Ensemble-based virtual screening reveals potential novel antiviral compounds for avian influenza neuraminidase

Indole-core inhibitors of influenza a neuraminidase: iterative medicinal chemistry and molecular modeling

Influenza viruses that cause seasonal and pandemic flu are a permanent health threat. The surface glycoprotein, neuraminidase, is crucial for the infectivity of the virus and therefore an attractive target for flu drug discovery campaigns. We have designed and synthesized more than 40 3-indolinone derivatives. We mainly investigated the role of substituents at the 2 position of the core as well as the introduction of substituents or a nitrogen atom in the fused phenyl ring of the core for inhibition of influenza virus neuraminidase activity and replication in vitro and in vivo. After evaluating the compounds for their ability to inhibit the viral neuraminidase, six potent inhibitors 3c, 3e, 7c, 12o, 12v, 18d were progressed to evaluate for cytotoxicity and inhibition of influenza virus A/PR/8/34 replication in in MDCK cells. Two hit compounds 3e and 12o were tested in an animal model of influenza virus infection. Molecular mechanism of the 3-indolinone derivatives interactions with the neuraminidase was revealed in molecular dynamic simulations. Proposed inhibitors bind to the 430-cavity that is different from the conventional binding site of commercial compounds. The most promising 3-indolinone inhibitors demonstrate stronger interactions with the neuraminidase in molecular models that supports proposed binding site.

Pharmacophore-Assisted Covalent Docking Identifies a Potential Covalent Inhibitor for Drug-Resistant Genotype 3 Variants of Hepatitis C Viral NS3/4A Serine Protease

The emergence of drug-resistance-inducing mutations in Hepatitis C virus (HCV) coupled with genotypic heterogeneity has made targeting NS3/4A serine protease difficult. In this work, we investigated the mutagenic variations in the binding pocket of Genotype 3 (G3) HCV NS3/4A and evaluated ligands for efficacious inhibition. We report mutations at 14 positions within the ligand-binding residues of HCV NS3/4A, including H57R and S139P within the catalytic triad. We then modelled each mutational variant for pharmacophore-based virtual screening (PBVS) followed by covalent docking towards identifying a potential covalent inhibitor, i.e., cpd-217. The binding stability of cpd-217 was then supported by molecular dynamic simulation followed by MM/GBSA binding free energy calculation. The free energy decomposition analysis indicated that the resistant mutants alter the HCV NS3/4A-ligand interaction, resulting in unbalanced energy distribution within the binding site, leading to drug resistance. Cpd-217 was identified as interacting with all NS3/4A G3 variants with significant covalent docking scores. In conclusion, cpd-217 emerges as a potential inhibitor of HCV NS3/4A G3 variants that warrants further in vitro and in vivo studies. This study provides a theoretical foundation for drug design and development targeting HCV G3 NS3/4A.

Extended-ensemble docking to probe dynamic variation of ligand binding sites during large-scale structural changes of proteins

Proteins can sample a broad landscape as they undergo conformational transition between different functional states. At the same time, as key players in almost all cellular processes, proteins are important drug targets. Considering the different conformational states of a protein is therefore central for a successful drug-design strategy. Here we introduce a novel docking protocol, termed extended-ensemble docking, pertaining to proteins that undergo large-scale (global) conformational changes during their function. In its application to multidrug ABC-transporter P-glycoprotein (Pgp), extensive non-equilibrium molecular dynamics simulations employing system-specific collective variables are first used to describe the transition cycle of the transporter. An extended set of conformations (extended ensemble) representing the full transition cycle between the inward- and the outward-facing states is then used to seed high-throughput docking calculations of known substrates, non-substrates, and modulators of the transporter. Large differences are predicted in the binding affinities to different conformations, with compounds showing stronger binding affinities to intermediate conformations compared to the starting crystal structure. Hierarchical clustering of the binding modes shows all ligands preferably bind to the large central cavity of the protein, formed at the apex of the transmembrane domain (TMD), whereas only small binding populations are observed in the previously described R and H sites present within the individual TMD leaflets. Based on the results, the central cavity is further divided into two major subsites, first preferably binding smaller substrates and high-affinity inhibitors, whereas the second one shows preference for larger substrates and low-affinity modulators. These central subsites along with the low-affinity interaction sites present within the individual TMD leaflets may respectively correspond to the proposed high- and low-affinity binding sites in Pgp. We propose further an optimization strategy for developing more potent inhibitors of Pgp, based on increasing its specificity to the extended ensemble of the protein, instead of using a single protein structure, as well as its selectivity for the high-affinity binding site. In contrast to earlier in silico studies using single static structures of Pgp, our results show better agreement with experimental studies, pointing to the importance of incorporating the global conformational flexibility of proteins in future drug-discovery endeavors.

Effect of serial in vivo passages on the adaptation of H1N1 avian influenza virus to pigs

Introduction

The lack of proofreading activity of the viral polymerase and the segmented nature of the influenza A virus (IAV) genome are responsible for the genetic diversity of IAVs and for their ability to adapt to a new host. We tried to adapt avian IAV (avIAV) to the pig by serial passages in vivo and assessed the occurrence of point mutations and their influence on viral fitness in the pig’s body.

Material and Methods

A total of 25 in vivo avIAV passages of the A/duck/Bavaria/77 strain were performed by inoculation of 50 piglets, and after predetermined numbers of passages 20 uninoculated piglets were exposed to the virus through contact with inoculated animals. Clinical signs of swine influenza were assessed daily. Nasal swabs and lung tissue were used to detect IAV RNA by real-time RT-PCR and isolates from selected passages were sequenced.

Results

Apart from a rise in rectal temperature and a sporadic cough, no typical clinical signs were observed in infected pigs. The original strain required 20 passages to improve its replication ability noticeably. A total of 29 amino-acid substitutions were identified. Eighteen of them were detected in the first sequenced isolate, of which 16 were also in all other analysed strains. Additional mutations were detected with more passages. One substitution, threonine (T) 135 to serine (S) in neuraminidase (NA), was only detected in an IAV isolate from a contact-exposed piglet.

Conclusion

Passaging 25 times allowed us to obtain a partially swine-adapted IAV. The improvement in isolate replication ability was most likely related to S654 to glycine (G) substitution in the basic protein (PB) 1 as well as to aspartic acid (D) 701 to asparagine (N) and arginine (R) 477 to G in PB2, glutamic acid (E) 204 to D and G239E in haemagglutinin and T135S in NA.

Benchmarking ensemble docking methods in D3R Grand Challenge 4

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Bifunctional Inhibitors of Influenza Virus Neuraminidase: Molecular Design of a Sulfonamide Linker

The growing resistance of the influenza virus to widely used competitive neuraminidase inhibitors occupying the active site of the enzyme requires the development of bifunctional compounds that can simultaneously interact with other regulatory sites on the protein surface. When developing such an inhibitor and combining structural fragments that could be located in the sialic acid cavity of the active site and the adjacent 430-cavity, it is necessary to select a suitable linker not only for connecting the fragments, but also to ensure effective interactions with the unique arginine triad Arg118-Arg292-Arg371 of neuraminidase. Using molecular modeling, we have demonstrated the usefulness of the sulfonamide group in the linker design and the potential advantage of this functional group over other isosteric analogues.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

Accommodating receptor flexibility and free energy calculation to reduce false positive binders in the discovery of natural products blockers of SARS-COV-2 spike RBD-ACE2 interface

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), which causes coronavirus disease-19 (COVID-19) has caused more than 2 million deaths around the globe. The high transmissibility rate of the disease is related to the strong interaction between the virus spike receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) as documented in several reports. In this study, using state-of-the-art computational methods, natural products were screened and their molecular mechanism to disrupt spike RBD-ACE2 recognition was evaluated. There is the sensitivity of results to receptor ensemble docking calculations. Binding free energy and MD simulation are important tools to evaluate the thermodynamics of binding stability and the capacity of top hits to disrupt RBD-ACE2 recognition. The free energy profiles provide a slight decrease in binding affinity of the virus-receptor interaction. Three flavonoids parvisoflavone B (3), alpinumisoflavone (5) and norisojamicin (2) were effective in blocking the viral entry by binding strongly at the spike RBD-ACE2 interface with the inhibition constant of 0.56, 0.78 and 0.93 μM, respectively. The same compounds demonstrated similar effect on free ACE2 protein. Compound (2), also demonstrated ability to bind strongly on free spike RBD. Well-tempered metadynamics established that parvisoflavone B (3) works by binding to three sites namely interface α, β and loop thereby inhibiting viral cell entry. Owing to their desirable pharmacokinetic properties, the presented top hit natural products are suggested for further SARS-COV-2 molecular targets and subsequent in vitro and in vivo evaluations.

Luteolin: a blocker of SARS-CoV-2 cell entry based on relaxed complex scheme, molecular dynamics simulation, and metadynamics

Natural products have served human life as medications for centuries. During the outbreak of COVID-19, a number of naturally derived compounds and extracts have been tested or used as potential remedies against COVID-19. Tetradenia riparia extract is one of the plant extracts that have been deployed and claimed to manage and control COVID-19 by some communities in Tanzania and other African countries. The active compounds isolated from T. riparia are known to possess various biological properties including antimalarial and antiviral. However, the underlying mechanism of the active compounds against SARS-CoV-2 remains unknown. Results in the present work have been interpreted from the view point of computational methods including molecular dynamics, free energy methods, and metadynamics to establish the related mechanism of action. Among the constituents of T. riparia studied, luteolin inhibited viral cell entry and was thermodynamically stable. The title compound exhibit residence time and unbinding kinetics of 68.86 ms and 0.014 /ms, respectively. The findings suggest that luteolin could be potent blocker of SARS-CoV-2 cell entry. The study shades lights towards identification of bioactive constituents from T. riparia against COVID-19, and thus bioassay can be carried out to further validate such observations.

Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation

Aim: SARS-CoV-2, an emerging betacoronavirus, is the causative agent of COVID-19. Currently, there are few specific and selective antiviral drugs for the treatment and vaccines to prevent contagion. However, their long-term effects can be revealed after several years, and new drugs for COVID-19 should continue to be investigated. Materials & methods: In the first step of our study we identified, through a gene expression analysis, several drugs that could act on the biological pathways altered in COVID-19. In the second step, we performed a docking simulation to test the properties of the identified drugs to target SARS-CoV-2. Results: The drugs that showed a higher binding affinity are bardoxolone (-8.78 kcal/mol), irinotecan (-8.40 kcal/mol) and pyrotinib (-8.40 kcal/mol). Conclusion: We suggested some drugs that could be efficient in treating COVID-19.

CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Motivation

Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies, are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme.

Results

We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software.

Availability and implementation

www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe.

Supplementary information

Supplementary data are available at Bioinformatics online.

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

Plant isoquinoline alkaloids as potential neurodrugs: A comparative study of the effects of benzo[c]phenanthridine and berberine-based compounds on β-amyloid aggregation

Herein we present a comparative study of the effects of isoquinoline alkaloids belonging to benzo[c]phenanthridine and berberine families on β-amyloid aggregation. Results obtained using a Thioflavine T (ThT) fluorescence assay and circular dichroism (CD) spectroscopy suggested that the benzo[c]phenanthridine nucleus, present in both sanguinarine and chelerythrine molecules, was directly involved in an inhibitory effect of Aβ_1-42 aggregation. Conversely, coralyne, that contains the isomeric berberine nucleus, significantly increased propensity for Aβ_1-42 to aggregate. Surface Plasmon Resonance (SPR) experiments provided quantitative estimation of these interactions: coralyne bound to Aβ_1-42 with an affinity (K_D = 11.6 μM) higher than benzo[c]phenanthridines. Molecular docking studies confirmed that all three compounds are able to recognize Aβ_1-42 in different aggregation forms suggesting their effective capacity to modulate the Aβ_1-42 self-recognition mechanism. Molecular dynamics simulations indicated that coralyne increased the β-content of Aβ_1-42, in early stages of aggregation, consistent with fluorescence-based promotion of the Aβ_1-42 self-recognition mechanism by this alkaloid. At the same time, sanguinarine induced Aβ_1-42 helical conformation corroborating its ability to delay aggregation as experimentally proved in vitro. The investigated compounds were shown to interfere with aggregation of Aβ_1-42 demonstrating their potential as starting leads for the development of therapeutic strategies in neurodegenerative diseases.

Discovery of HIV entry inhibitors via a hybrid CXCR4 and CCR5 receptor pharmacophore-based virtual screening approach

Chemokine receptors are key regulators of cell migration in terms of immunity and inflammation. Among these, CCR5 and CXCR4 play pivotal roles in cancer metastasis and HIV-1 transmission and infection. They act as essential co-receptors for HIV and furnish a route to the cell entry. In particular, inhibition of either CCR5 or CXCR4 leads very often the virus to shift to a more virulent dual-tropic strain. Therefore, dual receptor inhibition might improve the therapeutic strategies against HIV. In this study, we aimed to discover selective CCR5, CXCR4, and dual CCR5/CXCR4 antagonists using both receptor- and ligand-based computational methods. We employed this approach to fully incorporate the interaction attributes of the binding pocket together with molecular dynamics (MD) simulations and binding free energy calculations. The best hits were evaluated for their anti-HIV-1 activity against CXCR4- and CCR5-specific NL4.3 and BaL strains. Moreover, the Ca²⁺ mobilization assay was used to evaluate their antagonistic activity. From the 27 tested compounds, three were identified as inhibitors: compounds 27 (CCR5), 6 (CXCR4) and 3 (dual) with IC₅₀ values ranging from 10.64 to 64.56 μM. The binding mode analysis suggests that the active compounds form a salt bridge with the glutamates and π-stacking interactions with the aromatic side chains binding site residues of the respective co-receptor. The presented hierarchical virtual screening approach provides essential aspects in identifying potential antagonists in terms of selectivity against a specific co-receptor. The compounds having multiple heterocyclic nitrogen atoms proved to be relatively more specific towards CXCR4 inhibition as compared to CCR5. The identified compounds serve as a starting point for further development of HIV entry inhibitors through synthesis and quantitative structure-activity relationship studies.

Deamidated Human Triosephosphate Isomerase is a Promising Druggable Target

Therapeutic strategies for the treatment of any severe disease are based on the discovery and validation of druggable targets. The human genome encodes only 600-1500 targets for small-molecule drugs, but posttranslational modifications lead to a considerably larger druggable proteome. The spontaneous conversion of asparagine (Asn) residues to aspartic acid or isoaspartic acid is a frequent modification in proteins as part of the process called deamidation. Triosephosphate isomerase (TIM) is a glycolytic enzyme whose deamidation has been thoroughly studied, but the prospects of exploiting this phenomenon for drug design remain poorly understood. The purpose of this study is to demonstrate the properties of deamidated human TIM (HsTIM) as a selective molecular target. Using in silico prediction, in vitro analyses, and a bacterial model lacking the tim gene, this study analyzed the structural and functional differences between deamidated and nondeamidated HsTIM, which account for the efficacy of this protein as a druggable target. The highly increased permeability and loss of noncovalent interactions of deamidated TIM were found to play a central role in the process of selective enzyme inactivation and methylglyoxal production. This study elucidates the properties of deamidated HsTIM regarding its selective inhibition by thiol-reactive drugs and how these drugs can contribute to the development of cell-specific therapeutic strategies for a variety of diseases, such as COVID-19 and cancer.

A computational study on the role of water and conformational fluctuations in Hsp90 in response to inhibitors

Molecular chaperone Heat Shock Protein 90 (Hsp90) represents an interesting chemotherapeutic target for cancer treatments as it plays a role in cancer proliferation. Thus, continued effort to identify novel inhibitors of this target is an important task. Drug design using computational approach has gained significant attention in recent years. This work aims to propose docking protocols to re-purpose FDA-approved drugs targeting Hsp90. Sensitivity of results to different docking protocols such apo, holo and receptor ensembles (relaxed complex) structures, the role of water and conformational changes of Hsp90, are described. We show that the protein conformation and water have effects on drug binding. Holo relaxed complex receptors ensembles improves the binding energy of ligands to the protein. We also compare and contrast structural stability of three drugs namely: ezetimibe, pitavastatin and vilazodon in the Hsp90 protein. The results obtained serves as a possible basis towards developing Hsp90 inhibitors.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

All-atom virus simulations

The constant threat of viral disease can be combated by the development of novel vaccines and therapeutics designed to disrupt key features of virus structure or infection cycle processes. Such development relies on high-resolution characterization of viruses and their dynamical behaviors, which are often challenging to obtain solely by experiment. In response, all-atom molecular dynamics simulations are widely leveraged to study the structural components of viruses, leading to some of the largest simulation endeavors undertaken to date. The present work reviews exemplary all-atom simulation work on viruses, as well as progress toward simulating entire virions.

Magnetic beads-based neuraminidase enzyme microreactor as a drug discovery tool for screening inhibitors from compound libraries and fishing ligands from natural products

Neuraminidase (NA) is a glycoside hydrolase that has been proposed as a potential therapeutic target for influenza. Thus, the identification of compounds that modulate NA activity could be of great therapeutic importance. The aim of this study is to develop a drug discovery tool for the identification of novel modulators of NA from both compound libraries and natural plant extracts. NA was immobilized onto the surface of magnetic beads and the inherent catalytic activity of NA-functionalized magnetic beads was characterized. Based on the enzymatic activity (hydrolysis ratio), the inhibitory activities of 12 compounds from plant secondary metabolites were screened, and the desired anti-NA activities of flavonoids were certified. Ligand fishing with the immobilized enzyme was optimized using an artificial test mixture consisting of oseltamivir, lycorine and matrine prior to carrying out the proof-of-concept experiment with the crude extract of Flos Lonicerae. The combination of ligand fishing and HPLC-MS/MS identified luteolin-7-O-β-D-glucoside, luteolin, 3,5-di-O-caffeoylquinic acid and 3,4-di-O-caffeoylquinic acid as neuraminidase inhibitory ligands in Flos Lonicerae. This is the first report on the use of neuraminidase functionalized magnetic beads for the identification of active ligands from a botanical matrix, and it sets the basis for the de novo identification of NA modulators from complex biological mixtures.

Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking

Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

Design and Molecular Docking Studies of Quinazoline Derivatives as Antiproliferation

Background: Nowadays, a lot of new active substances as anticancer agents have been developed. One of the protein targets of anticancer is selective cyclooxygenase-2 (COX-2). Selective COX-2 is the regulator of cell proliferation. Objective: In this research, quinazoline derivatives were used to design the anticancer agent through a selective COX-2 inhibition. The potential activity of quinazoline derivatives could be increased by substitution in position 2 and 3 of quinazolinone. Molecular docking of selective COX-2 inhibition was required to predict their antiproliferation activity. Methods: The molecular docking of quinazoline derivatives was carried out using Molegro Virtual Docker (MVD) Ver.5.5. Twenty-one of quinazoline derivatives were docked into selective COX-2 with PDB code 3LN1. The interaction was evaluated based on the re-ranked score comparison between quinazoline derivatives with co-crystallized ligand CEL_682. Celecoxib was used as the reference to this research. Results: The result indicated that 18 of 21 quinazoline derivatives showed the approximately re-ranked score -131.508 to -108.418 kcal/mol. Eight of these 18 new quinazoline derivatives have re-ranked score better than Celecoxib. Conclusions: In conclusion, 8 of the new quinazoline derivatives are feasible to be synthesize and performed their in vitro evaluation.

Exploration of the 150 cavity and the role of serendipity in the discovery of inhibitors of influenza virus A neuraminidase

Influenza pandemics are an ongoing threat for the human population, as the avian influenza viruses H5N1 and H7N9 continue to circulate in the bird population and the chance of avian to human transmission increases. Neuraminidase, a glycoprotein located on the surface of the influenza virus, plays a crucial role in the viral replication process and, hence, has proven to be a useful target enzyme for the treatment of influenza infections. The discovery that certain subtypes of influenza neuraminidase have an additional cavity, the 150 cavity, near the substrate binding site has triggered considerable interest in the design of influenza inhibitors that exploit this feature. Currently available antiviral drugs, neuraminidase inhibitors oseltamivir and zanamivir, were designed using crystal structures predating this discovery by some years. This mini review is aimed at summarizing our group’s efforts, together with related work from other groups, on neuraminidase inhibitors that are designed to exploit both the catalytic site and the 150 cavity. The design of a parent scaffold that yields a potent inhibitor that is active in cell culture assays and retains activity against several neuraminidases from mutant strains is also described. Finally, the role of serendipity in the discovery of a new class of potent neuraminidase inhibitors with a novel spirolactam scaffold is also highlighted.

Structural ensemble-based docking simulation and biophysical studies discovered new inhibitors of Hsp90 N-terminal domain

Heat shock protein 90 (Hsp90) is one of the most abundant cellular proteins and plays a substantial role in the folding of client proteins. The inhibition of Hsp90 has been regarded as an attractive therapeutic strategy for treating cancer because many oncogenic kinases are Hsp90 client proteins. In this study, we report new inhibitors that directly bind to N-terminal ATP-binding pocket of Hsp90. Optimized structure-based virtual screening predicted candidate molecules, which was followed by confirmation using biophysical and cell-based assays. Among the reported crystal structures, we chose the two structures that show the most favourable early enrichments of true-positives in the receiver operating characteristic curve. Four molecules showed significant changes in the signals of 2D [¹H, ¹⁵N] correlation NMR spectroscopy. Differential scanning calorimetry analysis supported the results indicating direct binding. Quantified dissociation constant values of the molecules, determined by a series of 2D NMR experiments, lie in the range of 0.1–33 μM. Growth inhibition assay with breast and lung cancer cells confirmed the cellular activities of the molecules. Cheminformatics revealed that the molecules share limited chemical similarities with known inhibitors. Molecular dynamics simulations detailed the putative binding modes of the inhibitors.

Structural analysis of protein tyrosine phosphatase 1B reveals potentially druggable allosteric binding sites

Catalytic proteins such as human protein tyrosine phosphatase 1B (PTP1B), with conserved and highly polar active sites, warrant the discovery of druggable nonactive sites, such as allosteric sites, and potentially, therapeutic small molecules that can bind to these sites. Catalyzing the dephosphorylation of numerous substrates, PTP1B is physiologically important in intracellular signal transduction pathways in diverse cell types and tissues. Aberrant PTP1B is associated with obesity, diabetes, cancers, and neurodegenerative disorders. Utilizing clustering methods (based on root mean square deviation, principal component analysis, nonnegative matrix factorization, and independent component analysis), we have examined multiple PTP1B structures. Using the resulting representative structures in different conformational states, we determined consensus clustroids and used them to identify both known and novel binding sites, some of which are potentially allosteric. We report several lead compounds that could potentially bind to the novel PTP1B binding sites and can be further optimized. Considering the possibility for drug repurposing, we discovered homologous binding sites in other proteins, with ligands that could potentially bind to the novel PTP1B binding sites.

Identification of neuraminidase inhibitors against dual H274Y/I222R mutant strains

Influenza is an annual seasonal epidemic that has continually drawn public attentions, due to the potential death toll and drug resistance. Neuraminidase, which is essential for the spread of influenza virus, has been regarded as a valid target for the treatment of influenza infection. Although neuraminidase drugs have been developed, they are susceptible to drug-resistant mutations in the sialic-binding site. In this study, we established computational models (site-moiety maps) of H1N1 and H5N1 to determine properties of the 150-cavity, which is adjacent to the drug-binding site. The models reveal that hydrogen-bonding interactions with residues R118, D151, and R156 and van der Waals interactions with residues Q136, D151, and T439 are important for identifying 150-cavitiy inhibitors. Based on the models, we discovered three new inhibitors with IC₅₀ values <10 μM that occupies both the 150-cavity and sialic sites. The experimental results identified inhibitors with similar activities against both wild-type and dual H274Y/I222R mutant neuraminidases and showed little cytotoxic effects. Furthermore, we identified three new inhibitors situated at the sialic-binding site with inhibitory effects for normal neuraminidase, but lowered effects for mutant strains. The results suggest that the new inhibitors can be used as a starting point to combat drug-resistant strains.

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

Ensemble-based virtual screening: identification of a potential allosteric inhibitor of Bcr-Abl

Ensemble-based virtual screening using different conformations of a target protein is gaining popularity, as it can leverage information from target flexibility for effective lead identification. In this paper, molecular dynamics simulation followed by RMSD-based clustering was employed to generate and choose distinct conformations of Bcr-Abl. Three representative structures from the most-populated clusters along with the crystal structure conformation (PDBID: 3K5V) were used to perform docking-based virtual screening of 14,400 compounds (in the Maybridge database) in order to identify potential allosteric site binders. Seven compounds found as hits in at least three of the four virtual screenings had higher Glide docking scores than the co-crystallized allosteric inhibitor GNF-2. Detailed computational analyses of the seven hits identified SEW02675 (ΔG _bind = -164.92 kJ/mol with the wild-type (wt) Bcr-Abl and -167.37 kJ/mol with the T334I Bcr-Abl mutant) as a better allosteric site binder with both the wt and the mutant Bcr-Abl protein than the reference allosteric inhibitor GNF-2 (ΔG _bind = -103.12 with wt and -142.96 kJ/mol with T334I). Moreover, the presence of SEW02675 in the allosteric site enhanced the binding of imatinib (ΔG _bind = -367.58 with wt and -294.56 kJ/mol with T334I) to the ATP sites of the wt and the mutant Bcr-Abl. However, when GNF-2 was present in the allosteric site, the binding of imatinib (ΔG _bind = -351.76 with wt and -273.94 kJ/mol with T334I) to the ATP site was weaker. The in silico findings suggest that SEW02675 could be used in combination with imatinib to treat chronic myeloid leukemia, and that it could help to overcome resistance due to T334I Bcr-Abl mutation. Graphical abstract Virtual screening strategy to identify allosteric inhbitors of Bcr-Abl for the treatment of Chronic myeloid leukemia.

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.

Discovery and Characterization of Diazenylaryl Sulfonic Acids as Inhibitors of Viral and Bacterial Neuraminidases

Viral neuraminidases are an established drug target to combat influenza. Severe complications observed in influenza patients are primarily caused by secondary infections with e.g., Streptococcus pneumoniae. These bacteria engage in a lethal synergism with influenza A viruses (IAVs) and also express neuraminidases. Therefore, inhibitors with dual activity on viral and bacterial neuraminidases are expected to be advantageous for the treatment of influenza infections. Here we report on the discovery and characterization of diazenylaryl sulfonic acids as dual inhibitors of viral and Streptococcus pneumoniae neuraminidase. The initial hit came from a virtual screening campaign for inhibitors of viral neuraminidases. For the most active compound, 7-[2-[4-[2-[4-[2-(2-hydroxy-3,6-disulfo-1-naphthalenyl)diazenyl]-2-methylphenyl]diazenyl]-2-methylphenyl]diazenyl]-1,3-naphthalenedisulfonic acid (NSC65847; 1), the Ki-values measured in a fluorescence-based assay were lower than 1.5 μM for both viral and pneumococcal neuraminidases. The compound also inhibited N1 virus variants containing neuraminidase inhibitor resistance-conferring substitutions. Via enzyme kinetics and nonlinear regression modeling, 1 was suggested to impair the viral neuraminidases and pneumococcal neuraminidase with a mixed-type inhibition mode. Given its antiviral and antipneumococcal activity, 1 was identified as a starting point for the development of novel, dual-acting anti-infectives.

Zwitterionic structures: from physicochemical properties toward computer-aided drug designs

Zwitterions, used widely in chemical, biological and medicinal fields, show distinct physicochemical properties relative to ordinary ampholytes, which largely decide their bioavailability and biological activities. In the present manuscript, these properties are discussed in order to facilitate our understanding of zwitterionic structures, followed by various examples of zwitterionic drugs and the critical role these properties play. We specifically focus our discussions on neuraminidase inhibitors (NAIs), which are used in the treatment and prevention of influenza, covering their computer-assisted design, transformation to zwitterionic isomers and interaction mechanisms of NAIs with proteins. The discovery and development of NAIs provide useful insights that may assist in the exploration of new zwitterionic drugs.

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.

Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking

MDM2 is a negative regulator of p53 activity and an important target for cancer therapeutics. The N-terminal lid region of MDM2 modulates interactions with p53 via competition for its binding cleft, exchanging slowly between docked and undocked conformations in the absence of p53. To better understand these dynamics, we constructed Markov State Models (MSMs) from large collections of unbiased simulation trajectories of apo-MDM2, and find strong evidence for diffuse, yet two-state folding and binding of the N-terminal region to the p53 receptor site. The MSM also identifies holo-like receptor conformations highly suitable for computational docking, despite initiating trajectories from closed-cleft receptor structures unsuitable for docking. Fixed-anchor docking studies using a test set of high-affinity small molecules and peptides show simulated receptor ensembles achieve docking successes comparable to cross-docking studies using crystal structures of receptors bound by alternative ligands. For p53, the best-scoring receptor structures have the N-terminal region lid region bound in a helical conformation mimicking the bound structure of p53, suggesting lid region association induces receptor conformations suitable for binding. These results suggest that MD + MSM approaches can sample binding-competent receptor conformations suitable for computational peptidomimetic design, and that inclusion of disordered regions may be essential to capturing the correct receptor dynamics.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

The Art of Compiling Protein Binding Site Ensembles

Structure-based drug design starts with the collection, preparation, and initial analysis of protein structures. With more than 115,000 structures publically available in the Protein Data Bank (PDB), fully automated processes reliably performing these important preprocessing steps are needed. Several tools are available for these tasks, however, most of them do not address the special needs of scientists interested in protein-ligand interactions. In this paper, we summarize our research activities towards an automated processing pipeline from raw PDB data towards ready-to-use protein binding site ensembles. Starting from a single protein structure, the pipeline covers the following phases: Extracting structurally related binding sites from the PDB, aligning disconnected binding site sequences, resolving tautomeric forms and protonation, orienting hydrogens and flippable side-chains, structurally aligning the multitude of binding sites, and performing a reasonable reduction of ensemble structures. The pipeline, named SIENA, creates protein-structural ensembles for the analysis of protein flexibility, molecular design efforts like docking or de novo design within seconds. For the first time, we are able to process the whole PDB in order to create a large collection of protein binding site ensembles. SIENA is available as part of the ZBH ProteinsPlus webserver under http://proteinsplus.zbh.uni-hamburg.de.

Peramivir Phosphonate Derivatives as Influenza Neuraminidase Inhibitors

Peramivir is a potent neuraminidase (NA) inhibitor for treatment of influenza infection by intravenous administration. By replacing the carboxylate group in peramivir with a phosphonate group, phosphono-peramivir (6a), the dehydration and deoxy derivatives (7a and 8a) as well as their corresponding monoalkyl esters are prepared from a pivotal intermediate epoxide 12. Among these phosphonate compounds, the dehydration derivative 7a that has a relatively rigid cyclopentene core structure exhibits the strongest inhibitory activity (IC50 = 0.3-4.1 nM) against several NAs of wild-type human and avian influenza viruses (H1N1, H3N2, H5N1, and H7N9), although the phosphonate congener 6a is unexpectedly less active than peramivir. The inferior binding affinity of 6a is attributable to the deviated orientations of its phosphonic acid and 3-pentyl groups in the NA active site as inferred from the NMR, X-ray diffraction, and molecular modeling analyses. Compound 7a is active to the oseltamivir-resistant H275Y strains of H1N1 and H5N1 viruses (IC50 = 73-86 nM). The phosphonate monoalkyl esters (6b, 6c, 7b, 7c, 8b, and 8c) are better anti-influenza agents (EC50 = 19-89 nM) than their corresponding phosphonic acids (EC50 = 50-343 nM) in protection of cells from the viral infection. The phosphonate monoalkyl esters are stable in buffer solutions (pH 2.0-7.4) and rabbit serum; furthermore, the alkyl group is possibly tuned to attain the desired pharmacokinetic properties.

Sequence diversity of NanA manifests in distinct enzyme kinetics and inhibitor susceptibility

Streptococcus pneumoniae is the leading pathogen causing bacterial pneumonia and meningitis. Its surface-associated virulence factor neuraminidase A (NanA) promotes the bacterial colonization by removing the terminal sialyl residues from glycoconjugates on eukaryotic cell surface. The predominant role of NanA in the pathogenesis of pneumococci renders it an attractive target for therapeutic intervention. Despite the highly conserved activity of NanA, our alignment of the 11 NanAs revealed the evolutionary diversity of this enzyme. The amino acid substitutions we identified, particularly those in the lectin domain and in the insertion domain next to the catalytic centre triggered our special interest. We synthesised the representative NanAs and the mutagenized derivatives from E. coli for enzyme kinetics study and neuraminidase inhibitor susceptibility test. Via molecular docking we got a deeper insight into the differences between the two major variants of NanA and their influence on the ligand-target interactions. In addition, our molecular dynamics simulations revealed a prominent intrinsic flexibility of the linker between the active site and the insertion domain, which influences the inhibitor binding. Our findings for the first time associated the primary sequence diversity of NanA with the biochemical properties of the enzyme and with the inhibitory efficiency of neuraminidase inhibitors.

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

SIENA: Efficient Compilation of Selective Protein Binding Site Ensembles

Structural flexibility of proteins has an important influence on molecular recognition and enzymatic function. In modeling, structure ensembles are therefore often applied as a valuable source of alternative protein conformations. However, their usage is often complicated by structural artifacts and inconsistent data annotation. Here, we present SIENA, a new computational approach for the automated assembly and preprocessing of protein binding site ensembles. Starting with an arbitrarily defined binding site in a single protein structure, SIENA searches for alternative conformations of the same or sequentially closely related binding sites. The method is based on an indexed database for identifying perfect k-mer matches and a recently published algorithm for the alignment of protein binding site conformations. Furthermore, SIENA provides a new algorithm for the interaction-based selection of binding site conformations which aims at covering all known ligand-binding geometries. Various experiments highlight that SIENA is able to generate comprehensive and well selected binding site ensembles improving the compatibility to both known and unconsidered ligand molecules. Starting with the whole PDB as data source, the computation time of the whole ensemble generation takes only a few seconds. SIENA is available via a Web service at www.zbh.uni-hamburg.de/siena .

Discovery of Novel ROCK1 Inhibitors via Integrated Virtual Screening Strategy and Bioassays

Rho-associated kinases (ROCKs) have been regarded as promising drug targets for the treatment of cardiovascular diseases, nervous system diseases and cancers. In this study, a novel integrated virtual screening protocol by combining molecular docking and pharmacophore mapping based on multiple ROCK1 crystal structures was utilized to screen the ChemBridge database for discovering potential inhibitors of ROCK1. Among the 38 tested compounds, seven of them exhibited significant inhibitory activities of ROCK1 (IC₅₀ < 10 μM) and the most potent one (compound TS-f22) with the novel scaffold of 4-Phenyl-1H-pyrrolo [2,3-b] pyridine had an IC₅₀ of 480 nM. Then, the structure-activity relationships of 41 analogues of TS-f22 were examined. Two potent inhibitors were proven effective in inhibiting the phosphorylation of the downstream target in the ROCK signaling pathway in vitro and protecting atorvastatin-induced cerebral hemorrhage in vivo. The high hit rate (28.95%) suggested that the integrated virtual screening strategy was quite reliable and could be used as a powerful tool for identifying promising active compounds for targets of interest.

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]