Vinyl Sulfones as Antiparasitic Agents and a Structural Basis for Drug Design

Cysteine proteases of the papain superfamily are implicated in a number of cellular processes and are important virulence factors in the pathogenesis of parasitic disease. These enzymes have therefore emerged as promising targets for antiparasitic drugs. We report the crystal structures of three major parasite cysteine proteases, cruzain, falcipain-3, and the first reported structure of rhodesain, in complex with a class of potent, small molecule, cysteine protease inhibitors, the vinyl sulfones. These data, in conjunction with comparative inhibition kinetics, provide insight into the molecular mechanisms that drive cysteine protease inhibition by vinyl sulfones, the binding specificity of these important proteases and the potential of vinyl sulfones as antiparasitic drugs.Sleeping sickness (African trypanosomiasis), caused by Trypanosoma brucei, and malaria, caused by Plasmodium falciparum, are significant, parasitic diseases of sub-Saharan Africa (1). Chagas'' disease (South American trypanosomiasis), caused by Trypanosoma cruzi, affects approximately, 16–18 million people in South and Central America. For all three of these protozoan diseases, resistance and toxicity to current therapies makes treatment increasingly problematic, and thus the development of new drugs is an important priority (2–4).T. cruzi, T. brucei, and P. falciparum produce an array of potential target enzymes implicated in pathogenesis and host cell invasion, including a number of essential and closely related papain-family cysteine proteases (5, 6). Inhibitors of cruzain and rhodesain, major cathepsin L-like papain-family cysteine proteases of T. cruzi and T. brucei rhodesiense (7–10) display considerable antitrypanosomal activity (11, 12), and some classes have been shown to cure T. cruzi infection in mouse models (11, 13, 14).In P. falciparum, the papain-family cysteine proteases falcipain-2 (FP-2)⁶ and falcipain-3 (FP-3) are known to catalyze the proteolysis of host hemoglobin, a process that is essential for the development of erythrocytic parasites (15–17). Specific inhibitors, targeted to both enzymes, display antiplasmodial activity (18). However, although the abnormal phenotype of FP-2 knock-outs is “rescued” during later stages of trophozoite development (17), FP-3 has proved recalcitrant to gene knock-out (16) suggesting a critical function for this enzyme and underscoring its potential as a drug target.Sequence analyses and substrate profiling identify cruzain, rhodesain, and FP-3 as cathepsin L-like, and several studies describe classes of small molecule inhibitors that target multiple cathepsin L-like cysteine proteases, some with overlapping antiparasitic activity (19–22). Among these small molecules, vinyl sulfones have been shown to be effective inhibitors of a number of papain family-like cysteine proteases (19, 23–27). Vinyl sulfones have many desirable attributes, including selectivity for cysteine proteases over serine proteases, stable inactivation of the target enzyme, and relative inertness in the absence of the protease target active site (25). This class has also been shown to have desirable pharmacokinetic and safety profiles in rodents, dogs, and primates (28, 29). We have determined the crystal structures of cruzain, rhodesain, and FP-3 bound to vinyl sulfone inhibitors and performed inhibition kinetics for each enzyme. Our results highlight key areas of interaction between proteases and inhibitors. These results help validate the vinyl sulfones as a class of antiparasitic drugs and provide structural insights to facilitate the design or modification of other small molecule inhibitor scaffolds.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

The N Terminus of Phosphodiesterase TbrPDEB1 of Trypanosoma brucei Contains the Signal for Integration into the Flagellar Skeleton

The precise subcellular localization of the components of the cyclic AMP (cAMP) signaling pathways is a crucial aspect of eukaryotic intracellular signaling. In the human pathogen Trypanosoma brucei, the strict control of cAMP levels by cAMP-specific phosphodiesterases is essential for parasite survival, both in cell culture and in the infected host. Among the five cyclic nucleotide phosphodiesterases identified in this organism, two closely related isoenzymes, T. brucei PDEB1 (TbrPDEB1) (PDEB1) and TbrPDEB2 (PDEB2) are predominantly responsible for the maintenance of cAMP levels. Despite their close sequence similarity, they are distinctly localized in the cell. PDEB1 is mostly located in the flagellum, where it forms an integral part of the flagellar skeleton. PDEB2 is mainly located in the cell body, and only a minor part of the protein localizes to the flagellum. The current study, using transfection of procyclic trypanosomes with green fluorescent protein (GFP) reporters, demonstrates that the N termini of the two enzymes are essential for determining their final subcellular localization. The first 70 amino acids of PDEB1 are sufficient to specifically direct a GFP reporter to the flagellum and to lead to its detergent-resistant integration into the flagellar skeleton. In contrast, the analogous region of PDEB2 causes the GFP reporter to reside predominantly in the cell body. Mutagenesis of selected residues in the N-terminal region of PDEB2 demonstrated that single amino acid changes are sufficient to redirect the reporter from a cell body location to stable integration into the flagellar skeleton.In eukaryotes, the ubiquitous second messenger cyclic AMP (cAMP) is generated from ATP by membrane-integral or by cytoplasmic, CO₂-regulated cyclases (35, 44). The cAMP signal is processed by a small group of receiver proteins, including the regulatory subunit of protein kinase A (28), cAMP-gated ion channels (4), and the guanine-nucleotide-exchange proteins EPAC1 and EPAC2 (39). The cAMP signal is terminated by the action of a family of cyclic nucleotide-specific phosphodiesterases (PDEs) (9). This paradigm is rather straightforward, involves a limited number of players, and is generally well understood, at least in mammalian cells. However, much less is known about how individual cAMP signals are temporally and spatially controlled. Since most eukaryotic adenylyl cyclases are integral membrane proteins, often restricted to specific membrane subdomains (10), cAMP signaling is usually initiated at the cell membrane (40). However, diffusion of cAMP away from its site of generation is rapid, with diffusion coefficients being about 400 μm²/s (8, 15, 29), translating into diffusion velocities of 30 to 40 μm/s. As a consequence, the signal would reach the center of the cell with a diameter of 3 μm within less than 50 ms and would rapidly saturate the entire cell. While regulation through fluctuating cellular levels of cAMP represents a valid paradigm of cAMP signaling, it has become clear that other, more localized modes of cAMP signaling must also exist. Several groups have shown that the cAMP response of a given cell can differ depending on what set of receptors activates the cyclase response (14, 30, 41, 42). Similarly, the cAMP response of endothelial cells depends on the subcellular site where the cAMP is produced. They tighten their barrier function when cAMP is produced by membrane-bound adenylyl cyclases but become more permeable when cAMP is produced in the cytoplasm (17, 45). The distinct subcellular localization of cAMP signals was experimentally demonstrated using an array of techniques (29, 40, 55, 56).Physically tethered PDEs might serve to confine newly synthesized cAMP to defined microdomains. Only cAMP-binding proteins that are localized within or extend into such microdomains would be able to receive the cAMP signal (17, 49). cAMP concentrations within such domains might rise and fall rapidly, reaching peak concentrations much more rapidly and locally far beyond the steady-state cAMP levels measured in whole-cell extracts. Such spatially organized, tethered PDEs can generate local sinks into which cAMP disappears (1, 23). This paradigm would allow the simultaneous presence of numerous local cAMP concentration gradients within a single cell, allowing great flexibility in signal generation and intracellular signal transmission. This concept is based on the distinct subcellular localization and physical association of PDEs with subcellular structures and on the existence of localized subcellular cAMP pools, for which there is extensive experimental support (3, 5, 13, 50, 52). Interestingly, PDEs localized in different subcellular regions may still be able to compensate for each other. Ablation of the cilium-specific PDE1C from the olfactory neurons in the mouse did not prolong response termination, as long as the cytoplasmic PDE4 in the cell body was still present (11).The unicellular eukaryote Trypanosoma brucei is the causative agent of human sleeping sickness in sub-Saharan Africa. It belongs to the large order of the kinetoplastida, which includes many medically and economically important pathogens of humans, their livestock, and their crops worldwide (27). Trypanosomes are very small cells (about 15 by 3 μm in diameter) that carry a single flagellum (10 by 0.5 μm). The volume of a procyclic trypanosome of strain 427 is (9.6 ± 0.8) × 10⁻¹⁴ liter (Markus Engstler, personal communication), with the flagellum representing about 15% of this. A signaling threshold concentration of 1 μM cAMP corresponds to just about 30,000 molecules of cAMP per cell. Given a diffusion coefficient of 400 μm²/s (29), unrestricted diffusion of cAMP would swamp the cell within 50 ms. Obviously, temporal and spatial control of cAMP signaling is crucial for T. brucei. Strategically located, physically tethered PDEs might thus play an important role in the architecture of the cAMP signaling pathways in T. brucei.The genomes of T. brucei and of other kinetoplastids, such as T. vivax, T. cruzi, Leishmania major, L. infantum, and L. braziliensis, all code for the same set of five cyclic nucleotide-specific PDEs (25, 53). In T. brucei, the genes for T. brucei PDEB1 (TbrPDEB1; subsequently termed PDEB1) and TbrPDEB2 (PDEB2) are tandemly arranged on chromosome 9 and code for two very similar cAMP-specific PDEs, each with two GAF (mammalian cyclic GMP-dependent PDEs, Anabaena adenylyl cyclases, Escherichia coli FhlA) domains (21) in their N-terminal regions (38, 57). These two PDEs were also studied experimentally in T. cruzi (12) and L. major (24, 52), and orthologues are present in all kinetoplastid genomes available so far. Despite their high overall sequence similarity, PDEB1 and PDEB2 exhibit distinct subcellular localizations (31). PDEB1 is predominantly found in the flagellum, where it is stably associated with cytoskeletal components that are resistant to detergent extraction. In contrast, PDEB2 is mostly localized in the cell body, from where it is fully extractable by nonionic detergents. However, a minor fraction of PDEB2 also associates with the flagellar skeleton in a Triton-resistant manner, most likely through interaction with PDEB1. Earlier work has shown that both PDEB1and PDEB2 are essential enzymes in bloodstream-form T. brucei (31), while TbPDEA, TbPDEC, and TbPDED play minor roles (20; S. Kunz, unpublished data).     

A Novel Protein Kinase Localized to Lipid Droplets Is Required for Droplet Biogenesis in Trypanosomes

Ubiquitous among eukaryotes, lipid droplets are organelles that function to coordinate intracellular lipid homeostasis. Their morphology and abundance is affected by numerous genes, many of which are involved in lipid metabolism. In this report we identify a Trypanosoma brucei protein kinase, LDK, and demonstrate its localization to the periphery of lipid droplets. Association with lipid droplets was abrogated when the hydrophobic domain of LDK was deleted, supporting a model in which the hydrophobic domain is associated with or inserted into the membrane monolayer of the organelle. RNA interference knockdown of LDK modestly affected the growth of mammalian bloodstream-stage parasites but did not affect the growth of insect (procyclic)-stage parasites. However, the abundance of lipid droplets dramatically decreased in both cases. This loss was dominant over treatment with myriocin or growth in delipidated serum, both of which induce lipid body biogenesis. Growth in delipidated serum also increased LDK autophosphorylation activity. Thus, LDK is required for the biogenesis or maintenance of lipid droplets and is one of the few protein kinases specifically and predominantly associated with an intracellular organelle.Trypanosoma brucei is a single-celled eukaryotic pathogen responsible for human African trypanosomiasis (also known as African sleeping sickness) and nagana in domestic animals. More than 50,000 cases of human disease occur yearly, with over 70 million people at risk. No vaccine exists, and chemotherapy is difficult to administer and prone to pathogen resistance. As T. brucei transits between the mammalian bloodstream and the tsetse fly vector during its life cycle, the organism encounters and adapts to profoundly different environmental conditions. The parasite undergoes dramatic changes in both energy (7, 51) and lipid biosynthesis and metabolism (39, 47, 49) as it shifts between these environments.Protein kinases function in numerous regulatory aspects of the cell, including control of the cell cycle and morphology, responses to stress, and transmission of signals from the extracellular environment or between compartments of the cell. As is the case in other eukaryotes, protein kinases, particularly those associated with membranes, are expected to play pivotal roles in the cell''s ability to sense and appropriately respond to its environment. Trypanosoma brucei possesses over 170 protein kinases (16, 44). Most of these can be assigned to the standard groups of protein kinases based on sequence similarity within the kinase domain. However, sequence similarities with kinases from more well-studied organisms are rarely strong enough to allow one-to-one orthologous relationships to be determined (44), and even those which appear orthologous by sequence have sometimes shown functional divergence (46). Hence, an understanding of the roles of specific protein kinases of trypanosomatids requires an individualized assessment. The initial genome analysis of the trypanosomatids (16) showed a lack of receptor tyrosine kinases, but nine T. brucei predicted serine/threonine kinases were annotated as possessing transmembrane domains. One of these was recently shown to be strategically located at a key interface between the host and parasite: the flagellar pocket (38). This eukaryotic translation initiation factor 2α (eIF2α) family kinase was postulated to play a sensory role in monitoring protein transport.Only a very small number of protein kinases of various organisms have been observed to localize to the membranes of intracellular organelles, most of them to the endoplasmic reticulum (ER) (14, 27, 50). Lipid droplets (also known as lipid bodies, adiposomes, or oil bodies in plants) are thought to arise from the ER, although the routes of protein localization to them are not well understood. They are increasingly recognized as legitimate organelles due to their dynamic roles in energy metabolism (40), lipid trafficking (41), and protection against toxic effects of nonesterified lipids and sterols (18). Studies also suggest that they function as potential protein storage depots (12) and in antigen presentation (10). Although recent efforts to expand the lipid droplet proteome have resulted in a vastly increased and in many cases surprising catalogue of potentially associated proteins (3, 5, 11, 12, 23, 37), relatively little is known as to how these structures form and are regulated within the cell.We examine here a novel T. brucei protein kinase with a predicted transmembrane domain. Surprisingly, this protein is localized intracellularly in association with lipid droplets. RNAi-mediated knockdown of this newly identified kinase, dubbed LDK (for lipid droplet kinase), reveals a role in the formation or maintenance of lipid droplets in both mammalian bloodstream-form (BF) and insect procyclic-form (PF) stages of the parasite life cycle.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]