Discovery of a novel HCV helicase inhibitor by a de novo drug design approach

Fragment-to-lead tailored in silico design

Fragment-based drug discovery (FBDD) emerged as a disruptive technology and became established during the last two decades. Its rationality and low entry costs make it appealing, and the numerous examples of approved drugs discovered through FBDD validate the approach. However, FBDD still faces numerous challenges. Perhaps the most important one is the transformation of the initial fragment hits into viable leads. Fragment-to-lead (F2L) optimization is resource-intensive and is therefore limited in the possibilities that can be actively pursued. In silico strategies play an important role in F2L, as they can perform a deeper exploration of chemical space, prioritize molecules with high probabilities of being active and generate non-obvious ideas. Here we provide a critical overview of current in silico strategies in F2L optimization and highlight their remarkable impact. While very effective, most solutions are target- or fragment- specific. We propose that fully integrated in silico strategies, capable of automatically and systematically exploring the fast-growing available chemical space can have a significant impact on accelerating the release of fragment originated drugs.

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

Structural Study of the DNA: Clock/Bmal1 Complex Provides Insights for the Role of Cortisol, hGR, and HPA Axis in Stress Management and Sleep Disorders

Herein, we deploy an in silico pipeline of structural bioinformatics, thermodynamics, and molecular dynamics to investigate the role of cortisol in circadian rhythms, biorhythms, stress response, and even sleep disorders. Our study shows that high concentrations of cortisol intercalate in the minor groove of DNA. This phenomenon widens the adjacent major grooves and provides the Clock/Bmal1 complex with more space to dock and interact with DNA. Then, the strong charges of cortisol pull the alpha helices of the Clock/Bmal1 complex and bend it inward, thus establishing stronger interactions and prolonged signaling. Our results indicate that elevated cortisol levels play an important role in stress, inflammation, and sleep disorders as a result of prolonged and stronger dsDNA - Clock/Bmal1 interactions.

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Olive Oil Polyphenols in Neurodegenerative Pathologies

Neurodegenerative diseases lead to the death of nerve cells in the brain or the spinal cord. A wide range of diseases are included within the group of neurodegenerative disorders, with the most common ones being dementia, Alzheimer's, and Parkinson's diseases. Millions of older people are suffering from such pathologies. The global increase of life expectancy unavoidably leads to a consequent increase in the number of people who will be at some degree affected by neurodegenerative-related diseases. At this moment, there is no effective therapy or treatment that can reverse the loss of neurons. A growing number of studies highlight the value of the consumption of medical foods, and in particular olive oil, as one of the most important components of the Mediterranean diet. A diet based on extra virgin olive oil seems to contribute toward the lowering of risk of age-related pathologies due to high phenol concentration. The link of a polyphenol found in extra virgin olive oil, namely, tyrosol, with the protein tyrosinase, associated to Parkinson's disease is underlined as a paradigm of affiliation between polyphenols and neurodegenerative disorders.

Ortholog of the polymerase theta helicase domain modulates DNA replication in Trypanosoma cruzi

DNA polymerase theta (Polθ), a member of the DNA polymerase family A, exhibits a polymerase C-terminal domain, a central domain, and an N-terminal helicase domain. Polθ plays important roles in DNA repair via its polymerase domain, regulating genome integrity. In addition, in mammals, Polθ modulates origin firing timing and MCM helicase recruitment to chromatin. In contrast, as a model eukaryote, Trypanosoma cruzi exhibits two individual putative orthologs of Polθ in different genomic loci; one ortholog is homologous to the Polθ C-terminal polymerase domain, and the other is homologous to the Polθ helicase domain, called Polθ-polymerase and Polθ-helicase, respectively. A pull-down assay using the T. cruzi component of the prereplication complex Orc1/Cdc6 as bait captured Polθ-helicase from the nuclear extract. Orc1/Cdc6 and Polθ-helicase directly interacted, and Polθ-helicase presented DNA unwinding and ATPase activities. A T. cruzi strain overexpressing the Polθ-helicase domain exhibited a significantly decreased amount of DNA-bound MCM7 and impaired replication origin firing. Taken together, these data suggest that Polθ-helicase modulates DNA replication by directly interacting with Orc1/Cdc6, which reduces the binding of MCM7 to DNA and thereby impairs the firing of replication origins.

Fragment-Based Ligand Designing

Fragment-based drug design strategies have been used in drug discovery since it was first demonstrated using experimental structural biology techniques such as nuclear magnetic resonance (NMR) and X-ray crystallography. The underlying idea is that existing or new chemical entities with known desirable properties may serve both as tool compounds and as starting points for hit-to-lead expansion. Despite the recent advancements, there remain challenges to overcome, such as assembly of the synthetically feasible structures, development of scoring functions to correlate structure and their activities, and fine tuning of the promising molecules. This chapter first covers the theoretical background needed to understand the concepts and the challenges related to the field of study, followed by the description of important protocols and related software. Case studies are presented to demonstrate practical applications.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins

Protein-RNA complexes are important for many biological processes. However, structural modeling of such complexes is hampered by the high flexibility of RNA. Particularly challenging is the docking of single-stranded RNA (ssRNA). We have developed a fragment-based approach to model the structure of ssRNA bound to a protein, based on only the protein structure, the RNA sequence and conserved contacts. The conformational diversity of each RNA fragment is sampled by an exhaustive library of trinucleotides extracted from all known experimental protein–RNA complexes. The method was applied to ssRNA with up to 12 nucleotides which bind to dimers of the RNA recognition motifs (RRMs), a highly abundant eukaryotic RNA-binding domain. The fragment based docking allows a precise de novo atomic modeling of protein-bound ssRNA chains. On a benchmark of seven experimental ssRNA–RRM complexes, near-native models (with a mean heavy-atom deviation of <3 Å from experiment) were generated for six out of seven bound RNA chains, and even more precise models (deviation < 2 Å) were obtained for five out of seven cases, a significant improvement compared to the state of the art. The method is not restricted to RRMs but was also successfully applied to Pumilio RNA binding proteins.

De Novo Design at the Edge of Chaos

Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach

Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.

Ebselen inhibits hepatitis C virus NS3 helicase binding to nucleic acid and prevents viral replication

The hepatitis C virus (HCV) nonstructural protein 3 (NS3) is both a protease, which cleaves viral and host proteins, and a helicase that separates nucleic acid strands, using ATP hydrolysis to fuel the reaction. Many antiviral drugs, and compounds in clinical trials, target the NS3 protease, but few helicase inhibitors that function as antivirals have been reported. This study focuses on the analysis of the mechanism by which ebselen (2-phenyl-1,2-benzisoselenazol-3-one), a compound previously shown to be a HCV antiviral agent, inhibits the NS3 helicase. Ebselen inhibited the abilities of NS3 to unwind nucleic acids, to bind nucleic acids, and to hydrolyze ATP, and about 1 μM ebselen was sufficient to inhibit each of these activities by 50%. However, ebselen had no effect on the activity of the NS3 protease, even at 100 times higher ebselen concentrations. At concentrations below 10 μM, the ability of ebselen to inhibit HCV helicase was reversible, but prolonged incubation of HCV helicase with higher ebselen concentrations led to irreversible inhibition and the formation of covalent adducts between ebselen and all 14 cysteines present in HCV helicase. Ebselen analogues with sulfur replacing the selenium were just as potent HCV helicase inhibitors as ebselen, but the length of the linker between the phenyl and benzisoselenazol rings was critical. Modifications of the phenyl ring also affected compound potency over 30-fold, and ebselen was a far more potent helicase inhibitor than other, structurally unrelated, thiol-modifying agents. Ebselen analogues were also more effective antiviral agents, and they were less toxic to hepatocytes than ebselen. Although the above structure-activity relationship studies suggest that ebselen targets a specific site on NS3, we were unable to confirm binding to either the NS3 ATP binding site or nucleic acid binding cleft by examining the effects of ebselen on NS3 proteins lacking key cysteines.

Unique helicase determinants in the essential conjugative TraI factor from Salmonella enterica serovar Typhimurium plasmid pCU1

The widespread development of multidrug-resistant bacteria is a major health emergency. Conjugative DNA plasmids, which harbor a wide range of antibiotic resistance genes, also encode the protein factors necessary to orchestrate the propagation of plasmid DNA between bacterial cells through conjugative transfer. Successful conjugative DNA transfer depends on key catalytic components to nick one strand of the duplex DNA plasmid and separate the DNA strands while cell-to-cell transfer occurs. The TraI protein from the conjugative Salmonella plasmid pCU1 fulfills these key catalytic roles, as it contains both single-stranded DNA-nicking relaxase and ATP-dependent helicase domains within a single, 1,078-residue polypeptide. In this work, we unraveled the helicase determinants of Salmonella pCU1 TraI through DNA binding, ATPase, and DNA strand separation assays. TraI binds DNA substrates with high affinity in a manner influenced by nucleic acid length and the presence of a DNA hairpin structure adjacent to the nick site. TraI selectively hydrolyzes ATP, and mutations in conserved helicase motifs eliminate ATPase activity. Surprisingly, the absence of a relatively short (144-residue) domain at the extreme C terminus of the protein severely diminishes ATP-dependent strand separation. Collectively, these data define the helicase motifs of the conjugative factor TraI from Salmonella pCU1 and reveal a previously uncharacterized C-terminal functional domain that uncouples ATP hydrolysis from strand separation activity.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Protein signatures using electrostatic molecular surfaces in harmonic space

We developed a novel method based on the Fourier analysis of protein molecular surfaces to speed up the analysis of the vast structural data generated in the post-genomic era. This method computes the power spectrum of surfaces of the molecular electrostatic potential, whose three-dimensional coordinates have been either experimentally or theoretically determined. Thus we achieve a reduction of the initial three-dimensional information on the molecular surface to the one-dimensional information on pairs of points at a fixed scale apart. Consequently, the similarity search in our method is computationally less demanding and significantly faster than shape comparison methods. As proof of principle, we applied our method to a training set of viral proteins that are involved in major diseases such as Hepatitis C, Dengue fever, Yellow fever, Bovine viral diarrhea and West Nile fever. The training set contains proteins of four different protein families, as well as a mammalian representative enzyme. We found that the power spectrum successfully assigns a unique signature to each protein included in our training set, thus providing a direct probe of functional similarity among proteins. The results agree with established biological data from conventional structural biochemistry analyses.

Molecular modeling and pharmacophore elucidation study of the Classical Swine Fever virus helicase as a promising pharmacological target

The Classical Swine Fever virus (CSFV) is a major pathogen of livestock and belongs to the flaviviridae viral family. Even though there aren’t any verified zoonosis cases yet, the outcomes of CSFV epidemics have been devastating to local communities. In an effort to shed light to the molecular mechanisms underlying the structural and drug design potential of the viral helicase, the three dimensional structure of CSFV helicase has been modeled using conventional homology modeling techniques and the crystal structure of the Hepatitis C virus (HCV) as a template. The established structure of the CSFV helicase has been in silico evaluated for its viability using a repertoire of in silico tools. The ultimate goal of this study is to introduce the 3D conformation of the CSFV helicase as a reliable structure that may be used as the designing platform for de novo, structure-based drug design experiments. In this direction using the modeled structure of CSVF helicase, a 3D pharmacophore was designed. The pharmacophore comprises of a series of key characteristics that molecular inhibitors must satisfy in order to achieve maximum predicted affinity for the given enzyme. Overall, invaluable insights and conclusions are drawn from this structural study of the CSFV helicase, which may provide the scientific community with the founding plinth in the fight against CSFV infections through the perspective of the CSFV helicase as a potential pharmacological target. Notably, to date no antiviral agent is available against the CSFV nor is expected soon. Subsequently, there is urgent need for new modern and state-of-the-art antiviral strategies to be developed.

A holistic evolutionary and structural study of flaviviridae provides insights into the function and inhibition of HCV helicase

Viral RNA helicases are involved in duplex unwinding during the RNA replication of the virus. It is suggested that these helicases represent very promising antiviral targets. Viruses of the flaviviridae family are the causative agents of many common and devastating diseases, including hepatitis, yellow fever and dengue fever. As there is currently no available anti-Flaviviridae therapy, there is urgent need for the development of efficient anti-viral pharmaceutical strategies. Herein, we report the complete phylogenetic analysis across flaviviridae alongside a more in-depth evolutionary study that revealed a series of conserved and invariant amino acids that are predicted to be key to the function of the helicase. Structural molecular modelling analysis revealed the strategic significance of these residues based on their relative positioning on the 3D structures of the helicase enzymes, which may be used as pharmacological targets. We previously reported a novel series of highly potent HCV helicase inhibitors, and we now re-assess their antiviral potential using the 3D structural model of the invariant helicase residues. It was found that the most active compound of the series, compound C4, exhibited an IC₅₀ in the submicromolar range, whereas its stereoisomer (compound C12) was completely inactive. Useful insights were obtained from molecular modelling and conformational search studies via molecular dynamics simulations. C12 tends to bend and lock in an almost “U” shape conformation, failing to establish vital interactions with the active site of HCV. On the contrary, C4 spends most of its conformational time in a straight, more rigid formation that allows it to successfully block the passage of the oligonucleotide in the ssRNA channel of the HCV helicase. This study paves the way and provides the necessary framework for the in-depth analysis required to enable the future design of new and potent anti-viral agents.

Discovering new medicines targeting helicases: challenges and recent progress

Helicases are ubiquitous motor proteins that separate and/or rearrange nucleic acid duplexes in reactions fueled by adenosine triphosphate (ATP) hydrolysis. Helicases encoded by bacteria, viruses, and human cells are widely studied targets for new antiviral, antibiotic, and anticancer drugs. This review summarizes the biochemistry of frequently targeted helicases. These proteins include viral enzymes from herpes simplex virus, papillomaviruses, polyomaviruses, coronaviruses, the hepatitis C virus, and various flaviviruses. Bacterial targets examined include DnaB-like and RecBCD-like helicases. The human DEAD-box protein DDX3 is the cellular antiviral target discussed, and cellular anticancer drug targets discussed are the human RecQ-like helicases and eIF4A. We also review assays used for helicase inhibitor discovery and the most promising and common helicase inhibitor chemotypes, such as nucleotide analogues, polyphenyls, metal ion chelators, flavones, polycyclic aromatic polymers, coumarins, and various DNA binding pharmacophores. Also discussed are common complications encountered while searching for potent helicase inhibitors and possible solutions for these problems.

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.

Broad-spectrum antiviral activity including human immunodeficiency and hepatitis C viruses mediated by a novel retinoid thiosemicarbazone derivative

Aromatic aldehyde-derived thiosemicarbazones 4-6, the S-substituted modified thiosemicarbazones 7/8, and a vitamin A-derived (retinoid) thiosemicarbazone derivative 12 were investigated as inhibitors of human hepatitis C virus (HCV) subgenomic RNA replicon Huh7 ET (luc-ubi-neo/ET) replication. Compounds 4-6 and 12 were found to be potent suppressors of HCV RNA replicon replication. The trifluoromethoxy-substituted thiosemicarbazone 6 and the retinoid thiosemicarbazone derivative 12 were even superior in selectivity to the included reference agent recombinant human alpha-interferon-2b, showing potencies in the nanomolar range of concentration. In addition, compounds 5, 6, 8 and 12 were tested as inhibitors of cytopathic effect (CPE) induced by human varicella-zoster virus (VZV) and/or human cytomegalovirus (HCMV). Compounds 4-6, 8 and 12 were additionally examined as inhibitors of CPE induced by cowpox virus and vaccinia virus. Thiosemicarbazone 4 was inhibitory on cowpox and vaccinia virus replication comparable in potency and selectivity to the reference agent cidofovir. Retinoid thiosemicarbazone derivative 12 was active as micromolar inhibitor of VZV, HCMV, and, in addition, human immunodeficiency virus type 1 (HIV-1) replication. These results indicate that thiosemicarbazone derivatives are appropriate lead structures to be evaluated in targeted antiviral therapies for hepatitis C (STAT-C), and that the vitamin A-related thiosemicarbazone derivative 12 emerges as a broad-spectrum antiviral agent, co-suppressing the multiplication of important RNA and DNA viruses.

Inhibition of RNA Helicases of ssRNA(+) Virus Belonging to Flaviviridae, Coronaviridae and Picornaviridae Families

Many viral pathogens encode the motor proteins named RNA helicases which display various functions in genome replication. General strategies to design specific and selective drugs targeting helicase for the treatment of viral infections could act via one or more of the following mechanisms: inhibition of the NTPase activity, by interferences with ATP binding and therefore by limiting the energy required for the unwinding and translocation, or by allosteric mechanism and therefore by stabilizing the conformation of the enzyme in low helicase activity state; inhibition of nucleic acids binding to the helicase; inhibition of coupling of ATP hydrolysis to unwinding; inhibition of unwinding by sterically blocking helicase translocation. Recently, by in vitro screening studies, it has been reported that several benzotriazole, imidazole, imidazodiazepine, phenothiazine, quinoline, anthracycline, triphenylmethane, tropolone, pyrrole, acridone, small peptide, and Bananin derivatives are endowed with helicase inhibition of pathogen viruses belonging to Flaviviridae, Coronaviridae, and Picornaviridae families.

Discovery of potent nucleotide-mimicking competitive inhibitors of hepatitis C virus NS3 helicase

Among the enzymes involved in the life cycle of HCV, the non-structural protein NS3, with its double function of protease and NTPase/helicase, is essential for the virus replication. Exploiting our previous knowledge in the development of nucleotide-mimicking NS3 helicase (NS3h) inhibitors endowed with key structural and electronic features necessary for an optimal ligand-enzyme interaction, we developed the tetrahydroacridinyl derivative 3a as the most potent NS3h competitive inhibitor reported to date (HCV NS3h K(i)=20 nM).

Development of novel therapies for hepatitis C

The current standard of care for the treatment of hepatitis C virus (HCV) infection is a combination of pegylated IFN and ribavirin (Peg-IFN/RBV). Because of the adverse effects associated with both IFN and ribavirin and because Peg-IFN/RBV provides only about a 45-50% sustained virological response (SVR, undetectable HCV RNA for greater than 24 weeks after cessation of therapy) in genotype 1-infected individuals, there is a need for more potent anti-HCV compounds with fewer adverse effects. The twenty-first International Conference on Antiviral Research held in May 2009 in Miami Beach, Florida, featured a special session focused on novel targets for HCV therapy. The session included presentations by world-renowned experts in HCV virology and covered a diverse array of potential targets for the development of new classes of HCV therapies. This review contains concise summaries of discussed topics that included the innate immune response, virus entry, the NS2 protease, the NS3 helicase, NS4B, and NS5A. Each presenter discussed the current knowledge of these targets and provided examples of recent scientific breakthroughs that are enhancing our understanding of these targets. As our understanding of the role of these novel anti-HCV targets increases so will our ability to discover new, more safe and effective anti-HCV therapies.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Advanced in silico Approaches in Antiviral Research

Computer-aided drug design has seen constantly increasing application over the past two decades in every area of drug discovery. It can offer significant advantages over conventional approaches, being far less expensive and faster than conventional methods, or offering the possibility to predict molecular behaviours that cannot be elucidated in any other way. Recent developments in software and hardware make it possible to simulate increasingly complex molecular environments, widening the applicability of in silico studies from the interactions of small molecules with key protein residues, to the simulation of the dynamic evolution of complex biological systems with atomic resolution. Antiviral research offers several open challenges, from a biological, biochemical and pharmaceutical point of view. Computational approaches are already providing some answers and will undoubtedly give more in the near future. Here, we present a brief overview of the cutting-edge computational methods that play a major role in present and future antiviral research.