Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

BACKGROUND: The sequencing of the human genome has enabled us to access a comprehensive list of genes (both experimental and predicted) for further analysis. While a majority of the approximately 30000 known and predicted human coding genes are characterized and have been assigned at least one function, there remains a fair number of genes (about 12000) for which no annotation has been made. The recent sequencing of other genomes has provided us with a huge amount of auxiliary sequence data which could help in the characterization of the human genes. Clustering these sequences into families is one of the first steps to perform comparative studies across several genomes. RESULTS: Here we report a novel clustering algorithm (CLUGEN) that has been used to cluster sequences of experimentally verified and predicted proteins from all sequenced genomes using a novel distance metric which is a neural network score between a pair of protein sequences. This distance metric is based on the pairwise sequence similarity score and the similarity between their domain structures. The distance metric is the probability that a pair of protein sequences are of the same Interpro family/domain, which facilitates the modelling of transitive homology closure to detect remote homologues. The hierarchical average clustering method is applied with the new distance metric. CONCLUSION: Benchmarking studies of our algorithm versus those reported in the literature shows that our algorithm provides clustering results with lower false positive and false negative rates. The clustering algorithm is applied to cluster several eukaryotic genomes and several dozens of prokaryotic genomes

Alcohol abstinence and drinking among African women: data from the World Health Surveys

<p>Abstract</p> <p>Background</p> <p>Alcohol use is increasing among women in Africa, and comparable information about women's current alcohol use is needed to inform national and international health policies relevant to the entire population. This study aimed to provide a comparative description of alcohol use among women across 20 African countries.</p> <p>Methods</p> <p>Data were collected as part of the WHO World Health Survey using standardized questionnaires. In total, 40,739 adult women were included in the present study. Alcohol measures included lifetime abstinence, current use (≥1 drink in previous week), heavy drinking (15+ drinks in the previous week) and risky single-occasion drinking (5+ drinks on at least one day in the previous week). Country-specific descriptives of alcohol use were calculated, and K-means clustering was performed to identify countries with similar characteristics. Multiple logistic regression models were fitted for each country to identify factors associated with drinking status.</p> <p>Results</p> <p>A total of 33,841 (81%) African women reported lifetime abstinence. Current use ranged from 1% in Malawi to 30% in Burkina Faso. Among current drinkers, heavy drinking varied between 4% in Ghana to 41% in Chad, and risky single-occasion drinking ranged from <1% in Mauritius to 58% in Chad. Increasing age was associated with increased odds of being a current drinker in about half of the countries.</p> <p>Conclusions</p> <p>A variety of drinking patterns are present among African women with lifetime abstention the most common. Countries with hazardous consumption patterns require serious attention to mitigate alcohol-related harm. Some similarities in factors related to alcohol use can be identified between different African countries, although these are limited and highlight the contextual diversity of female drinking in Africa.</p

TRANSFER NUCLEAR OVERHAUSER EFFECT STUDY OF THE CONFORMATION OF OXYTOCIN BOUND TO BOVINE NEUROPHYSIN-I.

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NMR relaxation analysis of pharmaceutically active peptides

Nuclear spin relaxation (NSR) is a powerful approach for studying dynamics at the ps-ns timescale, and is typically used to characterize fundamental biophysical phenomena such as bond vibrations and fluctuations, which affect the activity of the molecule in question. Here, this chapter will look to the application of NSR to study peptides, which are short chains of amino acids and have shown promise as modalities in drug design. This chapter will begin with a brief description of theoretical and practical aspects related to the use of NSR, such as experimental considerations during data acquisition and processing. As an example of this approach for studying peptide dynamics, this chapter will step through a case study that examines the effect of backbone cyclization on the dynamics of polycyclic disulfide-rich peptides. This case study will focus on a cyclic and linear variant of a promising drug scaffold isolated from sunflower seeds called SFTI-1 (sunflower trypsin inhibitor-1), which is a naturally backbone-cyclic peptide that comprises one cross-bracing disulfide bond