A single glycine residue at the entrance to the first membrane-spanning domain of the gamma-aminobutyric acid type A receptor beta(2) subunit affects allosteric sensitivity to GABA and anesthetics

Structure-Based Drug Design and Development of Novel Synthetic Compounds with Anti-Viral Property against SARS-COV-2

• Background : The world is suffering from health and economic devastation due to the Corona Virus Disease-2019 (COVID-19) pandemic. Given the number of people affected and also the death rate, the virus is definitely a serious threat to humanity. By analogy with previous reports on the Severe Acute Respiratory Syndrome (SARS-CoV-2) virus, the novel replication mechanism of the coronavirus is likely well understood. • Objective : The antiviral activity of various compounds of the flavonoid class was checked against SARS-COVID-19 using diverse tools and software. • Method : From the flavonoid compound class, 100 synthetic compounds with potential antiviral activity were selected and improved for screening and induced fit docking, which was reduced to 25 compounds with good docking score and docking energy. In addition to the apparent match of the molecule with the shape of the binding pocket, a full analysis of the non-covalent interactions in the active site was assessed. • Results : Compounds (nol26), (fla37-fl40), (an32), (an39) showed a maximum docking score, which shows essential interactions for a tight bond. Now, all compounds are synthetic with beneficial drug-like properties. • Conclusion : During the docking study, an increased lipophilic interaction of compounds due to the presence of chlorine in (nol26), (fla37-fl40), (an32), (an39) was discovered. (fla37-fla40) can be investigated as lead molecules against SARS-COV-2 in futuristic drug development.

Cosolvent and Dynamic Effects in Binding Pocket Search by Docking Simulations

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

BANΔIT: B'-Factor Analysis for Drug Design and Structural Biology

The analysis of B‐factor profiles from X‐ray protein structures can be utilized for structure‐based drug design since protein mobility changes have been associated with the quality of protein‐ligand interactions. With the BANΔIT (B’‐factor analysis and ΔB’ interpretation toolkit), we have developed a JavaScript‐based browser application that provides a graphical user interface for the normalization and analysis of B’‐factor profiles. To emphasize the usability for rational drug design applications, we have analyzed a selection of crystallographic protein‐ligand complexes and have given exemplary conclusions for further drug optimization including the development of a B’‐factor‐supported pharmacophore model for SARS CoV‐2 main protease inhibitors. BANΔIT is available online at https://bandit.uni‐mainz.de. The source code can be downloaded from https://github.com/FBarthels/BANDIT.

Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study

Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein-ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT.

CANDOCK: Chemical Atomic Network-Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

Small-molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations such as improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g., cofactors, metal ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm, that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions, and cofactor interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind, Astex, and PINC proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions such that the statistical score of the best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best-docked pose with biological activity. CANDOCK along with all structures and scripts used for benchmarking is available at https://github.com/chopralab/candock_benchmark.

A structural look at GABAA receptor mutations linked to epilepsy syndromes

Understanding the genetic variation in GABA_A receptor subunit genes (GABRs), GABRA1-6, GABRB1-3, GABRG1-3 and GABRD, in individuals affected by epilepsy may improve the diagnosis and treatment of epilepsy syndromes through identification of disease-associated variants. However, the lack of functional analysis and validation of many novel and previously reported familial and de novo mutations have made it challenging to address meaningful gene associations with epilepsy syndromes. GABA_A receptors belong to the Cys-loop receptor family. Even though GABA_A receptor mutant residues are widespread among different GABRs, their frequent occurrence in important structural domains that share common functional features suggests associations between structure and function.

GABAA receptor: Positive and negative allosteric modulators

gamma-Aminobutyric acid (GABA)-mediated inhibitory neurotransmission and the gene products involved were discovered during the mid-twentieth century. Historically, myriad existing nervous system drugs act as positive and negative allosteric modulators of these proteins, making GABA a major component of modern neuropharmacology, and suggesting that many potential drugs will be found that share these targets. Although some of these drugs act on proteins involved in synthesis, degradation, and membrane transport of GABA, the GABA receptors Type A (GABAAR) and Type B (GABABR) are the targets of the great majority of GABAergic drugs. This discovery is due in no small part to Professor Norman Bowery. Whereas the topic of GABABR is appropriately emphasized in this special issue, Norman Bowery also made many insights into GABAAR pharmacology, the topic of this article. GABAAR are members of the ligand-gated ion channel receptor superfamily, a chloride channel family of a dozen or more heteropentameric subtypes containing 19 possible different subunits. These subtypes show different brain regional and subcellular localization, age-dependent expression, and potential for plastic changes with experience including drug exposure. Not only are GABAAR the targets of agonist depressants and antagonist convulsants, but most GABAAR drugs act at other (allosteric) binding sites on the GABAAR proteins. Some anxiolytic and sedative drugs, like benzodiazepine and related drugs, act on GABAAR subtype-dependent extracellular domain sites. General anesthetics including alcohols and neurosteroids act at GABAAR subunit-interface trans-membrane sites. Ethanol at high anesthetic doses acts on GABAAR subtype-dependent trans-membrane domain sites. Ethanol at low intoxicating doses acts at GABAAR subtype-dependent extracellular domain sites. Thus GABAAR subtypes possess pharmacologically specific receptor binding sites for a large group of different chemical classes of clinically important neuropharmacological agents. This article is part of the "Special Issue Dedicated to Norman G. Bowery".

High Constitutive Activity Accounts for the Combination of Enhanced Direct Activation and Reduced Potentiation in Mutated GABAA Receptors

GABAA receptors activated by the transmitter GABA are potentiated by several allosterically acting drugs, including the intravenous anesthetic propofol. Propofol can also directly activate the receptor, albeit at higher concentrations. Previous functional studies have identified amino acid residues whose substitution reduces potentiation of GABA-activated receptors by propofol while enhancing the ability of propofol to directly activate the receptor. One interpretation of such observations is that the mutation has specific effects on the sites or processes involved in potentiation or activation. We show here that divergent effects on potentiation and direct activation can be mediated by increased constitutive open probability in the mutant receptor without any specific effect on the interactions between the allosteric drug and the receptor. By simulating GABAA receptor activity using the concerted transition model, we demonstrate that the predicted degree of potentiation is reduced as the level of constitutive activity increases. The model further predicts that a potentiating effect of an allosteric modulator is a computable value that depends on the level of constitutive activity, the amplitude of the response to the agonist, and the amplitude of the direct activating response to the modulator. Specific predictions were confirmed by electrophysiological data from the binary α1β3 and concatemeric ternary β2α1γ2L+β2α1 GABAA receptors. The corollaries of reduced potentiation due to increased constitutive activity are isobolograms that conform to simple additivity and a loss of separation between the concentration-response relationships for direct activation and potentiation.

Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23

Ensemble docking is now commonly used in early-stage in silico drug discovery and can be used to attack difficult problems such as finding lead compounds which can disrupt protein-protein interactions. We give an example of this methodology here, as applied to fibroblast growth factor 23 (FGF23), a protein hormone that is responsible for regulating phosphate homeostasis. The first small-molecule antagonists of FGF23 were recently discovered by combining ensemble docking with extensive experimental target validation data (Science Signaling, 9, 2016, ra113). Here, we provide a detailed account of how ensemble-based high-throughput virtual screening was used to identify the antagonist compounds discovered in reference (Science Signaling, 9, 2016, ra113). Moreover, we perform further calculations, redocking those antagonist compounds identified in reference (Science Signaling, 9, 2016, ra113) that performed well on drug-likeness filters, to predict possible binding regions. These predicted binding modes are rescored with the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) approach to calculate the most likely binding site. Our findings suggest that the antagonist compounds antagonize FGF23 through the disruption of protein-protein interactions between FGF23 and fibroblast growth factor receptor (FGFR).

Deep analysis of N-cadherin/ADH-1 interaction: a computational survey

Due to the considerable role of N-cadherin in cancer metastasis, tumor growth, and progression, inhibition of this protein has been highly regarded in recent years. Although ADH-1 has been known as an appropriate inhibitor of N-cadherin in clinical trials, its chemical nature and binding mode with N-cadherin have not been precisely specified yet. Accordingly, in this study, quantum mechanics calculations were used to investigate the chemical nature of ADH-1. These calculations clarify the molecular properties of ADH-1 and determine its reactive sites. Based on the results, the oxygen atoms are suitable for electrophilic reactivity, while the hydrogen atoms that are connected to nitrogen atoms are the favorite sites for nucleophilic reactivity. The higher electronegativity of the oxygen atoms makes them the most reactive portions in this molecule. Molecular docking and molecular dynamics (MD) simulation have also been applied to specify the binding mode of ADH-1 with N-cadherin and determine the important residues of N-cadherin involving in the interaction with ADH-1. Moreover, the verified model by MD simulation has been studied to extract the free energy value and find driving forces. These calculations and molecular electrostatic potential map of ADH-1 indicated that hydrophobic and electrostatic interactions are almost equally involved in the implantation of ADH-1 in the N-cadherin binding site. The presented results not only enable a closer examination of N-cadherin in complex with ADH-1 molecule, but also are very beneficial in designing new inhibitors for N-cadherin and can help to save time and cost in this field.

Dynamic changes in binding interaction networks of sex steroids establish their non-classical effects

Non-classical signaling in the intracellular second messenger system plays a pivotal role in the cytoprotective effect of estradiol. Estrogen receptor is a common target of sex steroids and important in mediating estradiol-induced neuroprotection. Whereas the mechanism of genomic effects of sex steroids is fairly understood, their non-classical effects have not been elucidated completely. We use real time molecular dynamics calculations to uncover the interaction network of estradiol and activator estren. Besides steroid interactions, we also investigate the co-activation of the receptor. We show how steroid binding to the alternative binding site of the non-classical action is facilitated by the presence of a steroid in the classical binding site and the absence of the co-activator peptide. Uncovering such dynamic mechanisms behind steroid action will help the structure-based design of new drugs with non-classical responses and cytoprotective potential.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

Binding site opening by loop C shift and chloride ion-pore interaction in the GABAAreceptor model

Molecular dynamics simulations of the shut α₁β₂γ₂GABA_Aheteropentamer receptor homology model reveal significant differences between intersubunit interfaces (ligand binding G1, G2 and non-binding) compared to homomeric receptor assemblies and possible ion interaction sites in the top part of the transmembrane domain (TMD).

Energetics and structural characterization of the “DFG-flip” conformational transition of B-RAF kinase: a SITS molecular dynamics study

A combination of a homology modeling technique and an enhanced sampling molecular dynamics simulation implemented using the SITS method is employed to compute a detailed map of the free-energy landscape and explore the conformational transition pathway of B-RAF kinase.

Multistep Modeling Strategy To Improve the Binding Affinity Prediction of PET Tracers to Aβ42: Case Study with Styrylbenzoxazole Derivatives

Positron emission tomography (PET) tracers play an important role in the diagnosis of Alzheimer's disease, a condition that leads to progressive dementia and memory loss. A high binding affinity and specificity of the PET tracers to amyloid oligomers and fibrils are crucial for their successful application as diagnostic agents. In this sense, it is essential to design PET tracers with enhanced binding affinities, which can lead to more precise and earlier detection of Alzheimer's disease conditions. The application of in silico methodology for the design and development of efficient PET tracers may serve as an important route to improved Alzheimer's disease diagnosis. In this work, the performance of widely used computational methods is explored for predicting experimental binding affinities of styrylbenzoxazole (SB) derivatives against a common amyloid protofibril. By performing docking, molecular dynamics, and quantum chemistry calculations in sequence their combined predictive performance is explored. The present work emphasizes the merits as well as limitations of these simulation strategies in the realm of designing PET tracers for Alzheimer's disease diagnosis.

Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations

In silico virtual screening (VS) is a powerful hit identification technique used in drug discovery projects that aims to effectively distinguish true actives from inactive or decoy molecules. To better capture the dynamic behavior of protein drug targets, compound databases may be screened against an ensemble of protein conformations, which may be experimentally determined or generated computationally, i.e. via molecular dynamics (MD) simulations. Several studies have shown that conformations generated by MD are useful in identifying novel hit compounds, in part because structural rearrangements sampled during MD can provide novel targetable areas. However, it remains difficult to predict a priori when an MD conformation will outperform a VS against the crystal structure alone. Here, we assess whether MD conformations result in improved VS performance for six protein kinases. MD conformations are selected using three different methods, and their VS performances are compared to the corresponding crystal structures. Additionally, these conformations are used to train ensembles, and their VS performance is compared to the individual MD conformations and the corresponding crystal structures using receiver operating characteristic curve (ROC) metrics. We show that performing MD results in at least one conformation that offers better VS performance than the crystal structure, and that, while it is possible to train ensembles to outperform the crystal structure alone, the extent of this enhancement is target dependent. Lastly, we show that the optimal structural selection method is also target dependent and recommend optimizing virtual screens on a kinase-by-kinase basis to improve the likelihood of success.

Measuring the Elasticity of Poly-l-Proline Helices with Terahertz Spectroscopy

The rigidity of poly-l-proline is an important contributor to the stability of many protein secondary structures, where it has been shown to strongly influence bulk flexibility. The experimental Young's moduli of two known poly-l-proline helical forms, right-handed all-cis (Form I) and left-handed all-trans (Form II), were determined in the crystalline state by using an approach that combines terahertz time-domain spectroscopy, X-ray diffraction, and solid-state density functional theory. Contrary to expectations, the helices were found to be considerably less rigid than many other natural and synthetic polymers, as well as differing greatly from each other, with Young's moduli of 4.9 and 9.6 GPa for Forms I and II, respectively.

Flexible CDOCKER: Development and application of a pseudo-explicit structure-based docking method within CHARMM

Protein-ligand docking is a commonly used method for lead identification and refinement. While traditional structure-based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well-appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid-based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced-fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re-docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs.

Adaptive BP-Dock: An Induced Fit Docking Approach for Full Receptor Flexibility

We present an induced fit docking approach called Adaptive BP-Dock that integrates perturbation response scanning (PRS) with the flexible docking protocol of RosettaLigand in an adaptive manner. We first perturb the binding pocket residues of a receptor and obtain a new conformation based on the residue response fluctuation profile using PRS. Next, we dock a ligand to this new conformation by RosettaLigand, where we repeat these steps for several iterations. We test this approach on several protein test sets including difficult unbound docking cases such as HIV-1 reverse transcriptase and HIV-1 protease. Adaptive BP-Dock results show better correlation with experimental binding affinities compared to other docking protocols. Overall, the results imply that Adaptive BP-Dock can easily capture binding induced conformational changes by simultaneous sampling of protein and ligand conformations. This can provide faster and efficient docking of novel targets for rational drug design.

Drug design from the cryptic inhibitor envelope

Conformational dynamics plays an important role in enzyme catalysis, allosteric regulation of protein functions and assembly of macromolecular complexes. Despite these well-established roles, such information has yet to be exploited for drug design. Here we show by nuclear magnetic resonance spectroscopy that inhibitors of LpxC--an essential enzyme of the lipid A biosynthetic pathway in Gram-negative bacteria and a validated novel antibiotic target--access alternative, minor population states in solution in addition to the ligand conformation observed in crystal structures. These conformations collectively delineate an inhibitor envelope that is invisible to crystallography, but is dynamically accessible by small molecules in solution. Drug design exploiting such a hidden inhibitor envelope has led to the development of potent antibiotics with inhibition constants in the single-digit picomolar range. The principle of the cryptic inhibitor envelope approach may be broadly applicable to other lead optimization campaigns to yield improved therapeutics.

Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities

Understanding and controlling biological phenomena via structure-based drug screening efforts often critically rely on accurate description of protein-ligand interactions. However, most of the currently available computational techniques are affected by severe deficiencies in both protein and ligand conformational sampling as well as in the scoring of the obtained docking solutions. To overcome these limitations, we have recently developed MedusaDock, a novel docking methodology, which simultaneously models ligand and receptor flexibility. Coupled with MedusaScore, a physical force field-based scoring function that accounts for the protein-ligand interaction energy, MedusaDock, has reported the highest success rate in the CSAR 2011 exercise. Here, we present a standard computational protocol to evaluate the binding properties of the two enantiomers of the non-selective β-blocker propanolol in the β2 adrenergic receptor's binding site. We describe details of our protocol, which have been successfully applied to several other targets.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

Molecular perception of interactions between bis(7)tacrine and cystamine-tacrine dimer with cholinesterases as the promising proposed agents for the treatment of Alzheimer's disease

The infamous chronic neurodegenerative disease, Alzheimer's, that starts with short-term memory loss and eventually leads to gradual bodily function decline which has been attributed to the deficiency in brain neurotransmitters, acetylcholine, and butylcholine. As a matter of fact, design of compounds that can inhibit cholinesterases activities (acetylcholinesterase and butylcholinesterase) has been introduced as an efficient method to treat Alzheimer's. Among proposed compounds, bis(7)tacrine (B7T) is recognized as a noteworthy suppressor for Alzheimer's disease. Recently a new analog of B7T, cystamine-tacrine dimer is offered as an agent to detain Alzheimer's complications, even better than the parent compound. In this study, classical molecular dynamic simulations have been employed to take a closer look into the modes of interactions between the mentioned ligands and both cholinesterase enzymes. According to our obtained results, the structural differences in the target enzymes active sites result in different modes of interactions and inhibition potencies of the ligands against both enzymes. The obtained information can help to investigate those favorable fragments in the studied ligands skeletons that have raised the potency of the analog in comparison with the parent compound to design more potent multi target ligands to heal Alzheimer's disease.

Protein Flexibility in Drug Discovery: From Theory to Computation

Nowadays it is widely accepted that the mechanisms of biomolecular recognition are strongly coupled to the intrinsic dynamic of proteins. In past years, this evidence has prompted the development of theoretical models of recognition able to describe ligand binding assisted by protein conformational changes. On a different perspective, the need to take into account protein flexibility in structure-based drug discovery has stimulated the development of several and extremely diversified computational methods. Herein, on the basis of a parallel between the major recognition models and the simulation strategies used to account for protein flexibility in ligand binding, we sort out and describe the most innovative and promising implementations for structure-based drug discovery.

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

BACKGROUND

Plasmodium 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.

RESULTS

A 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.

CONCLUSION

The 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.

Allosteric ligands and their binding sites define γ-aminobutyric acid (GABA) type A receptor subtypes

GABAA receptors (GABA(A)Rs) mediate rapid inhibitory transmission in the brain. GABA(A)Rs are ligand-gated chloride ion channel proteins and exist in about a dozen or more heteropentameric subtypes exhibiting variable age and brain regional localization and thus participation in differing brain functions and diseases. GABA(A)Rs are also subject to modulation by several chemotypes of allosteric ligands that help define structure and function, including subtype definition. The channel blocker picrotoxin identified a noncompetitive channel blocker site in GABA(A)Rs. This ligand site is located in the transmembrane channel pore, whereas the GABA agonist site is in the extracellular domain at subunit interfaces, a site useful for low energy coupled conformational changes of the functional channel domain. Two classes of pharmacologically important allosteric modulatory ligand binding sites reside in the extracellular domain at modified agonist sites at other subunit interfaces: the benzodiazepine site and the high-affinity, relevant to intoxication, ethanol site. The benzodiazepine site is specific for certain GABA(A)R subtypes, mainly synaptic, while the ethanol site is found at a modified benzodiazepine site on different, extrasynaptic, subtypes. In the transmembrane domain are allosteric modulatory ligand sites for diverse chemotypes of general anesthetics: the volatile and intravenous agents, barbiturates, etomidate, propofol, long-chain alcohols, and neurosteroids. The last are endogenous positive allosteric modulators. X-ray crystal structures of prokaryotic and invertebrate pentameric ligand-gated ion channels, and the mammalian GABA(A)R protein, allow homology modeling of GABA(A)R subtypes with the various ligand sites located to suggest the structure and function of these proteins and their pharmacological modulation.

Computational evidence to design an appropriate candidate for the treatment of Alzheimer's disease through replacement of the heptamethylene linker of bis(7)tacrine with S-allylcysteine

Due to the multiple pathogens of Alzheimer's disease, multitarget-directed ligand (MTDL) design has been highly regarded in recent years.

Assessing the suitability of the multilevel strategy for the conformational analysis of small ligands

Predicting the conformational preferences of flexible compounds is a challenging problem in drug design, where the recognition between ligand and receptor is affected by the ability of the interacting partners to adopt a favorable conformation for the binding. To explore the conformational space of flexible ligands and to obtain the relative free energy of the conformation wells, we have recently reported a multilevel computational strategy that relies on the predominant-state approximation-where the conformational space is partitioned into distinct conformational wells-and combines a low-level method for sampling the conformational minima and high-level ab initio calculations for estimating their relative stability. In this study, we assess the performance of the multilevel strategy for predicting the conformational preferences of a series of structurally related phenylethylamines and streptomycin in aqueous solution. The charged nature of these compounds and the chemical complexity of streptomycin make them a challenging test for the multilevel approach. Furthermore, we explore the suitability of using a molecular mechanics approach as a source of approximate ensembles in the first stage of the multilevel strategy. The results support the reliability of the multilevel approach for obtaining an accurate conformational ensemble of small (bio)organic molecules in aqueous solution.

Polyphony: superposition independent methods for ensemble-based drug discovery

Background

Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates.

Results

Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered.

Conclusions

New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].

Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility

In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naïve Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naïve Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.

Exploring the structural and functional impact of the ALK F1174L mutation using bioinformatics approach

Crizotinib is the most effective and the only drug that has been approved for the treatment of anaplastic lymphoma kinase (ALK)-positive lung cancer. Reports suggest that there is a development of an acquired resistance against crizotinib action due to the emergence of several mutations in the ALK gene and F1174L is one such mutation. In this study, we used molecular docking and molecular dynamics (MD) approach to decipher the effect of F1174L mutation in drug-target binding. Docking results suggest that crizotinib was found to adopt the most promising conformations to the native-type ALK by identifying the M1199 residue as a prospective partner for making a hydrogen bond as compared to the mutant-type ALK. MD results showed that the average atom, especially atoms of the native-type ALK-crizotinib complex, movements were less, displayed less fluctuation, fast convergence of energy, and changes in geometry. This shows the stable binding of crizotinib with the native-type ALK in comparison to the mutant-type ALK. We believe that this study could be useful for the logical design of stronger, more selective, and more consistent ALK inhibitor against drug-resistant F1174L mutation.

GABAergic pharmacological activity of propofol related compounds as possible enhancers of general anesthetics and interaction with membranes

Phenol compounds, such as propofol and thymol, have been shown to act on the GABAA receptor through interaction with specific sites of this receptor. In addition, considering the high lipophilicity of phenols, it is possible that their pharmacological activity may also be the result of the interaction of phenol molecules with the surrounding lipid molecules, modulating the supramolecular organization of the receptor environment. Thus, in the present study, we study the pharmacological activity of some propofol- and thymol-related phenols on the native GABAA receptor using primary cultures of cortical neurons and investigate the effects of these compounds on the micro viscosity of artificial membranes by means of fluorescence anisotropy. The phenol compounds analyzed in this article are carvacrol, chlorothymol, and eugenol. All compounds were able to enhance the binding of [(3)H]flunitrazepam with EC50 values in the micromolar range and to increase the GABA-evoked Cl(-) influx in a concentration-dependent manner, both effects being inhibited by the competitive GABAA antagonist bicuculline. These results strongly suggest that the phenols studied are positive allosteric modulators of this receptor. Chlorothymol showed a bell-type effect, reducing its positive effect at concentrations >100 μM. The concentrations necessary to induce positive allosteric modulation of GABAA receptor were not cytotoxic. Although all compounds were able to decrease the micro viscosity of artificial membranes, chlorothymol displayed a larger effect which could explain its effects on [(3)H]flunitrazepam binding and on cell viability at high concentrations. Finally, it is suggested that these compounds may exert depressant activity on the central nervous system and potentiate the effects of general anesthetics.

In silico study on multidrug resistance conferred by I223R/H275Y double mutant neuraminidase

It was recently reported that an I223R/H275Y double mutant of neuraminidase (NA) creates a multidrug-resistant form of the pandemic influenza A (H1N1) virus. However, a comprehensive understanding of the molecular mechanisms is still lacking. We conducted a systematic in silico study to explore the structural basis underlying this multidrug resistance. By molecular docking analyses and molecular dynamics (MD) simulations, we compared various biochemical and biophysical properties of the wild type, the I223R single mutant and the I223R/H275Y double mutant NA with two inhibitors, zanamivir (ZMR) and oseltamivir (G39). The binding free energy of oseltamivir with all types of NA was substantially lower than its zanamivir counterpart. On the other hand, the binding free energy of each inhibitor with wild type NA was generally higher than that with mutant NAs. MD simulation outcomes exemplify distinct patterns for oseltamivir and zanamivir with all types of NA. In particular, the stronger resistance of the double mutant NA relative to the wild and single mutant types can be ascribed to the overall looser but locally more compact structure of the former. Specifically, as a whole the double mutant NA adapts to the larger gyration radius and greater distance between charged atom groups, which is contrary to the pattern in the local binding site region. The enhanced resistance of all types of NA to oseltamivir rather than zanamivir might be accounted for similarly. We expect these findings to provide significant insights into improving inhibitors for the multidrug-resistant neuraminidase of H1N1 influenza viruses.

Identification of Novel Histamine H4 Ligands by Virtual Screening on Molecular Dynamics Ensembles

We report the identification of novel histamine H4 receptor ligands by ensemble docking on homology model conformers derived from molecular dynamics simulations. Selected receptor models from the trajectories demonstrated superior virtual screening performance compared to the initial models. The ensemble of the best models was able to retrieve a diverse set of known H4 ligands. Prospective virtual screening against these models and subsequent in vitro experimental validation identified novel H4 ligands. Compound 3 showing highest affinity and ligand efficiency represents an interesting scaffold for further medicinal chemistry exploration.

BP-Dock: a flexible docking scheme for exploring protein-ligand interactions based on unbound structures

Molecular docking serves as an important tool in modeling protein-ligand interactions. However, it is still challenging to incorporate overall receptor flexibility, especially backbone flexibility, in docking due to the large conformational space that needs to be sampled. To overcome this problem, we developed a novel flexible docking approach, BP-Dock (Backbone Perturbation-Dock) that can integrate both backbone and side chain conformational changes induced by ligand binding through a multi-scale approach. In the BP-Dock method, we mimic the nature of binding-induced events as a first-order approximation by perturbing the residues along the protein chain with a small Brownian kick one at a time. The response fluctuation profile of the chain upon these perturbations is computed using the perturbation response scanning method. These response fluctuation profiles are then used to generate binding-induced multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, we applied our approach on a large and diverse data set using unbound structures as receptors. We also compared the BP-Dock results with bound and unbound docking, where overall receptor flexibility was not taken into account. Our results highlight the importance of modeling backbone flexibility in docking for recapitulating the experimental binding affinities, especially when an unbound structure is used. With BP-Dock, we can generate a wide range of binding site conformations realized in nature even in the absence of a ligand that can help us to improve the accuracy of unbound docking. We expect that our fast and efficient flexible docking approach may further aid in our understanding of protein-ligand interactions as well as virtual screening of novel targets for rational drug design.

Adaptability in protein structures: structural dynamics and implications in ligand design

The basic framework of understanding the mechanisms of protein functions is achieved from the knowledge of their structures which can model the molecular recognition. Recent advancement in the structural biology has revealed that in spite of the availability of the structural data, it is nontrivial to predict the mechanism of the molecular recognition which progresses via situation-dependent structural adaptation. The mutual selectivity of protein-protein and protein-ligand interactions often depends on the modulations of conformations empowered by their inherent flexibility, which in turn regulates the function. The mechanism of a protein's function, which used to be explained by the ideas of 'lock and key' has evolved today as the concept of 'induced fit' as well as the 'population shift' models. It is felt that the 'dynamics' is an essential feature to take into account for understanding the mechanism of protein's function. The design principles of therapeutic molecules suffer from the problems of plasticity of the receptors whose binding conformations are accurately not predictable from the prior knowledge of a template structure. On the other hand, flexibility of the receptors provides the opportunity to improve the binding affinity of a ligand by suitable substitution that will maximize the binding by modulating the receptors surface. In this paper, we discuss with example how the protein's flexibility is correlated with its functions in various systems, revealing the importance of its understanding and for making applications. We also highlight the methodological challenges to investigate it computationally and to account for the flexible nature of the molecules in drug design.

Exploiting conformational dynamics in drug discovery: design of C-terminal inhibitors of Hsp90 with improved activities

The interaction that occurs between molecules is a dynamic process that impacts both structural and conformational properties of the ligand and the ligand binding site. Herein, we investigate the dynamic cross-talk between a protein and the ligand as a source for new opportunities in ligand design. Analysis of the formation/disappearance of protein pockets produced in response to a first-generation inhibitor assisted in the identification of functional groups that could be introduced onto scaffolds to facilitate optimal binding, which allowed for increased binding with previously uncharacterized regions. MD simulations were used to elucidate primary changes that occur in the Hsp90 C-terminal binding pocket in the presence of first-generation ligands. This data was then used to design ligands that adapt to these receptor conformations, which provides access to an energy landscape that is not visible in a static model. The newly synthesized compounds demonstrated antiproliferative activity at ∼150 nM concentration. The method identified herein may be used to design chemical probes that provide additional information on structural variations of Hsp90 C-terminal binding site.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.