Structure of the c14 Rotor Ring of the Proton Translocating Chloroplast ATP Synthase

The structure of the membrane integral rotor ring of the proton translocating F₁F₀ ATP synthase from spinach chloroplasts was determined to 3.8 Å resolution by x-ray crystallography. The rotor ring consists of 14 identical protomers that are symmetrically arranged around a central pore. Comparisons with the c₁₁ rotor ring of the sodium translocating ATPase from Ilyobacter tartaricus show that the conserved carboxylates involved in proton or sodium transport, respectively, are 10.6–10.8 Å apart in both c ring rotors. This finding suggests that both ATPases have the same gear distance despite their different stoichiometries. The putative proton-binding site at the conserved carboxylate Glu⁶¹ in the chloroplast ATP synthase differs from the sodium-binding site in Ilyobacter. Residues adjacent to the conserved carboxylate show increased hydrophobicity and reduced hydrogen bonding. The crystal structure reflects the protonated form of the chloroplast c ring rotor. We propose that upon deprotonation, the conformation of Glu⁶¹ is changed to another rotamer and becomes fully exposed to the periphery of the ring. Reprotonation of Glu⁶¹ by a conserved arginine in the adjacent a subunit returns the carboxylate to its initial conformation.ATP synthases found in the energy-transducing membranes of bacteria, mitochondria, and chloroplasts catalyze ATP synthesis and ATP hydrolysis coupled with transmembrane proton or sodium ion transport. The enzymes are multi-subunit complexes composed of an extra-membranous catalytic F₁ domain and an interconnected integral membrane F₀ domain. The hydrophilic F₁ domain consists of five different polypeptides with a stoichiometry of α₃β₃γδϵ. Detailed structural information obtained with the mitochondrial enzyme (1–3) in combination with biochemical (4), biophysical (5), and single molecule studies (6–9) revealed that synthesis or hydrolysis of ATP in the F₁ domain is accomplished via a rotary catalytic mechanism. In addition to information on the catalytic mechanism, structure analysis and single molecule studies of the mitochondrial or the chloroplast F₁ complex have also unraveled the molecular mechanism of several F₁-specific inhibitors (10–14). Less detailed information is available on the integral membrane F₀ domain, which consists of three different polypeptides (a, b, and c) and mediates the transfer of protons or sodium ions across the membrane. Subunits a and b were shown to reside at the periphery of a cylindrical complex formed by multiple copies of the c subunit (15–18). The number of c subunits in the cylindrical subcomplex shows substantial variation in different organisms. Ten protomers are found in ATP synthases from yeast, Escherichia coli and Bacillus PS3 (19–21), 11 in Ilyobacter tartaricus, Propionigenium modestum, and Clostridium paradoxum (22–24), 13 in the thermoalkalophilic Bacillus TA2.TA1 (25), 14 in spinach chloroplasts (26), and 15 in the cyanobacterium Spirulina platensis (27). The structure of isolated subunits a, b, and c from E. coli has been studied by mutagenesis analysis and by NMR spectroscopy in a mixed solvent that was suggested to mimic the membrane environment (28–32). These studies showed that subunit a folds with five membrane-spanning helices. The fourth of these helices directly interacts with subunit c and contains a conserved arginine (Arg²¹⁰), which is thought to be involved in proton transfer (33). Subunit b, which is present in two copies in the intact F₀, contains a single transmembrane helix. Cross-linking data support a direct interaction of the two copies of the b subunit (29). Subunit c was studied at two different pH values to obtain the protonated and deprotonated form of a conserved carboxylate (Asp⁶¹ in E. coli) that was shown to be essential for proton transport (34). NMR spectroscopy revealed that the isolated c subunit consists of two long hydrophobic membrane spanning segments connected by a short hydrophilic loop (30, 35). This loop is located close to the γ and ϵ subunit on the F₁ side of the membrane (36, 37). Low resolution x-ray crystallography, cryo-electron microscopy, and atomic force microscopy showed that the membrane-spanning helices of the multiple copies of subunit c in the intact F₀ complex are tightly packed in two concentric rings (19, 22, 26). Atomic resolution of the c ring was recently provided for the Na⁺-translocating F-type ATPase from I. tartaricus (38) and the related Na⁺-translocating V-type ATPase from Enterococcus hirae (39). Rotation of the c ring was demonstrated by cross-linking (18), fluorescence studies (40), and single molecule visualization (41, 42). Based on the structural and biochemical information on F₁ and F₀, different mechanical models have been proposed describing how the rotation of the c ring is coupled to the rotation of the F₁ rotor subunits. This rotation in turn drives sequential conformational shifts at the three catalytic β subunits that result in ATP synthesis (43–45). Vice versa hydrolysis of ATP in the F₁ domain is thought to drive rotation of the γϵc_10–15 subcomplex and transports protons or sodium ions across the membrane.Here we describe the crystal structure of the chloroplast c₁₄ rotor, which is the first structure of an isolated c ring rotor from a proton driven ATPase. The structure was solved by molecular replacement using a tetradecameric search model that was generated from a monomer taken from the I. tartaricus c₁₁ structure. The imposition of noncrystallographic symmetry restraints during refinement substantially improved electron density and structure determination.     

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]     

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.     

Dbf4-Cdc7 Phosphorylation of Mcm2 Is Required for Cell Growth

The Dbf4-Cdc7 kinase (DDK) is required for the activation of the origins of replication, and DDK phosphorylates Mcm2 in vitro. We find that budding yeast Cdc7 alone exists in solution as a weakly active multimer. Dbf4 forms a likely heterodimer with Cdc7, and this species phosphorylates Mcm2 with substantially higher specific activity. Dbf4 alone binds tightly to Mcm2, whereas Cdc7 alone binds weakly to Mcm2, suggesting that Dbf4 recruits Cdc7 to phosphorylate Mcm2. DDK phosphorylates two serine residues of Mcm2 near the N terminus of the protein, Ser-164 and Ser-170. Expression of mcm2-S170A is lethal to yeast cells that lack endogenous MCM2 (mcm2Δ); however, this lethality is rescued in cells harboring the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Mcm2 is required for cell growth.The Cdc7 protein kinase is required throughout the yeast S phase to activate origins (1, 2). The S phase cyclin-dependent kinase also activates yeast origins of replication (3–5). It has been proposed that Dbf4 activates Cdc7 kinase in S phase, and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6). However, it is not known how Dbf4-Cdc7 (DDK)² acts during S phase to trigger the initiation of DNA replication. DDK has homologs in other eukaryotic species, and the role of Cdc7 in activation of replication origins during S phase may be conserved (7–10).The Mcm2-7 complex functions with Cdc45 and GINS to unwind DNA at a replication fork (11–15). A mutation of MCM5 (mcm5-bob1) bypasses the cellular requirements for DBF4 and CDC7 (16), suggesting a critical physiologic interaction between Dbf4-Cdc7 and Mcm proteins. DDK phosphorylates Mcm2 in vitro with proteins purified from budding yeast (17, 18) or human cells (19). Furthermore, there are mutants of MCM2 that show synthetic lethality with DBF4 mutants (6, 17), suggesting a biologically relevant interaction between DBF4 and MCM2. Nevertheless, the physiologic role of DDK phosphorylation of Mcm2 is a matter of dispute. In human cells, replacement of MCM2 DDK-phosphoacceptor residues with alanines inhibits DNA replication, suggesting that Dbf4-Cdc7 phosphorylation of Mcm2 in humans is important for DNA replication (20). In contrast, mutation of putative DDK phosphorylation sites at the N terminus of Schizosaccharomyces pombe Mcm2 results in viable cells, suggesting that phosphorylation of S. pombe Mcm2 by DDK is not critical for cell growth (10).In budding yeast, Cdc7 is present at high levels in G₁ and S phase, whereas Dbf4 levels peak in S phase (18, 21, 22). Furthermore, budding yeast DDK binds to chromatin during S phase (6), and it has been shown that Dbf4 is required for Cdc7 binding to chromatin in budding yeast (23, 24), fission yeast (25), and Xenopus (9). Human and fission yeast Cdc7 are inert on their own (7, 8), but Dbf4-Cdc7 is active in phosphorylating Mcm proteins in budding yeast (6, 26), fission yeast (7), and human (8, 10). Based on these data, it has been proposed that Dbf4 activates Cdc7 kinase in S phase and that Dbf4 interaction with Cdc7 is essential for Cdc7 kinase activity (6, 9, 18, 21–24). However, a mechanistic analysis of how Dbf4 activates Cdc7 has not yet been accomplished. For example, the multimeric state of the active Dbf4-Cdc7 complex is currently disputed. A heterodimer of fission yeast Cdc7 (Hsk1) in complex with fission yeast Dbf4 (Dfp1) can phosphorylate Mcm2 (7). However, in budding yeast, oligomers of Cdc7 exist in the cell (27), and Dbf4-Cdc7 exists as oligomers of 180 and 300 kDa (27).DDK phosphorylates the N termini of human Mcm2 (19, 20, 28), human Mcm4 (10), budding yeast Mcm4 (26), and fission yeast Mcm6 (10). Although the sequences of the Mcm N termini are poorly conserved, the DDK sites identified in each study have neighboring acidic residues. The residues of budding yeast Mcm2 that are phosphorylated by DDK have not yet been identified.In this study, we find that budding yeast Cdc7 is weakly active as a multimer in phosphorylating Mcm2. However, a low molecular weight form of Dbf4-Cdc7, likely a heterodimer, has a higher specific activity for phosphorylation of Mcm2. Dbf4 or DDK, but not Cdc7, binds tightly to Mcm2, suggesting that Dbf4 recruits Cdc7 to Mcm2. DDK phosphorylates two serine residues of Mcm2, Ser-164 and Ser-170, in an acidic region of the protein. Mutation of Ser-170 is lethal to yeast cells, but this phenotype is rescued by the DDK bypass mutant mcm5-bob1. We conclude that DDK phosphorylation of Ser-170 of Mcm2 is required for budding yeast growth.