Tel Aviv University

About: Tel Aviv University is a education organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Population & Medicine. The organization has 47791 authors who have published 115959 publications receiving 3904391 citations. The organization is also known as: TAU & Universiṭat Tel-Aviv.

The Coral Probiotic Hypothesis

Abstract: Summary Emerging diseases have been responsible for the death of about 30% of corals worldwide during the last 30 years. Coral biologists have predicted that by 2050 most of the world's coral reefs will be destroyed. This prediction is based on the assumption that corals can not adapt rapidly enough to environmental stress-related conditions and emerging diseases. Our recent studies of the Vibrio shiloi/Oculina patagonica model system of the coral bleaching disease indicate that corals can indeed adapt rapidly to changing envi- ronmental conditions by altering their population of symbiotic bacteria. These studies have led us to propose the Coral Probiotic Hypothesis. This hypoth- esis posits that a dynamic relationship exists between symbiotic microorganisms and environmen- tal conditions which brings about the selection of the most advantageous coral holobiont. Changing their microbial partners would allow the corals to adapt to changing environmental conditions more rapidly (days to weeks) than via mutation and selection (many years). An important outcome of the Probiotic Hypothesis would be development of resistance of the coral holobiont to diseases. The following evi- dence supports this hypothesis: (i) Corals contain a large and diverse bacterial population associated with their mucus and tissues; (ii) the coral-associated bacterial population undergoes a rapid change when environmental conditions are altered; and (iii) although lacking an adaptive immune system (no antibodies), corals can develop resistance to pathogens. The Coral Probiotic Hypothesis may help explain the evolutionary success of corals and mod- erate the predictions of their demise.

Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues

Abstract: Motivation: A number of proteins of known threedimensional (3D) structure exist, with yet unknown function. In light of the recent progress in structure determination methodology, this number is likely to increase rapidly. A novel method is presented here: ‘Rate4Site’, which maps the rate of evolution among homologous proteins onto the molecular surface of one of the homologues whose 3D-structure is known. Functionally important regions often correspond to surface patches of slowly evolving residues. Results: Rate4Site estimates the rate of evolution of amino acid sites using the maximum likelihood (ML) principle. The ML estimate of the rates considers the topology and branch lengths of the phylogenetic tree, as well as the underlying stochastic process. To demonstrate its potency, we study the Src SH2 domain. Like previously established methods, Rate4Site detected the SH2 peptide-binding groove. Interestingly, it also detected inter-domain interactions between the SH2 domain and the rest of the Src protein that other methods failed to detect. Availability: Rate4Site can be downloaded at: http:// ashtoret.tau.ac.il/. It is implemented as a web server at:

PredictProtein—an open resource for online prediction of protein structural and functional features

Abstract: PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.