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]     

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]     

Extent of Antigenic Diversity in the V3 Region of the Surface Glycoprotein,gp120, of Human Immunodeficiency Virus Type 1 Group M and Consequences for Serotyping

Human immunodeficiency virus type 1 (HIV-1) may be studied by molecular or immunological approaches. Most analyses have been performed by genetic comparison of isolates and have led to the definition of clades or subtypes within the major (M) group of HIV-1. Five subtypes (A to E) were initially identified by comparison of genomic sequences. Four new subtypes (F to I) were identified more recently. Amino acid differences in the immunogenic V3 loop between isolates have also been studied, leading to a phenetic classification of at least 14 clusters (1 to 14) of sequences (B. T. M. Korber, K. McInnes, R. F. Smith, and G. Myers, J. Virol. 68:6730–6744, 1994). In this study, we compared the antigenicity of the V3 consensus sequences defined by phylogenetic analysis to the antigenicity of those defined by phenetic analysis. We used a recently developed subtype-specific enzyme immunoassay (SSEIA) that uses the principle of blocking with an excess of peptide in the liquid phase. Two SSEIAs were performed, the first with five V3 sequences defined by phylogenetic analysis and the second with 14 V3 sequences defined by phenetic analysis. A total of 168 HIV-1 sera taken from seropositive individuals from seven different countries or regions were studied. Experimental and statistical data, including correlation matrix and cluster analyses, demonstrated associations between the genetic subtypes and phenetically associated groups. Most of these were predicted by Korber et al. (J. Virol. 68:6730–6744, 1994) by theoretical analysis. We also found that V3 sequences can be grouped into between three and five antigenically unrelated categories. Residues that may be responsible for major antigenic differences were identified at the apex of the V3 loop, within the octapeptide xIGPGxxx, where x represents the critical positions. Our study provides evidence that there is a limited number of V3 serotypes which could be easily monitored by serological assays to study the diversity and dynamics of HIV-1 strains.The diversity of human immunodeficiency virus type 1 (HIV-1) is a major problem in the development of an effective vaccine against AIDS. Many HIV-1 sequences are now available, and phylogenetic analysis resulting in a continuously developing classification into subtypes or clades is possible (45). HIV-1 isolates are classified into the M group (for major) or O group (for outlier). The O group contains only a few variants, all from a limited area of Africa (19, 27, 50). The M group includes variants responsible for the present AIDS pandemic. It contains at least five subtypes (A to E), to which have been added more recently four other subtypes (F to I) (23, 28, 34, 36, 37). Subtypes A, C, D, G, and H are common in Africa (21, 35, 37, 38). Subtype B is the most common in America and Europe (24, 26, 51). Subtype E occurs mainly in Asia (25, 30, 41), and subtype F has been detected in Brazil and Romania (3, 28, 34). These distributions are not restrictive. Subtype C is also present in Asia (India and China), and subtype G is also present in Russia (7, 12, 29). The African subtypes (A, C, and D) and the Asian subtype (E) have also been identified in North America and in European countries (9, 13, 14, 32, 48). All the subtypes are present in Africa, including B (detected in West Africa), E (Central African Republic), and F (Cameroon) (1, 35, 38). Analysis of the genetic diversity of HIV-1 is becoming more difficult due to the increasing frequency of coinfections and recombinations (15, 20, 44).Phylogenetic trees have been generated with gag, env, or tat nucleotide sequences. Shorter DNA sequences encoding the functionally important V3 region of the envelope protein are most frequently used to provide reliable subtype designations (37). The diversity of the immunogenic V3 loop has also been studied by comparing the amino acids of different isolates, leading to a phenetic classification of at least 14 clusters of sequences, each one characterized by a consensus sequence based on the most common amino acid in a given position (22).The heterogeneity of HIV-1 strains is studied mostly by molecular characterization of genomic sequences. This involves sequencing fragments amplified by the PCR or the use of the heteroduplex mobility assay (10, 11). However, although these methods allow direct subtype classification, they are time-consuming and expensive and require highly trained workers. Serotyping of HIV-1 by antibody (Ab) binding to the V3 region has been suggested as an alternative approach (8, 40, 49, 51). Such an approach may make it possible to identify subtypes based on antigenic rather than genetic properties. This immunological information about antigenic diversity might be of value in vaccine development. We recently developed a subtype-specific enzyme immunoassay (SSEIA) which gave results consistent with those of genotyping (4, 48). This assay used V3 consensus sequences defined by genetic classification, so we wanted to compare the antigenicity of these V3 consensus sequences to the antigenicity of those defined by phenetic analysis. The phenetic clustering of V3 loop amino acid sequences is not always consistent with phylogenetic analysis. Our results suggested that a limited number of serotypes may exist and identified amino acids at the tip of the V3 loop that may be responsible for serological discrimination.     

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]     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]