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]     

Dynamics of Ribosomal Protein S1 on a Bacterial Ribosome with Cross-Linking and Mass Spectrometry

Ribosomal protein S1 has been shown to be a significant effector of prokaryotic translation. The protein is in fact capable of efficiently initiating translation, regardless of the presence of a Shine-Dalgarno sequence in mRNA. Structural insights into this process have remained elusive, as S1 is recalcitrant to traditional techniques of structural analysis, such as x-ray crystallography. Through the application of protein cross-linking and high resolution mass spectrometry, we have detailed the ribosomal binding site of S1 and have observed evidence of its dynamics. Our results support a previous hypothesis that S1 acts as the mRNA catching arm of the prokaryotic ribosome. We also demonstrate that in solution the major domains of the 30S subunit are remarkably flexible, capable of moving 30–50Å with respect to one another.Initiation of translation is often the rate-limiting step of protein biosynthesis (1). In prokaryotes, this process is widely recognized to be directed by the Shine-Dalgarno (S.D.)¹ sequence of mRNA and its complementation with the 3′ end of 16S rRNA (2). However, binding of the S.D. sequence to the ribosome is not obligatory for initiation. Ribosomal protein S1, widely conserved in prokaryotes, (3) has been shown to efficiently initiate translation, regardless of the presence of an S.D. sequence (4, 5).S1 is a strikingly atyptical ribosomal protein, being both the largest (61 kDa) and the most acidic (pI 4.7) (6). The protein is composed of six homologous repeats each forming beta barrel domains (3) that in solution comprise a highly elongated structure spanning up to ca. 230 Å (7). This length is comparable to the diameter of the ribosome itself. In addition to these anomalous characteristics, S1 is also one of only two ribosomal proteins that has been attributed functional significance (6). Ribosomal protein S1, for instance, has no apparent role in the assembly of the ribosome, (2) yet is critical for translation in E. coli (8, 9). The functional significance of S1 is related to its most pronounced characteristic, the ability to simultaneously bind mRNA and the ribosome. Analysis of fragments produced by limited proteolysis and chemical cleavage of S1 has shown that an N-terminal fragment of S1 (residues 1–193) binds the ribosome (10) but not RNA (11). Likewise, a C-terminal fragment (res 172–557) binds RNA (12, 13) but not the ribosome (6, 10). By nature of this bi-functional structure, S1 enhances the E. coli ribosome''s affinity for RNA ∼5000 fold (14) and can directly mediate initiation of translation by binding the 5′ UTR of mRNA (4, 5). These observations have led to the hypothesis that S1 acts as a catching arm for the prokaryotic ribosome, working to bring mRNA to the proximity of the ribosome and thereby facilitate initiation (6).Unfortunately, structural analyses capturing how S1 is able to function in this manner remain elusive. A high-resolution crystal structure of ribosome bound S1, or even free S1, does not exist, because S1 is recalcitrant to crystallography (6). Preparation of ribosomes for x-ray crystallography actually involves the deliberate removal of ribosomal protein S1 as a means to improve the reproducibility of crystallization and the quality of the ribosome crystals formed (15–17). The structure and interactions of the protein have nevertheless intrigued structural biologists for decades. However, studies completed to date have failed to convincingly demonstrate the interaction between S1 and the rest of the 30S subunit, because they were incapable of localizing the individual S1 domains (16, 18–20).We have studied the binding of S1 to the 30S subunit by combining cross-linking with mass spectrometry. Chemical cross-linking has long been appreciated as a technique to probe protein-protein interactions (21, 22). With the advent of modern mass spectrometers, it can be very effectively employed to confidently identify the exact residues involved in linkages (23–28). In most cross-linking analyses, protein residues are targeted for covalent modification with a molecule that contains two reactive groups separated by a spacer arm of known length. Only protein residues closer than the length of the spacer arm are capable of being linked. Identification of cross-linked residues thereby provides distance constraints for structural modeling. In this work, the novel amidinating protein cross-linker, DEST (diethyl suberthioimidate), was employed (29, 30). This amine reactive reagent, unlike commercially available reagents, preserves the native basicity of the residues it modifies while being effective at physiological pH. Use of the reagent is unlikely to perturb protein structure and the modifications it imparts are compatible with ionization for mass spectrometry. We have additionally shown that the cross-links it forms can be efficiently enriched from other components of proteolytic digests using strong cation exchange (SCX) chromatography, (30) and that DEST cross-linking of ribosomes yields structural information in excellent agreement with x-ray crystallography (29). Although DEST is an 11Å spacer arm cross-linker, it links alpha carbons up to 24Å apart because of the length and flexibility of lysine side chains. Nevertheless, this is sufficient resolution to approximate the binding positions of the 10kDa domains of S1. Furthermore, multiple cross-linking of a single domain significantly enhances the resolution with which it can be localized.Here, through the application of protein cross-linking and high resolution mass spectrometry, we show that S1 binds to the 30S subunit near the anti-S.D. motif of the 16S rRNA, demonstrate that it is highly elongated even when bound to the ribosome, and provide evidence that its C-terminal mRNA binding region is remarkably dynamic. Our results thus indicate S1 is structurally poised, as previously hypothesized, (6) to act as the mRNA catching arm of the prokaryotic ribosome.     

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]     

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.     

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]     

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]