[Show abstract][Hide abstract] ABSTRACT: Proline metabolism is linked to hyperprolinemia, schizophrenia, cutis laxa, and cancer. In the latter case, tumor cells tend to rely on proline biosynthesis rather than salvage. Proline is synthesized from either glutamate or ornithine; both are converted to pyrroline-5-carboxylate (P5C), and then to proline via pyrroline-5-carboxylate reductases (PYCRs). Here, the role of three isozymic versions of PYCR was addressed in human melanoma cells by tracking the fate of (13)C-labeled precursors. Based on these studies we conclude that PYCR1 and PYCR2, which are localized in the mitochondria, are primarily involved in conversion of glutamate to proline. PYCRL, localized in the cytosol, is exclusively linked to the conversion of ornithine to proline. This analysis provides the first clarification of the role of PYCRs to proline biosynthesis.
PLoS ONE 09/2012; 7(9):e45190. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this perspective, we revise the historic notion that cancer is a disease of mitochondria. We summarize recent findings on the function and rewiring of central carbon metabolism in melanoma. Metabolic profiling studies using stable isotope tracers show that glycolysis is decoupled from the tricarboxylic acid (TCA) cycle. This decoupling is not 'dysfunction' but rather an alternate wiring required by tumor cells to remain metabolically versatile. In large part, this requirement is met by glutamine feeding the TCA cycle as an alternative source of carbon. Glutamine is also used in non-conventional ways, like traveling in reverse through the TCA flux to feed fatty acid biosynthesis. Biosynthetic networks linked with non-essential amino acids alanine, serine, arginine, and proline are also significantly impacted by the use of glutamine as an alternate carbon source.
[Show abstract][Hide abstract] ABSTRACT: Limited or regulatory proteolysis plays a critical role in many important biological pathways like blood coagulation, cell proliferation, and apoptosis. A better understanding of mechanisms that control this process is required for discovering new proteolytic events and for developing inhibitors with potential therapeutic value. Two features that determine the susceptibility of peptide bonds to proteolysis are the sequence in the vicinity of the scissile bond and the structural context in which the bond is displayed. In this study, we assessed statistical significance and predictive power of individual structural descriptors and combination thereof for the identification of cleavage sites. The analysis was performed on a data set of >200 proteolytic events documented in CutDB for a variety of mammalian regulatory proteases and their physiological substrates with known 3D structures. The results confirmed the significance and provided a ranking within three main categories of structural features: exposure > flexibility > local interactions. Among secondary structure elements, the largest frequency of proteolytic cleavage was confirmed for loops and lower but significant frequency for helices. Limited proteolysis has lower albeit appreciable frequency of occurrence in certain types of β-strands, which is in contrast with some previous reports. Descriptors deduced directly from the amino acid sequence displayed only marginal predictive capabilities. Homology-based structural models showed a predictive performance comparable to protein substrates with experimentally established structures. Overall, this study provided a foundation for accurate automated prediction of segments of protein structure susceptible to proteolytic processing and, potentially, other post-translational modifications.
Journal of Proteome Research 06/2011; 10(8):3642-51. · 5.06 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The interplay between a protease and its substrates is controlled at many different levels, including coexpression, colocalization, binding driven by ancillary contacts, and the presence of natural inhibitors. Here we focus on the most basic parameter that guides substrate recognition by a protease, the recognition specificity at the catalytic cleft. An understanding of this substrate specificity can be used to predict the putative substrates of a protease, to design protease activated imaging agents, and to initiate the design of active site inhibitors. Our group has characterized protease specificities of several matrix metalloproteinases using substrate phage display. Recently, we have adapted this method to a semiautomated platform that includes several high-throughput steps. The semiautomated platform allows one to obtain an order of magnitude more data, thus permitting precise comparisons among related proteases to define their functional distinctions.
Methods in Molecular Biology 02/2009; 539:93-114. · 1.29 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The Proteolysis MAP (PMAP, http://www.proteolysis.org) is a user-friendly website intended to aid the scientific community in reasoning about proteolytic networks and pathways. PMAP is comprised of five databases, linked together in one environment. The foundation databases, ProteaseDB and SubstrateDB, are driven by an automated annotation pipeline that generates dynamic 'Molecule Pages', rich in molecular information. PMAP also contains two community annotated databases focused on function; CutDB has information on more than 5000 proteolytic events, and ProfileDB is dedicated to information of the substrate recognition specificity of proteases. Together, the content within these four databases will ultimately feed PathwayDB, which will be comprised of known pathways whose function can be dynamically modeled in a rule-based manner, and hypothetical pathways suggested by semi-automated culling of the literature. A Protease Toolkit is also available for the analysis of proteases and proteolysis. Here, we describe how the databases of PMAP can be used to foster understanding of proteolytic pathways, and equally as significant, to reason about proteolysis.
Nucleic Acids Research 11/2008; 37(Database issue):D611-8. · 8.81 Impact Factor