Optimization of Benzoisothiazole dioxide inhibitory activity of the NS5B polymerase of HCV genotype 4 using ligand-steered homological modeling, reaction-driven scaffold-hopping and Enovo workflow

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery. A potent class of inhibitors having benzisothiazole dioxide scaffold has been identified on this target, however they were mainly active on genotype 1 while exhibiting much lowered activity on other genotypes due to the high degree of mutation of its binding site. Based on this fact, we employed a novel strategy to optimize this class on genotype 4. This strategy depends on using a refined ligand-steered homological model of this genotype to study the mutation binding energies of the binding site amino acid residues, the essential features for interaction and provide a structure-based pharmacophore model that can aid optimization. This model was applied on a focused library which was generated using a reaction-driven scaffold-hopping strategy. The hits retrieved were subjected to Enovo pipeline pilot optimization workflow that employs R-group enumeration, core-constrained protein docking using modified CDOCKER and finally ranking of poses using an accurate molecular mechanics generalized Born with surface area method.     

Identification of potential inhibitors for HCV NS5b of genotype 4a by combining dynamic simulation,protein–ligand interaction fingerprint, 3D pharmacophore,docking and 3D QSAR

Abstract

HCV NS5B polymerase has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting Hepatitis C Virus genotype 1 (HCV GT1). Hepatitis C virus genotype 4a (HCV GT4a) dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS5B polymerase of GT4a using homology modeling, protein–ligand interaction fingerprint (PLIF), docking, pharmacophore, and 3D CoMFA quantitative structure activity relationship (QSAR). Firstly, a high-quality 3D model of HCV NS5B polymerase of GT4a was constructed using crystal structure of HCV NS5B polymerase of GT1 (PDB ID: 3hkw) as a template. Then, both the model and the template were simulated to compare conformational stability. PLIF was generated using five crystal structures of HCV NS5B (PDB ID: 4mia, 4mib, 4mk9, 4mka, and 4mkb), which revealed the most important residues and their interactions with the co-crystalized ligands. After that, a 3D pharmacophore model was developed from the generated PLIF data and then used as a screening filter for 17000328 drug-like zinc database compounds. 900 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. Finally, a 3D CoMFA QSAR was developed using 42 compounds as a training and 19 compounds as a test set. The 3D CoMFA QSAR was used to design and screen some potential inhibitors, these compounds were further evaluated by the docking stage. The highest ranked five hits from docking result (compounds (p1–p4) and compound q1) were selected for further analysis.

Communicated by Ramaswamy H. Sarma     

Focused library design in GPCR projects on the example of 5-HT(2c) agonists: comparison of structure-based virtual screening with ligand-based search methods

The aim of this study was to investigate the usefulness of structure-based virtual screening (VS) for focused library design in G protein-coupled receptors (GPCR) projects on the example of 5-HT(2c) agonists. We compared the performance of structure-based VS against two different homology models using FRED for docking and ScreenScore, FlexX, and PMF for rescoring with the results of 12 ligand-based similarity searches using four different query compounds and three different similarity metrics (Daylight, FTree, Phacir). The result of the similarity search showed much variation, from an enrichment factor up to 3.2 to worse than random, whereas the structure-based VS gave a more stable result with a constant enrichment factor around 2. Additionally, actives retrieved by the structure-based approach were more diverse than the actives among the top scorers of the similarity searches. Based on these results, we suggest basing a focused library design for a GPCR project on a combination of a ligand-based similarity search and structure-based docking.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Toward the virtual screening of Cdc25A phosphatase inhibitors with the homology modeled protein structure

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy due to the correlation of their overexpression with a wide variety of cancers. As a method for the discovery of novel inhibitors of Cdc25 phosphatases, we have evaluated the computer-aided drug design protocol involving the homology modeling of Cdc25A and virtual screening with the two docking tools: FlexX and the modified AutoDock program implementing the effects of ligand solvation in the scoring function. The homology modeling with the X-ray crystal structure of Cdc25B as a template provides a high-quality structure of Cdc25A that enables the structure-based inhibitor design. Of the two docking programs under consideration, AutoDock is found to be more accurate than FlexX in terms of scoring putative ligands. A detailed binding mode analysis of the known inhibitors shows that they can be stabilized in the active site of Cdc25A through the simultaneous establishment of the multiple hydrogen bonds and the hydrophobic interactions. The present study demonstrates the usefulness of the modified AutoDock program as a docking tool for virtual screening of new Cdc25 phosphatase inhibitors as well as for binding mode analysis to elucidate the activities of known inhibitors. Figure Structures and available IC50 values (in μM) of the twenty Cdc25 phosphatase inhibitors seeded in docking library     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Regio- and stereo-selective 1β-hydroxylation of lithocholic acid by cytochrome P450 BM3 mutants

Regio- and stereo-selective hydroxylation of bile acids is a valuable reaction but often lacks suitable catalysts. In the research, semi-rational design in protein engineering techniques had been applied on cytochrome P450 monooxygenase CYP102A1 (P450 BM3) from Bacillus megaterium, and a mutation library had been set up for the 1β-hydroxylation of lithocholic acid (LCA) to produce 1β-OH-LCA. After four rounds of mutagenesis, a key residue at W72 was identified to regulate the regio- and stereo-selectivity at C1 of LCA. A quadruple variant (G87A/W72T/A74L/L181M) was identified to reach 99.4% selectivity of 1β-hydroxylation and substrate conversion of 68.1% resulting in a 21.5-fold higher level of 1β-OH-LCA production than the template LG-23. Molecular docking indicated that introducing hydrogen bonds at W72 was responsible for enhancing selectivity and catalytic activity, which gave some insights into the structure-based understanding of Csp³-H activation by the developed P450 BM3 mutants.     

Evaluation of potential flavonoid inhibitors of glyoxalase-I based on virtual screening and in vitro studies

Glyoxalase-I (GLO-I) is a component of the ubiquitous detoxification system involved in the conversion of methylglyoxal (MG) to d-lactate in the glycolytic pathway. MG toxicity arises from its ability to form advanced glycation end products. GLO-I has been reported to be frequently overexpressed in various types of cancer cells. In this study, we performed structure-based virtual screening of focused flavonoids commercial library to identify potential and specific inhibitors of GLO-I. The compounds were ranked based on Glide extra precision docking score and five hits (curcumin, quercetin, morin, naringin and silibinin) were selected on the basis of their interaction with active site amino acid residues of GLO-I. Mixed mode QM/MM calculation was performed on the top-scoring hit to ascertain the role of zinc ion in ligand binding. In addition, the identified hits were subjected to MM/GBSA binding energy prediction, ADME prediction and similarity studies. The hits were tested in vitro for cell viability, and GLO-I inhibition. Naringin (ST072162) was found to be most potent inhibitor of GLO-I among the identified hits with highest glide XP dock score of ?14.906. These findings suggest that naringin could be a new scaffold for designing inhibitors against GLO-I with potential application as anticancer agents.     

Refined homology model of cytochrome bcc complex B subunit for virtual screening of potential anti-tuberculosis agents

Abstract

Cytochrome bcc complex is important for ATP synthesis and cellular activity, as a crucial step in the terminal reduction of oxygen in aerobic electron transport chains. The b subunit of cytochrome bcc complex (QcrB) has been reported as a promising anti-tuberculosis target, with many novel anti-tuberculosis scaffolds reported. However, the 3D structure of mycobacterium tuberculosis (M. tuberculosis) QcrB has not been released, making it hard to understand the interactions between QcrB and its inhibitors as well as to develop novel anti-tuberculosis scaffolds. Herein we built the optimal homology model of M. tuberculosis QcrB using the M. smegmatis QcrB structure as template, which was refined through all-atom molecular dynamics simulation. Then, the binding modes of known inhibitors were predicted through molecular docking method, along with molecular dynamics simulation and binding free energy calculation to verify the accuracy of docking results and stability of the protein-inhibitor complexes. The informative key residues within QcrB site enabled us to perform structure-based virtual library screening to obtain potential M. tuberculosis QcrB inhibitors, which were validated through molecular dynamics simulation and MM-GBSA calculation and analyzed through pharmacokinetic properties prediction. Our research would provide a deeper insight into the interactions between M. tuberculosis QcrB and its inhibitors, which boosts to develop novel therapy against tuberculosis.

Communicated by Ramaswamy H. Sarma     

Sequential Application of Ligand and Structure Based Modeling Approaches to Index Chemicals for Their hH4R Antagonism

The human histamine H₄ receptor (hH₄R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH₄R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH₄R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH₄R bioactivity. An application of the prediction model on external test set composed of more than 160 hH₄R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼4000 chemicals highly indexed as H₄R antagonists'' candidates. Next, a series of 3D models of hH₄R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH₄R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.     

Novel human mPGES-1 inhibitors identified through structure-based virtual screening

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase after exposure to pro-inflammatory stimuli and, therefore, represents a novel target for therapeutic treatment of acute and chronic inflammatory disorders. It is essential to identify mPGES-1 inhibitors with novel scaffolds as new leads or hits for the purpose of drug design and discovery that aim to develop the next-generation anti-inflammatory drugs. Herein we report novel mPGES-1 inhibitors identified through a combination of large-scale structure-based virtual screening, flexible docking, molecular dynamics simulations, binding free energy calculations, and in vitro assays on the actual inhibitory activity of the computationally selected compounds. The computational studies are based on our recently developed three-dimensional (3D) structural model of mPGES-1 in its open state. The combined computational and experimental studies have led to identification of new mPGES-1 inhibitors with new scaffolds. In particular, (Z)-5-benzylidene-2-iminothiazolidin-4-one is a promising novel scaffold for the further rational design and discovery of new mPGES-1 inhibitors. To our best knowledge, this is the first time a 3D structural model of the open state mPGES-1 is used in structure-based virtual screening of a large library of available compounds for the mPGES-1 inhibitor identification. The positive experimental results suggest that our recently modeled trimeric structure of mPGES-1 in its open state is ready for the structure-based drug design and discovery.     

In silico analysis of the effect of mutation on epidermal growth factor receptor in non-small-cell lung carcinoma: from mutational analysis to drug designing

Tyrosine kinase inhibitors (TKI)-resistant mutation in epidermal growth factor receptor’s (EGFR) kinase domain is an important anomaly to look into. Studying the mutations at atomic level using molecular dynamics simulations gave us an insight into the architectural changes happening at the microscopic level. The knowledge was used to design new TKI whose function is devoid of the affect of the mutations in kinase domain. Traditional Chinese medicinal library was used for structure-based drug designing, where virtual screening was followed by ADME/Tox analysis and the shortlisted compounds were docked into the kinase domain of EGFR and simulated there using atomic-level selection of the grid. The shortlisted compounds from molecular docking analysis were subjected to MM-PBSA calculations. The in silico data generated is giving a strong lead compound for further in vitro and in vivo analysis.     

Identification of potential inhibitors for HCV NS3 genotype 4a by combining protein–ligand interaction fingerprint, 3D pharmacophore,docking, and dynamic simulation

HCV NS3 protease domain has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting HCV genotype 1 infection. HCV genotype 4a dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS3 of genotype 4a using homology modeling, PLIF (protein–ligand interaction fingerprint), docking, pharmacophore, and dynamic simulation. A high-quality 3D model of HCV NS3 protease of genotype 4a was constructed using crystal structure of HCV NS3 protease of genotype 1b (PDB ID: 4u01) as a template. PLIF was generated using five crystal structures of HCV NS3 (PDB ID: 4u01, 3kee, 4ktc, 4i33, and 5epn) which revealed the most important residues and their interactions with the co-crystalized ligands. A 3D pharmacophore model consisting of six features was developed from the generated PLIF data and then used as a screening filter for 11,244 compounds. Only 423 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. The highest ranked five hits from docking result (compound (C1–C5)) were selected for further analysis. They exhibited stronger interaction and higher binding affinity than HCV NS3 protease ligands. Dynamic simulation of the protein–best lead complex was performed to validate and augment the virtual screening results and it showed that these compounds have a strong binding affinity and could be very effective in treating HCV genotype 4a infections.     

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.     

In silico study of M18 aspartyl amino peptidase (M18AAP) of Plasmodium vivax as an antimalarial drug target

Plasmodium vivax (Pv) is the second most malaria causing pathogen among Plasmodium species. M18 aspartic aminopeptidase (M18AAP) protein is a single gene copy present in Plasmodium. This protein is functional at the terminal stage of hemoglobin degradation of host and completes the hydrolysis process which makes it an important target for new chemotherapeutics. No experimental and structural study on M18AAP protein of P. vivax is reported till today. This paper advocates the application of multiple computational approaches like protein model prediction, ligand-based 3D QSAR study, pharmacophore, structure-based virtual screening and molecular docking simulation for identification of potent lead molecules against the enzyme. The 3D QSAR model was developed using known bioactive compounds against the PvM18AAP protein which statistically signify the k-NN model with q^2 = 0.7654. The study reports a lead molecule from ligand-centric approach with good binding affinity and possessing lowest docking score. The findings will be helpful for in-vivo and in-vitro validations and development of potent anti-malarial molecules against the drug resistant strains of malaria parasite.     

噬菌体展示HIV-1 Tat38-61碱性区51和55位随机突变体文库的构建

为了构建噬菌体展示Tat38-61(51N/55N) 碱性区突变体文库,进一步研究HIV-1 Tat38-61表位的分子进化筛选,采用含随机核苷酸序列的引物,通过Overlap PCR的方法获得51和55位氨基酸随机突变的全长Tat编码序列,再以此为模板PCR扩增出两端含有Xba I识别序列的Tat38-61突变体片段HIV-1 Tat38-61(51N/55N),克隆至噬菌体展示载体pCANTAB5S上,转化大肠杆菌TG1,经M13K07辅助噬菌体拯救,构建噬菌体展示Tat38-61(51N/55N) 碱性区突变体文库。结果显示文库的库容量为5.0×106,滴度为2.65×1012 TU/mL,阳性克隆率为56.50％;序列分析显示文库中51、55位核苷酸与氨基酸均呈随机性分布,达到了对文库进行分子进化筛选的要求,为获得可用作疫苗候选物的新型Tat突变体奠定基础。     

Structure-based virtual screening and biological evaluation of potent and selective ADAM12 inhibitors

We describe a series of potent and selective inhibitors of ADAM12 that were discovered using computational screening of a focused virtual library. The initial structure-based virtual screening selected 64 compounds from a 3D database of 67,062 molecules. Being evaluated by a cell-based ADAM12 activity assay, compounds 5, 11, 14, 16 were further identified as the potent and selective inhibitors of ADAM12 with low nanomolar IC₅₀ values. The mechanism underlying the potency and selectivity of a representative compound, 5, was investigated through molecular docking studies.     

Docking and scoring with alternative side-chain conformations

We describe a scoring and modeling procedure for docking ligands into protein models that have either modeled or flexible side-chain conformations. Our methodical contribution comprises a procedure for generating new potentials of mean force for the ROTA scoring function which we have introduced previously for optimizing side-chain conformations with the tool IRECS. The ROTA potentials are specially trained to tolerate small-scale positional errors of atoms that are characteristic of (i) side-chain conformations that are modeled using a sparse rotamer library and (ii) ligand conformations that are generated using a docking program. We generated both rigid and flexible protein models with our side-chain prediction tool IRECS and docked ligands to proteins using the scoring function ROTA and the docking programs FlexX (for rigid side chains) and FlexE (for flexible side chains). We validated our approach on the forty screening targets of the DUD database. The validation shows that the ROTA potentials are especially well suited for estimating the binding affinity of ligands to proteins. The results also show that our procedure can compensate for the performance decrease in screening that occurs when using protein models with side chains modeled with a rotamer library instead of using X-ray structures. The average runtime per ligand of our method is 168 seconds on an Opteron V20z, which is fast enough to allow virtual screening of compound libraries for drug candidates.