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]     

Binding Modes of Aromatic Ligands to Mammalian Heme Peroxidases with Associated Functional Implications: CRYSTAL STRUCTURES OF LACTOPEROXIDASE COMPLEXES WITH ACETYLSALICYLIC ACID, SALICYLHYDROXAMIC ACID, AND BENZYLHYDROXAMIC ACID*

The binding and structural studies of bovine lactoperoxidase with three aromatic ligands, acetylsalicylic acid (ASA), salicylhydoxamic acid (SHA), and benzylhydroxamic acid (BHA) show that all the three compounds bind to lactoperoxidase at the substrate binding site on the distal heme side. The binding of ASA occurs without perturbing the position of conserved heme water molecule W-1, whereas both SHA and BHA displace it by the hydroxyl group of their hydroxamic acid moieties. The acetyl group carbonyl oxygen atom of ASA forms a hydrogen bond with W-1, which in turn makes three other hydrogen-bonds, one each with heme iron, His-109 N^ϵ2, and Gln-105 N^ϵ2. In contrast, in the complexes of SHA and BHA, the OH group of hydroxamic acid moiety in both complexes interacts with heme iron directly with Fe-OH distances of 3.0 and 3.2Å respectively. The OH is also hydrogen bonded to His-109 N^ϵ2 and Gln-105N^ϵ2. The plane of benzene ring of ASA is inclined at 70.7° from the plane of heme moiety, whereas the aromatic planes of SHA and BHA are nearly parallel to the heme plane with inclinations of 15.7 and 6.2°, respectively. The mode of ASA binding provides the information about the mechanism of action of aromatic substrates, whereas the binding characteristics of SHA and BHA indicate the mode of inhibitor binding.Lactoperoxidase (LPO)⁴ (EC 1.11.1.7) is a member of the family of glycosylated mammalian heme-containing peroxidase enzymes which also includes myeloperoxidase (MPO), eosinophil peroxidase (EPO), and thyroid peroxidase. These enzymes also show functional similarities to non-homologous plant and fungal peroxidases because they follow a similar scheme of reaction (1–3), but their modes of ligand binding differ considerably. Furthermore, the association of the prosthetic heme group in mammalian peroxidases is through covalent bonds (4–9), whereas covalent linkages are absent in other peroxidases (10–14). Among the four mammalian peroxidases, the prosthetic heme group is linked through three covalent bonds in MPO, whereas in LPO, EPO, and thyroid peroxidase only two covalent linkages are formed. So far the detailed crystal structures of only two mammalian peroxidases, MPO and LPO, are known (15–20). One of the most striking differences between these two mammalian peroxidases is concerned with the basic structural organization in which MPO exists as a covalently linked dimer, whereas LPO is a monomeric protein. At present the fundamental questions pertaining to mammalian heme peroxidases are (i) what distinguishes between the aromatic ligands that one ligand acts as a substrate, whereas the other ligand works as an inhibitor and (ii) how the substrate and inhibitor specificities differ between two enzymes lactoperoxidase and myeloperoxidase.Lactoperoxidase oxidizes inorganic ions, preferentially thiocyanate (SCN⁻), and to a lesser extent, bromide (Br⁻), whereas in the case of myeloperoxidase the chloride (Cl⁻) ion is a preferred substrate (21, 22). The mammalian peroxidases including LPO are also involved in catalyzing the single electron oxidation of various physiologically important organic aromatic substrates including phenols (23, 24), catecholamines, and catechols (25–27) as well as other experimental model compounds such as aromatic amines (28), polychlorinated biphenyls (29), steroid hormones (30–32), and polycyclic aromatic hydrocarbons (33). However, the mode of binding of aromatic ligands and associated functional implications are not yet clearly understood. Surprisingly, the structural data on the complexes of mammalian peroxidases with aromatic ligands are conspicuously lacking. The only available structural report is on the complex of MPO with salicylhydroxamic acid (SHA) (34). Even in this case, the coordinates of this structure are not available for a detailed analysis. In the case of non-homologous plant peroxidases, a few crystal structures of their complexes with aromatic compounds are available (35–38), but their modes of binding are not very similar to those of mammalian peroxidases because the distal ligand binding sites in mammalian and plant peroxidases differ markedly. In this regard it is pertinent to note that the substrate binding site in peroxidases, in general, is observed at the δ-heme edge of the heme moiety on the distal side; in plant peroxidases an additional ligand binding site has also been observed at γ-heme edge (39–41). Unlike those in mammalian peroxidases where the heme moiety is buried deeply in the protein core, in plant peroxidases it is located close to the surface of the protein. Therefore, to characterize the mode of binding of the aromatic substrates and aromatic inhibitors and also for defining the subsites in the substrate binding site, we have determined the crystal structures of three complexes of bovine lactoperoxidase with aromatic ligands, acetylsalicylic acid (ASA), SHA, and benzylhydroxamic acid (BHA). Acetylsalicylic acid can be oxidized by lactoperoxidase to ASA free radical (42), whereas both salicylhydroxamic acid and benzylhydroxamic acid act as potent inhibitors of mammalian peroxidases (43–47). The determination of binding characteristics of these compounds having different actions has helped in establishing the relationship between the modes of binding and their potential actions as the substrates and inhibitors. To the best of our knowledge, this is the first structural report on the modes of binding of three aromatic ligands, ASA, SHA, and BHA, to LPO as well as the first structural study of the complexes of any mammalian peroxidase with ASA and BHA. These studies have shown that ASA, SHA, and BHA bind to LPO at the substrate binding site on the distal side. The SHA and BHA directly interact with the heme iron, whereas ASA interacts through the heme water molecule, which in turn is hydrogen-bonded to the heme iron. These studies have provided a greater insight into the mechanisms of substrate and inhibitor binding in the two mammalian peroxidases.     

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 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]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

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]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]