A Multiscale Model of Biofilm as a Senescence-Structured Fluid

We derive a physiologically structured multiscale model for biofilm development. The model has components on two spatial scales, which induce different time scales into the problem. The macroscopic behavior of the system is modeled using growth-induced flow in a domain with a moving boundary. Cell-level processes are incorporated into the model using a so-called physiologically structured variable to represent cell senescence, which in turn affects cell division and mortality. We present computational results for our models which shed light on modeling the combined role senescence and the biofilm state play in the defense strategy of bacteria

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

Improving the accuracy of protein secondary structure prediction using structural alignment

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally

Study of Virtual Molecular Docking of Avocados Compounds against Pseudomonas aeruginosa (5N5H) by Carbapenemase using DOCK 6 Algorithm

Antimicrobial resistance from bacteria is a global health problem that can cause death, and the cause is the emergence of carbapenem resistance Pseudomonas aeruginosa through VIM (Verona integron-encode metallo-β-lactamase), which causes the carbapenem class of antibiotics not to work properly. This species is a gram-negative bacteria which is the main cause of nosocomial pneumonia infection. This study aims to determine in silico inhibitory activity of 50 compounds obtained from avocado (Persea Americana Mill) on VIM, preventing carbapenem antibiotic resistance. The molecular docking process was carried out to test carbapenem's antibiotic resistance control activity by 50 compounds. Docking using DOCK 6 software with a flexible and rigid method, Molecular docking on a protein with PDB ID 5N5H, The target protein was prepared using the Chimera application. Visualization of ligand-protein interactions was carried out with PyMOL and PLIP. The results of the native ligand grid score obtained by each method are -63.013 kcal/mol (Flexible) and -64.032 kcal/mol (Rigid). The best test ligands in the flexible method are 44257090, 14282775 and 44257819, and the grid score are -77.474, -75.274 and -73.219 kcal/mol. The best test ligands in the rigid method are 5280637, 14282775 and 5490064; the grid score is -62.191, -61.714, and -60.453 kcal/mol. The results of the test ligands can provide a better grid score than native ligands, namely in the flexible method. However, the rigid method of grid score results is no better than the native ligand. A good result is that the test ligand grid score is smaller than native ligands, so it has less energy to bind to the active site

Radial and spiral stream formation in Proteus mirabilis

The enteric bacterium Proteus mirabilis, which is a pathogen that forms biofilms in vivo, can swarm over hard surfaces and form concentric ring patterns in colonies. Colony formation involves two distinct cell types: swarmer cells that dominate near the surface and the leading edge, and swimmer cells that prefer a less viscous medium, but the mechanisms underlying pattern formation are not understood. New experimental investigations reported here show that swimmer cells in the center of the colony stream inward toward the inoculation site and in the process form many complex patterns, including radial and spiral streams, in addition to concentric rings. These new observations suggest that swimmers are motile and that indirect interactions between them are essential in the pattern formation. To explain these observations we develop a hybrid cell-based model that incorporates a chemotactic response of swimmers to a chemical they produce. The model predicts that formation of radial streams can be explained as the modulation of the local attractant concentration by the cells, and that the chirality of the spiral streams can be predicted by incorporating a swimming bias of the cells near the surface of the substrate. The spatial patterns generated from the model are in qualitative agreement with the experimental observations

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 212

A bibliography listing 146 reports, articles, and other documents introduced into the NASA scientific and technical information system is presented. The subject coverage concentrates on the biological, psychological, and environmental factors involved in atmospheric and interplanetary flight. Related topics such as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, and exobiology are also given attention