Composition and Methods for Treating \u3cem\u3eYersinia Pestis\u3c/em\u3e Infection

Compositions and methods for treating a Yersinia pestis (Y. pestis) infection are provided. Compositions and methods of for inducing an immune response in a subject are provided. Composition can include a YadC polypeptide

Heat-stable metagenomic carbonic andydrases and their use

The present invention relates to polypeptides having carbonic anhydrase activity and polynucleotides encoding the polypeptides. The invention also relates to nucleic acid constructs, vectors, and host cells comprising the polynucleotides as well as methods of producing and using the polypeptides

2-D Electrophoresis Modeling of Multienzyme Cutting of Polypeptides

2-Dimensional Electrophoresis is one of the tools in the identification of proteins by molecular weight and pH. The display of molecular weight allows the researcher to quickly identify whether a specific protein or peptide string is in the sample. The pH measurement allows even better resolution between different species in the sample. The MultiEnzyme ElectroPhoresis (MEEP) program tries to model that by providing a graph that displays separated protein strings by both molecular weight and pH. The ability to cleave the protein with 43 different enzyme variations allows the researcher to analyze appropriate enzymes to isolate a protein subsequence before the actual experiment or to compare the experimental data with the simulated electrophoresis. This thesis reviews protein cutting simulations that have been done in the past or are currently available. It then describes the MEEP program: how it appears to the user, how the user makes it operate, and how it is structured. The thesis provides validation information for the calculation of molecular weight and isoelectric point. The program will hopefully provide a useful addition for the researcher’s work

10231 Abstracts Collection -- Structure Discovery in Biology: Motifs, Networks & Phylogenies

From 06.06. to 11.06.2010, the Dagstuhl Seminar 10231 ``Structure Discovery in Biology: Motifs, Networks & Phylogenies \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

CLUSS: Clustering of protein sequences based on a new similarity measure

<p>Abstract</p> <p>Background</p> <p>The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "<it>phylogenetic</it>" in the sense of "<it>relatedness of biological functions</it>".</p> <p>Results</p> <p>To show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity.</p> <p>Conclusion</p> <p>We have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.</p

Human immunodeficiency virus neutralizing antibodies and methods of use thereof [APPLICATION]

The invention provides broadly neutralizing antibodies directed to epitopes of Human Immunodeficiency Virus, or HIV. The invention further provides compositions containing HIV antibodies used for prophylaxis, and methods for diagnosis and treatment of HIV infection

A method for probabilistic mapping between protein structure and function taxonomies through cross training

<p>Abstract</p> <p>Background</p> <p>Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>) and classes of functional classification databases (e.g. PROSITE <abbrgrp><abbr bid="B2">2</abbr></abbrgrp>), structure and function of proteins could be probabilistically related.</p> <p>Results</p> <p>We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees.</p> <p>Conclusion</p> <p>We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.</p