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In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn2C6 transcription factors YRR1 and YRM1 (p

Influenza virus circulates in human, avian, and swine hosts, causing seasonal epidemic and occasional pandemic outbreaks. Influenza neuraminidase, a viral surface glycoprotein, has two sialic acid binding sites. The catalytic (primary) site, which also binds inhibitors such as oseltamivir carboxylate, is responsible for cleaving the sialic acid linkages that bind viral progeny to the host cell. In contrast, the functional annotation of the secondary site remains unclear. Here, we better characterize these two sites through the development of an all-atom, explicitly solvated, and experimentally based integrative model of the pandemic influenza A H1N1 2009 viral envelope, containing ∼160 million atoms and spanning ∼115 nm in diameter. Molecular dynamics simulations of this crowded subcellular environment, coupled with Markov state model theory, provide a novel framework for studying realistic molecular systems at the mesoscale and allow us to quantify the kinetics of the neuraminidase 150-loop transition between the open and closed states. An analysis of chloride ion occupancy along the neuraminidase surface implies a potential new role for the neuraminidase secondary site, wherein the terminal sialic acid residues of the linkages may bind before transfer to the primary site where enzymatic cleavage occurs. Altogether, our work breaks new ground for molecular simulation in terms of size, complexity, and methodological analyses of the components. It also provides fundamental insights into the understanding of substrate recognition processes for this vital influenza drug target, suggesting a new strategy for the development of anti-influenza therapeutics.

Studies of pathogen-host specificity, virulence, and transmissibility are critical for basic research as well as for assessing the pandemic potential of emerging infectious diseases. This is especially true for viruses such as influenza, which continue to affect millions of people annually through both seasonal and occasional pandemic events. Although the influenza virus has been fairly well studied for decades, our understanding of host-cell binding and its relation to viral transmissibility and infection is still incomplete. Assessing the binding mechanisms of complex biological systems with atomic-scale detail is challenging given current experimental limitations. Much remains to be learned, for example, about how the terminal residue of influenza-binding host-cell receptors (sialic acid) interacts with the viral surface. Here, we present an integrative structural-modeling and physics-based computational assay that reveals the sialic acid association rate constants (k on) to three influenza sites: the hemagglutinin (HA), neuraminidase (NA) active, and NA secondary binding sites. We developed a series of highly detailed (atomic-resolution) structural models of fully intact influenza viral envelopes. Brownian dynamics simulations of these systems showed how structural properties, such as stalk height and secondary-site binding, affect sialic acid k on values. Comparing the k on values of the three sialic acid binding sites across different viral strains suggests a detailed model of encounter-complex formation and indicates that the secondary NA binding site may play a compensatory role in host-cell receptor binding. Our method elucidates the competition among these sites, all present on the same virion, and provides a new technology for directly studying the functional balance between HA and NA.

Recent advances in cell-based, high-throughput phenotypic screening have identified new chemical compounds that are active against eukaryotic pathogens. A challenge to their future development lies in identifying these compounds' molecular targets and binding modes. In particular, subsequent structure-based chemical optimization and target-based screening require a detailed understanding of the binding event. Here, we use directed evolution and whole-genome sequencing of a drug-sensitive S. cerevisiae strain to identify the yeast ortholog of TcCyp51, lanosterol-14-alpha-demethylase (TcCyp51), as the target of MMV001239, a benzamide compound with activity against Trypanosoma cruzi, the etiological agent of Chagas disease. We show that parasites treated with MMV0001239 phenocopy parasites treated with another TcCyp51 inhibitor, posaconazole, accumulating both lanosterol and eburicol. Direct drug-protein binding of MMV0001239 was confirmed through spectrophotometric binding assays and X-ray crystallography, revealing a binding site shared with other antitrypanosomal compounds that target Cyp51. These studies provide a new probe chemotype for TcCyp51 inhibition.

Deletions in the stalk of the influenza neuraminidase (NA) surface protein are associated with increased virulence, but the mechanisms responsible for this enhanced virulence are unclear. Here we use microsecond molecular dynamics simulations to explore the effect of stalk deletion on enzymatic activity, contrasting NA proteins from the A/swine/Shandong/N1/2009 strain both with and without a stalk deletion. By modeling and simulating neuraminidase apo glycoproteins embedded in complex-mixture lipid bilayers, we show that the geometry and dynamics of the neuraminidase enzymatic pocket may differ depending on stalk length, with possible repercussions on the binding of the endogenous sialylated-oligosaccharide receptors. We also use these simulations to predict previously unrecognized druggable "hotspots" on the neuraminidase surface that may prove useful for future efforts aimed at structure-based drug design.

BackgroundSharing sets of chemical data (e.g., chemical properties, docking scores, etc.) among collaborators with diverse skill sets is a common task in computer-aided drug design and medicinal chemistry. The ability to associate this data with images of the relevant molecular structures greatly facilitates scientific communication. There is a need for a simple, free, open-source program that can automatically export aggregated reports of entire chemical data sets to files viewable on any computer, regardless of the operating system and without requiring the installation of additional software.ResultsWe here present a program called WebChem Viewer that automatically generates these types of highly portable reports. Furthermore, in designing WebChem Viewer we have also created a useful online web application for remotely generating molecular structures from SMILES strings. We encourage the direct use of this online application as well as its incorporation into other software packages.ConclusionsWith these features, WebChem Viewer enables interdisciplinary collaborations that require the sharing and visualization of small molecule structures and associated sets of heterogeneous chemical data. The program is released under the FreeBSD license and can be downloaded from http://nbcr.ucsd.edu/WebChemViewer. The associated web application (called "Smiley2png 1.0") can be accessed through freely available web services provided by the National Biomedical Computation Resource at http://nbcr.ucsd.edu.

Enoyl-acyl carrier protein reductase (ENR), a critical enzyme in type II fatty acid biosynthesis, is a promising target for drug discovery against hepatocyte-stage Plasmodium falciparum. In order to identify PfENR-specific inhibitors, we docked 70 FDA-approved, bioactive, and/or natural product small molecules known to inhibit the growth of whole-cell blood-stage P. falciparum into several PfENR crystallographic structures. Subsequent in vitro activity assays identified a noncompetitive low-micromolar PfENR inhibitor, celastrol, from this set of compounds.

We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas' disease.

Abstract This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads.

Abstract Background Trypanosoma brucei (T. brucei) is an infectious agent for which drug development has been largely neglected. We here use a recently developed computer program called AutoGrow to add interacting molecular fragments to S5, a known inhibitor of the validated T. brucei drug target RNA editing ligase 1, in order to improve its predicted binding affinity. Results The proposed binding modes of the resulting compounds mimic that of ATP, the native substrate, and provide insights into novel protein-ligand interactions that may be exploited in future drug-discovery projects. Conclusions We are hopeful that these new predicted inhibitors will aid medicinal chemists in developing novel therapeutics to fight human African trypanosomiasis.

Matrix metalloproteinases are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Owing to their role in human disease, matrix metalloproteinase have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had approximately 2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the matrix-metalloproteinase active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. To extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from matrix-metalloproteinase molecular dynamics simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all matrix-metalloproteinase conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand.

The Malaria Eradication Research Agenda (malERA) Consultative Group on Vector Control outline the research needed to ensure vector control at every stage of malaria eradication.Different challenges are presented by the variety of malaria transmission environments present in the world today. In each setting, improved control for reduction of morbidity is a necessary first step towards the long-range goal of malaria eradication and a priority for regions where the disease burden is high. For many geographic areas where transmission rates are low to moderate, sustained and well-managed application of currently available tools may be sufficient to achieve local elimination. The research needs for these areas will be to sustain and perhaps improve the effectiveness of currently available tools. For other low-to-moderate transmission regions, notably areas where the vectors exhibit behaviours such as outdoor feeding and resting that are not well targeted by current strategies, new interventions that target predictable features of the biology/ecologies of the local vectors will be required. To achieve elimination in areas where high levels of transmission are sustained by very efficient vector species, radically new interventions that significantly reduce the vectorial capacity of wild populations will be needed. Ideally, such interventions should be implemented with a one-time application with a long-lasting impact, such as genetic modification of the vectorial capacity of the wild vector population.

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention.

The Distributed Drug Discovery (D3) program trains students in three drug discovery disciplines (synthesis, computational analysis, and biological screening) while addressing the important challenge of discovering drug leads for neglected diseases. This article focuses on implementation of the synthesis component in the second-semester undergraduate organic laboratory. The educational program was started at IUPUI in 2003 and has been carried out over 23 semesters with 65 lab sections by >1200 students. Since the chemistry component is most advanced, it serves as a model for the computational and biological modules in development. Synthetic procedures are based on well-documented, reproducible solid-phase combinatorial chemistry. They are carried out in a 2 × 3 combinatorial grid (Bill-Board) to create a control molecule and five new products (50 µmol scale, ~10-20 mg product, typically high LC/MS purity). The first of these synthetic procedures (D3 Lab 1) utilizes a protected and activated derivative of glycine that is converted in a five-step synthetic sequence (alkylation, hydrolysis, neutralization, acylation, and cleavage from the resin) to N-acylated unnatural amino acids containing two variable diversity elements: a new "a"-side chain and an "N"-acyl group. Based on these combinatorial procedures, large virtual libraries/catalogs of student-accessible molecules can be created and computationally analyzed. Selected molecules are then synthesized and screened by D3 students. Active classroom learning experiences and recorded lectures or demonstrations are used to teach fundamental knowledge and skills in synthesis while enabling students to pursue, with no predetermined outcome, multidisciplinary, distributed, research-based experiments toward drug-lead discovery.