Insight into ligand selectivity in HCV NS5B polymerase: molecular dynamics simulations, free energy decomposition and docking

Genome-Wide Identification of Aldehyde Oxidase Genes in Moths and Butterflies Suggests New Insights Into Their Function as Odorant-Degrading Enzymes

Aldehyde oxidases (AOXs) are common detoxifying enzymes in several organisms. In insects, AOXs act in xenobiotic metabolism and as odorant-degrading enzymes (ODEs). These last appear as crucial enzymes in the life cycle of insects, helping to reset their olfactory system, particularly in lepidopterans, which fulfill important ecological roles (e.g., pollination or destructive life cycles). A comprehensive understanding of their olfactory system has provided opportunities to study key chemosensory proteins. However, no significant advance has been made around lepidopteran AOXs research, and even less around butterflies, a recently evolved lineage. In this study we have identified novel AOX gene families in moths and butterflies in order to understand their role as ODEs. Eighteen genomes from both moths and butterflies were used for phylogenetics, molecular evolution and sequence analyses. We identified 164 AOXs, from which 91 are new. Their phylogeny showed two main clades that are potentially related to odorant-degrading function, where both moths and butterflies have AOXs. A first ODE-related clade seems to have a non-ditrysian origin, likely related to plant volatiles. A second ODE-related clade could be more pheromone-biased. Molecular evolution analysis suggests a slight purifying selection process, though a number of sites appeared under positive selection. ODE-related AOXs have changed a phenylalanine residue by proline in the active site. Finally, this study could serve as a reference for further evolutionary and functional studies around Lepidopteran AOXs.

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Nonstructural 5B (NS5B) polymerase and Nonstructural 3/4A (NS3/4A) protease have proven to be promising targets for the development of anti-HCV (Hepatitis C Virus) agents. The NS5B polymerase is of paramount importance in HCV viral replication; therefore, employing NS5B inhibitors was considered an effective way for the treatment of HCV. Identifying inhibitors against NS3/4A serine protease represents another attractive approach applied in anti-HCV drug discovery, which is evidenced by its crucial role of in the biogenesis of the viral replication activity. In this chapter, many different computational approaches including Quantitative Structure-Activity Relationship (QSAR) and virtual screening in anti-HCV drug discovery were considered and discussed in detail. Virtual Screening (VS) techniques, including ligand-based and structure-based, and QSAR have been utilized for the discovery of NS5B inhibitors. Moreover, using various in silico protocols and workflows, a number of studies have been conducted with an aim of identifying potential NS3/4A blockage agents.

Searching for anthranilic acid-based thumb pocket 2 HCV NS5B polymerase inhibitors through a combination of molecular docking, 3D-QSAR and virtual screening

A combination of the following computational methods: (i) molecular docking, (ii) 3-D Quantitative Structure Activity Relationship Comparative Molecular Field Analysis (3D-QSAR CoMFA), (iii) similarity search and (iv) virtual screening using PubChem database was applied to identify new anthranilic acid-based inhibitors of hepatitis C virus (HCV) replication. A number of known inhibitors were initially docked into the "Thumb Pocket 2" allosteric site of the crystal structure of the enzyme HCV RNA-dependent RNA polymerase (NS5B GT1b). Then, the CoMFA fields were generated through a receptor-based alignment of docking poses to build a validated and stable 3D-QSAR CoMFA model. The proposed model can be first utilized to get insight into the molecular features that promote bioactivity, and then within a virtual screening procedure, it can be used to estimate the activity of novel potential bioactive compounds prior to their synthesis and biological tests.

Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

Structure prediction, molecular dynamics simulation and docking studies of D-specific dehalogenase from Rhizobium sp. RC1

Currently, there is no three-dimensional structure of D-specific dehalogenase (DehD) in the protein database. We modeled DehD using ab initio technique, performed molecular dynamics (MD) simulation and docking of D-2-chloropropionate (D-2CP), D-2-bromopropionate (D-2BP), monochloroacetate (MCA), monobromoacetate (MBA), 2,2-dichloropropionate (2,2-DCP), d,l-2,3-dichloropropionate (d,l-2,3-DCP), and 3-chloropropionate (3-CP) into the DehD active site. The sequences of DehD and D-2-haloacid dehalogenase (HadD) from Pseudomonas putida AJ1 have 15% sequence similarity. The model had 80% of the amino acid residues in the most favored region when compared to the crystal structure of DehI from Pseudomonas putida PP3. Docking analysis revealed that Arg107, Arg134 and Tyr135 interacted with D-2CP, and Glu20 activated the water molecule for hydrolytic dehalogenation. Single residue substitutions at 25-30 °C showed that polar residues of DehD were stable when substituted with nonpolar residues and showed a decrease in activity within the same temperature range. The molecular dynamics simulation of DehD and its variants showed that in R134A variant, Arg107 interacted with D-2CP, while in Y135A, Gln221 and Arg231 interacted with D-2CP. It is our emphatic belief that the new model will be useful for the rational design of DehDs with enhanced potentials.

3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications

Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

Allosteric inhibition of the hepatitis C virus NS5B polymerase: in silico strategies for drug discovery and development

Chronic infection by hepatitis C virus (HCV) often leads to severe liver disease including cirrhosis, hepatocellular carcinoma and liver failure. Despite it being more than 20 years since the identification of HCV, the current standard of care for treating the infection is based on aspecific therapy often associated with severe side effects and low-sustained virological response. Research is ongoing to develop new and better medications, including a broad range of allosteric NS5B polymerase inhibitors. This article reviews traditional computational methodologies and more recently developed in silico strategies aimed at identifying and optimizing non-nucleoside inhibitors targeting allosteric sites of HCV NS5B polymerase. The drug-discovery approaches reviewed could provide take-home lessons for general computer-aided research projects.

Structure-based virtual screening, synthesis and SAR of novel inhibitors of hepatitis C virus NS5B polymerase

Hepatitis C virus (HCV) NS5B polymerase is a key target for the development of therapeutic agents aimed at the treatment of HCV infections. Here we report on the identification of novel allosteric inhibitors of HCV NS5B through a combination of structure-based virtual screening, synthesis and structure-activity relationship (SAR) optimization approach. Virtual screening of 260,000 compounds from the ChemBridge database against the tetracyclic indole inhibitor binding pocket of NS5B (allosteric pocket-1, AP-1), sequentially down-sized the library by 4 orders of magnitude to yield 23 candidates. In vitro evaluation of the NS5B inhibitory activity of the in-silico selected compounds resulted in 17% hit rate, identifying two novel chemotypes. Of these, compound 3, bearing the rhodanine scaffold, proved amenable for productive SAR exploration and synthetic modification. As a result, 25 derivatives that exhibited IC₅₀ values ranging from 7.7 to 68.0 μM were developed. Docking analysis of lead compound 28 within the tetracyclic indole- and benzylidene-binding allosteric pockets (AP-1 and AP-3, respectively) of NS5B revealed topological similarities between these two pockets. Compound 28, a novel rhodanine analog with NS5B inhibitory potency in the low micromolar level range may be a promising lead for future development of more potent NS5B inhibitors.