LigBuilder program. Compounds from the Available Chemicals Directory that fit the pharmacophore model were identified and have been submitted for experimental testing. The approach described here can be used as a valuable tool for the design of novel inhibitors of other biomolecular targets.

Developing a dynamic pharmacophore model for HIV-1 integrase.

We present the first receptor-based pharmacophore model for HIV-1 integrase. The development of "dynamic" pharmacophore models is a new method that accounts for the inherent flexibility of the active site and aims to reduce the entropic penalties associated with binding a ligand. Furthermore, this new drug discovery method overcomes the limitation of an incomplete crystal structure of the target protein. A molecular dynamics (MD) simulation describes the flexibility of the uncomplexed protein. Many conformational models of the protein are saved from the MD simulations and used in a series of multi-unit search for interacting conformers (MUSIC) simulations. MUSIC is a multiple-copy minimization method, available in the BOSS program; it is used to determine binding regions for probe molecules containing functional groups that complement the active site. All protein conformations from the MD are overlaid, and conserved binding regions for the probe molecules are identified. Those conserved binding regions define the dynamic pharmacophore model. Here, the dynamic model is compared to known inhibitors of the integrase as well as a three-point, ligand-based pharmacophore model from the literature. Also, a "static" pharmacophore model was determined in the standard fashion, using a single crystal structure. Inhibitors thought to bind in the active site of HIV-1 integrase fit the dynamic model but not the static model. Finally, we have identified a set of compounds from the Available Chemicals Directory that fit the dynamic pharmacophore model, and experimental testing of the compounds has confirmed several new inhibitors.

Method for Including the Dynamic Fluctuations of a Protein in Computer-Aided Drug Design

We have recently presented a new pharmacophore design method that allows for the incorporation of the inherent flexibility of a target active site. The flexibility of the enzymatic system is described by collecting many conformations of the uncomplexed protein; this ensemble of conformational states can come from a molecular dynamics (MD) simulation, multiple crystal structures, or many NMR structures. Binding sites for functional groups that complement the active site are determined through multiple-copy calculations. These calculations are conducted for each protein conformation, providing a large collection of potential binding sites. The Cartesian coordinates from each protein conformation are overlaid through RMS fitting of essential catalytic residues, and the pharmacophore model is described by binding regions that are conserved over many protein conformations. Previously, we developed a “dynamic” pharmacophore model for HIV-1 integrase using 11 conformations of the protein from an MD simulation; the MUSIC procedure was used to calculate binding positions for methanol molecules in each configuration of the active site. Here we present “static” pharmacophore models developed with a single conformation of the protein from two new crystal structures (standard protocol for multiple-copy methods). The static models do not perform as well as the previous dynamic model in fitting known inhibitors of HIV-1 integrase. To test the applicability of the dynamic pharmacophore method and the assumption that any reliable source of protein conformations is applicable, we have now developed a second dynamic pharmacophore model based on the two crystal structures also used for the development of the static models. Though the dynamic model based on the two crystal structures does not fit as many known inhibitors as the previous dynamic model, it is a significant improvement over the static models. Even better performance is expected with the addition of new crystal structures as they become available. However, it is notable that using only two structures leads to great improvement in the models.