Protein translocation into peroxisomes takes place via recognition of a peroxisomal targeting signal present at either the extreme C termini (PTS1) or N termini (PTS2) of matrix proteins. In mammals and yeast, the peroxisomal targeting signal receptor, Pex5p, recognizes the PTS1 consisting of -SKL or variants thereof. Although many plant peroxisomal matrix proteins are transported through the PTS1 pathway, little is known about the PTS1 receptor or any other peroxisome assembly protein from plants. We cloned tobacco (Nicotiana tabacum) cDNAs encoding Pex5p (Nt-PEX5) based on the protein's interaction with a PTS1-containing protein in the yeast two-hybrid system. Nucleotide sequence analysis revealed that the tobacco Pex5p contains seven tetratricopeptide repeats and that NtPEX5 shares greater sequence similarity with its homolog from humans than from yeast. Expression of NtPEX5 fusion proteins, consisting of the N-terminal part of yeast Pex5p and the C-terminal region of NtPEX5, in a Saccharomyces cerevisiae pex5 mutant restored protein translocation into peroxisomes. These experiments confirmed the identity of the tobacco protein as a PTS1 receptor and indicated that components of the peroxisomal translocation apparatus are conserved functionally. Two-hybrid assays showed that NtPEX5 interacts with a wide range of PTS1 variants that also interact with the human Pex5p. Interestingly, the C-terminal residues of some of these peptides deviated from the established plant PTS1 consensus sequence. We conclude that there are significant sequence and functional similarities between the plant and human Pex5ps.
We describe an innovative bioinformatics course developed under grants from the National Science Foundation and the California State University Program in Research and Education in Biotechnology for undergraduate biology students. The project has been part of a continuing effort to offer students classroom experiences focused on principles and reasoning, using a computer-assisted, problem-based learning model. Here we report on the course design, methods of assessment for the course and materials, and analysis of results obtained in initial offerings of the course.Keywords: Bioinformatics, course design, interdisciplinary curriculum, problem-based learning.In their 2003 report, BIO 2010, a committee of the National Research Council charted a course for educating 21st century biological scientists. The ambitious proposal, aimed at implementation by the year 2010, envisions interdisciplinary undergraduate experiences that seamlessly integrate biology with mathematics, statistics, computer science, physics, and chemistry. The research community has enthusiastically endorsed this new, quantitative bioscience curriculum, viewing the proposed restructuring as essential to transcend the traditional barriers between biology and mathematics-based sciences [2]. The changes, they say, will benefit students in mathematics and the physical sciences by providing biological systems to model while preparing biology students for the new world of quantitative biology. The relatively new discipline known as bioinformatics already provides a bridge from molecular and structural biology to computer science, statistics, and information theory. Adding a bioinformatics course to the undergraduate biology curriculum thus represents an easy, natural step toward the goal of providing quantitative, interdisciplinary coursework for all bioscience students. DESIGN ISSUESThe Central Concept-For students, understanding the fundamental concept of a course can illuminate every aspect of their learning experience. A central, powerful concept provides a framework for thinking, not only through the topics explored in the class but also through related courses and experiences in the real world. For course designers, the explanatory power of a fundamental concept provides an organizing principle. Construction of a course around a primary concept increases the likelihood that students will take away the essence of the discipline and a usable set of thinking skills [3].At its core, bioinformatics is the extraction of biological information (or meaning) from biological data stored in databases. All other topics and questions in bioinformatics, such as how biological data are collected and stored, how protein structure and function can be predicted from sequence data, or how metabolic networks may be visualized, can be understood in relation to this primary concept. The overall design of our undergraduate bioinformatics course reflects our effort to keep this fundamental idea in focus.Curricular Goals-The curriculum project aimed to establish a foundatio...
Vitellogenin, the only phosphoprotein detectable in the plasma of laying hens, is present at an approximate concentration of 1 mg/mL and can be isolated by chromatography on diethylaminoethylcellulose. Vitellogenin has a molecular weight of 235 000--240 000 and contains approximately 3% phosphorus by weight. Evidence that this protein is the precursor of phosvitins includes its ability to act as an acceptor for phosphate with a phosvitin specific kinase, the generation of a peptide similar to phosvitin by trypsinization, and the presence of distinctive peptides of multiple clustered phosphoserine upon partial acid hydrolysis. This partial sequence similarity between phosvitins and vitellogenin has not been previously reported. The phosphorus content and amino acid composition of vitellogenin are consistent with a model which contains two phosvitins and one lipovitellin. The total molecular weights of these proteins (28 000 + 34 000 + 170 000 = 232 000) are close to that of vitellogenin.
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