Novel mutation in hexokinase 2 confers resistance to 2-deoxyglucose by altering protein dynamics.

Glucose is central to many biological processes, serving as an energy source and a building block for biosynthesis. After glucose enters the cell, hexokinases convert it to glucose-6-phosphate (Glc-6P) for use in anaerobic fermentation, aerobic oxidative phosphorylation, and the pentose-phosphate pathway. We here describe a genetic screen in Saccharomyces cerevisiae that generated a novel spontaneous mutation in hexokinase-2, hxk2G238V, that confers resistance to the toxic glucose analog 2-deoxyglucose (2DG). Wild-type hexokinases convert 2DG to 2-deoxyglucose-6-phosphate (2DG-6P), but 2DG-6P cannot support downstream glycolysis, resulting in a cellular starvation-like response. Curiously, though the hxk2G238V mutation encodes a loss-of-function allele, the affected amino acid does not interact directly with bound glucose, 2DG, or ATP. Molecular dynamics simulations suggest that Hxk2G238V impedes sugar binding by altering the protein dynamics of the glucose-binding cleft, as well as the large-scale domain-closure motions required for catalysis. These findings shed new light on Hxk2 dynamics and highlight how allosteric changes can influence catalysis, providing new structural insights into this critical regulator of carbohydrate metabolism. Given that hexokinases are upregulated in some cancers and that 2DG and its derivatives have been studied in anti-cancer trials, the present work also provides insights that may apply to cancer biology and drug resistance.

PARP1: Structural insights and pharmacological targets for inhibition.

Poly(ADP-ribose) polymerase 1 (PARP1, also known as ADPRT1) is a multifunctional human ADP-ribosyltransferase. It plays a role in multiple DNA repair pathways, including the base excision repair (BER), non-homologous end joining (NHEJ), homologous recombination (HR), and Okazaki-fragment processing pathways. In response to DNA strand breaks, PARP1 covalently attaches ADP-ribose moieties to arginine, glutamate, aspartate, cysteine, lysine, and serine acceptor sites on both itself and other proteins. This signal recruits DNA repair proteins to the site of DNA damage. PARP1 binding to these sites enhances ADP-ribosylation via allosteric communication between the distant DNA binding and catalytic domains. In this review, we provide a general overview of PARP1 and emphasize novel potential approaches for pharmacological inhibition. Clinical PARP1 inhibitors bind the catalytic pocket, where they directly interfere with ADP-ribosylation. Some inhibitors may further enhance potency by "trapping" PARP1 on DNA via an allosteric mechanism, though this proposed mode of action remains controversial. PARP1 inhibitors are used clinically to treat some cancers, but resistance is common, so novel pharmacological approaches are urgently needed. One approach may be to design novel small molecules that bind at inter-domain interfaces that are essential for PARP1 allostery. To illustrate these points, this review also includes instructive videos showing PARP1 structures and mechanisms.

Characterization of Female Reproductive Proteases in a Butterfly from Functional and Evolutionary Perspectives.

Molecules that mediate reproductive interactions are some of the most rapidly evolving traits. Researchers have often suggested that this is due to coevolution at key physiological interfaces. However, very few of these interfaces are well understood at the functional level. One such interface is the digestion of the spermatophore in Lepidoptera. Female Lepidoptera have a specialized reproductive organ called the bursa copulatrix that receives and processes the male spermatophore, a complex proteinaceous ejaculate. In the cabbage white butterfly, Pieris rapae, the bursa secretes a mixture of proteases hypothesized to digest the spermatophore. However, these proteases remain biochemically uncharacterized. Using a zymogram approach, we identified six proteases in bursal extracts at sufficiently high concentrations to characterize their in vitro activity. We assessed the modes of action of these bursal enzymes by quantifying their activity following exposure to diagnostic protease inhibitors. A serine protease-specific inhibitor failed to reduce bursal protease digestion of casein. However, a cysteine protease-specific inhibitor did decrease the activity of some proteases. To explore the possible molecular mechanisms responsible for these responses, we created protease homology models. The models mirrored the results of our in vitro experiments, indicating that protease homology models may offer insight into underlying functional mechanisms. Whether the observed bursal protease resistance to known inhibitors is important in the context of spermatophore digestion remains to be tested. However, our results suggest the exciting possibility that bursal protease specificity may have evolved in response to interactions with various proteins and inhibitors present within the female tract during the reproductive process.

Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening.

Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library preparation have restrictive and/or expensive licenses. Freely available alternatives, though often effective, do not fully account for all possible ionization, tautomeric, and ring-conformational variants. We here present Gypsum-DL, a free, robust open-source program that addresses these challenges. As input, Gypsum-DL accepts virtual compound libraries in SMILES or flat SDF formats. For each molecule in the virtual library, it enumerates appropriate ionization, tautomeric, chiral, cis/trans isomeric, and ring-conformational forms. As output, Gypsum-DL produces an SDF file containing each molecular form, with 3D coordinates assigned. To demonstrate its utility, we processed 1558 molecules taken from the NCI Diversity Set VI and 56,608 molecules taken from a Distributed Drug Discovery (D3) combinatorial virtual library. We also used 4463 high-quality protein-ligand complexes from the PDBBind database to show that Gypsum-DL processing can improve virtual-screening pose prediction. Gypsum-DL is available free of charge under the terms of the Apache License, Version 2.0.

Comparative chemical genomics reveal that the spiroindolone antimalarial KAE609 (Cipargamin) is a P-type ATPase inhibitor.

The spiroindolones, a new class of antimalarial medicines discovered in a cellular screen, are rendered less active by mutations in a parasite P-type ATPase, PfATP4. We show here that S. cerevisiae also acquires mutations in a gene encoding a P-type ATPase (ScPMA1) after exposure to spiroindolones and that these mutations are sufficient for resistance. KAE609 resistance mutations in ScPMA1 do not confer resistance to unrelated antimicrobials, but do confer cross sensitivity to the alkyl-lysophospholipid edelfosine, which is known to displace ScPma1p from the plasma membrane. Using an in vitro cell-free assay, we demonstrate that KAE609 directly inhibits ScPma1p ATPase activity. KAE609 also increases cytoplasmic hydrogen ion concentrations in yeast cells. Computer docking into a ScPma1p homology model identifies a binding mode that supports genetic resistance determinants and in vitro experimental structure-activity relationships in both P. falciparum and S. cerevisiae. This model also suggests a shared binding site with the dihydroisoquinolones antimalarials. Our data support a model in which KAE609 exerts its antimalarial activity by directly interfering with P-type ATPase activity.

Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.

The magnitude of the investment required to bring a drug to the market hinders medical progress, requiring hundreds of millions of dollars and years of research and development. Any innovation that improves the efficiency of the drug-discovery process has the potential to accelerate the delivery of new treatments to countless patients in need. "Virtual screening," wherein molecules are first tested in silico in order to prioritize compounds for subsequent experimental testing, is one such innovation. Although the traditional scoring functions used in virtual screens have proven useful, improved accuracy requires novel approaches. In the current work, we use the estrogen receptor to demonstrate that neural networks are adept at identifying structurally novel small molecules that bind to a selected drug target, ultimately allowing experimentalists to test fewer compounds in the earliest stages of lead identification while obtaining higher hit rates. We describe 39 novel estrogen-receptor ligands identified in silico with experimentally determined Ki values ranging from 460 nM to 20 µM, presented here for the first time.

Novel cruzain inhibitors for the treatment of Chagas' disease.

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.

LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders.

One common practice in drug discovery is to optimize known or suspected ligands in order to improve binding affinity. In performing these optimizations, it is useful to look at as many known inhibitors as possible for guidance. Medicinal chemists often seek to improve potency by altering certain chemical moieties of known/endogenous ligands while retaining those critical for binding. To our knowledge, no automated, ligand-based algorithm exists for systematically 'swapping' the chemical moieties of known ligands to generate novel ligands with potentially improved potency. To address this need, we have created a novel algorithm called 'LigMerge'. LigMerge identifies the maximum (largest) common substructure of two three-dimensional ligand models, superimposes these two substructures, and then systematically mixes and matches the distinct fragments attached to the common substructure at each common atom, thereby generating multiple compound models related to the known inhibitors that can be evaluated using computer docking prior to synthesis and experimental testing. To demonstrate the utility of LigMerge, we identify compounds predicted to inhibit peroxisome proliferator-activated receptor gamma, HIV reverse transcriptase, and dihydrofolate reductase with affinities higher than those of known ligands. We hope that LigMerge will be a helpful tool for the drug design community.

Towards the development of novel Trypanosoma brucei RNA editing ligase 1 inhibitors.

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.

Non-bisphosphonate inhibitors of isoprenoid biosynthesis identified via computer-aided drug design.

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.

Computational identification of uncharacterized cruzain binding sites.

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.

A multidimensional strategy to detect polypharmacological targets in the absence of structural and sequence homology.

Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

Including receptor flexibility and induced fit effects into the design of MMP-2 inhibitors.

Matrix metalloproteinases (MMPs) comprise a class of flexible proteins required for normal tissue remodeling. Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer. Nearly all MMP inhibitors have failed in clinical trials, in part due to lack of specificity. Due to the highly dynamic molecular motions of the MMP-2 binding pockets, the rational drug design of MMP inhibitors has been very challenging. To address these challenges, in the current study we combine computer docking with molecular dynamics (MD) simulations in order to incorporate receptor-flexibility and induced-fit effects into the drug-design process. Our strategy identifies molecular fragments predicted to target multiple MMP-2 binding pockets.