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]     

The Mouse C2C12 Myoblast Cell Surface N-Linked Glycoproteome: IDENTIFICATION, GLYCOSITE OCCUPANCY, AND MEMBRANE ORIENTATION*

Endogenous regeneration and repair mechanisms are responsible for replacing dead and damaged cells to maintain or enhance tissue and organ function, and one of the best examples of endogenous repair mechanisms involves skeletal muscle. Although the molecular mechanisms that regulate the differentiation of satellite cells and myoblasts toward myofibers are not fully understood, cell surface proteins that sense and respond to their environment play an important role. The cell surface capturing technology was used here to uncover the cell surface N-linked glycoprotein subproteome of myoblasts and to identify potential markers of myoblast differentiation. 128 bona fide cell surface-exposed N-linked glycoproteins, including 117 transmembrane, four glycosylphosphatidylinositol-anchored, five extracellular matrix, and two membrane-associated proteins were identified from mouse C2C12 myoblasts. The data set revealed 36 cluster of differentiation-annotated proteins and confirmed the occupancy for 235 N-linked glycosylation sites. The identification of the N-glycosylation sites on the extracellular domain of the proteins allowed for the determination of the orientation of the identified proteins within the plasma membrane. One glycoprotein transmembrane orientation was found to be inconsistent with Swiss-Prot annotations, whereas ambiguous annotations for 14 other proteins were resolved. Several of the identified N-linked glycoproteins, including aquaporin-1 and β-sarcoglycan, were found in validation experiments to change in overall abundance as the myoblasts differentiate toward myotubes. Therefore, the strategy and data presented shed new light on the complexity of the myoblast cell surface subproteome and reveal new targets for the clinically important characterization of cell intermediates during myoblast differentiation into myotubes.Endogenous regeneration and repair mechanisms are responsible for replacing dead and damaged cells to maintain or enhance tissue and organ function. One of the best examples of endogenous repair mechanisms involves skeletal muscle, which has innate regenerative capacity (for reviews, see Refs. 1–4). Skeletal muscle repair begins with satellite cells, a heterogeneous population of mitotically quiescent cells located in the basal lamina that surrounds adult skeletal myofibers (5, 6), that, when activated, rapidly proliferate (7). The progeny of activated satellite cells, known as myogenic precursor cells or myoblasts, undergo several rounds of division prior to withdrawal from the cell cycle. This is followed by fusion to form terminally differentiated multinucleated myotubes and skeletal myofibers (7, 8). These cells effectively repair or replace damaged cells or contribute to an increase in skeletal muscle mass.The molecular mechanisms that regulate differentiation of satellite cells and myoblasts toward myofibers are not fully understood, although it is known that the cell surface proteome plays an important biological role in skeletal muscle differentiation. Examples include how cell surface proteins modulate myoblast elongation, orientation, and fusion (for a review, see Ref. 8). The organization and fusion of myoblasts is mediated, in part, by cadherins (for reviews, see Refs. 9 and 10), which enhance skeletal muscle differentiation and are implicated in myoblast fusion (11). Neogenin, another cell surface protein, is also a likely regulator of myotube formation via the netrin ligand signal transduction pathway (12, 13), and the family of sphingosine 1-phosphate receptors (Edg receptors) are known key signal transduction molecules involved in regulating myogenic differentiation (14–17). Given the important role of these proteins, identifying and characterizing the cell surface proteins present on myoblasts in a more comprehensive approach could provide insights into the molecular mechanisms involved in skeletal muscle development and repair. The identification of naturally occurring cell surface proteins (i.e. markers) could also foster the enrichment and/or characterization of cell intermediates during differentiation that could be useful therapeutically.Although it is possible to use techniques such as flow cytometry, antibody arrays, and microscopy to probe for known proteins on the cell surface in discrete populations, these methods rely on a priori knowledge of the proteins present on the cell surface and the availability/specificity of an antibody. Proteomics approaches coupled with mass spectrometry offer an alternative approach that is antibody-independent and allows for the de novo discovery of proteins on the surface. One approach, which was used in the current study, exploits the fact that a majority of the cell surface proteins are glycosylated (18). The method uses hydrazide chemistry (19) to immobilize and enrich for glycoproteins/glycopeptides, and previous studies using this chemistry have successfully identified soluble glycoproteins (20–24) as well as cell surface glycoproteins (25–28). A recently optimized hydrazide chemistry strategy by Wollscheid et al. (29) termed cell surface capturing (CSC)¹ technology, reports the ability to identify cell surface (plasma membrane) proteins specifically with little (<15%) contamination from non-cell surface proteins. The specificity stems from the fact that the oligosaccharide structure is labeled using membrane-impermeable reagents while the cells are intact rather than after cell lysis. Consequently, only extracellular oligosaccharides are labeled and subsequently captured. Utilizing information regarding the glycosylation site then allows for a rapid elimination of nonspecifically captured proteins (i.e. non-cell surface proteins) during the data analysis process, a feature that makes this approach unique to methods where no label or tag is used. Additionally, the CSC technology provides information about glycosylation site occupancy (i.e. whether a potential N-linked glycosylation site is actually glycosylated), which is important for determining the protein orientation within the membrane and, therefore, antigen selection and antibody design.To uncover information about the cell surface of myoblasts and to identify potential markers of myoblast differentiation, we used the CSC technology on the mouse myoblast C2C12 cell line model system (30, 31). This adherent cell line derived from satellite cells has routinely been used as a model for skeletal muscle development (e.g. Refs. 1, 32, and 33), skeletal muscle differentiation (e.g. Refs. 34–36), and studying muscular dystrophy (e.g. Refs. 37–39). Additionally, these cells have been used in cell-based therapies (e.g. Refs. 40–42). Using the CSC technology, 128 cell surface N-linked glycoproteins were identified, including several that were found to change in overall abundance as the myoblasts differentiate toward myotubes. The current data also confirmed the occupancy of 235 N-linked glycosites of which 226 were previously unconfirmed. The new information provided by the current study is expected to facilitate the development of useful tools for studying the differentiation of myoblasts toward myotubes.     

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]     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.