Caspases are important players in programmed cell death. Normal activities of caspases are critical for the cell life cycle and dysfunction of caspases may lead to the development of cancer and neurodegenerative diseases. They have become a popular target for drug design against abnormal cell death. In this study, the recognition of P5 position in substrates by caspase-3, -6 and -7 has been investigated by kinetics, modeling and crystallography. Crystal structures of caspase-3 and -7 in complexes with substrate analog inhibitor Ac-LDESD-CHO have been determined at resolutions of 1.61 and 2.45 Å, respectively, while a model of caspase-6/LDESD is constructed. Enzymatic study and structural analysis have revealed that Caspase-3 and -6 recognize P5 in pentapeptides, while caspase-7 lacks P5-binding residues.
D-arginine dehydrogenase catalyzes the flavin-dependent oxidative deamination of D-amino acids to the corresponding imino acids and ammonia. The X-ray crystal structures of DADH and its complexes with several imino acids were determined at 1.03-1.30 Å resolution. The DADH crystal structure comprises a product-free conformation and a product-bound conformation. A flexible loop near the active site forms an “active site lid” and may play an essential role in substrate recognition. The DADH Glu87 forms an ionic interaction with the side chain of iminoarginine, suggesting its importance for DADH preference of positively charged D-amino acids. Comparison of the kinetic data of DADH activity on different D-amino acids and the crystal structures demonstrated that this enzyme is characterized by relatively broad substrate specificity, being able to oxidize positively charged and large hydrophobic D-amino acids bound within a flask-like cavity.
Understanding biology at the system level has gained much more attention recently due to the rapid development in genome sequencing and high-throughput measurements. Current simulation methods include deterministic method and stochastic method. Both have their own advantages and disadvantages. Our group has developed a deterministic-stochastic crossover algorithm for simulating biochemical networks. Simulation studies have been performed on biological systems like auto-regulatory gene network and glycolysis system. The new system retains the high efficiency of deterministic method while still reflects the random fluctuations at lower concentration.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:digitalarchive.gsu.edu:biology_diss-1124 |
| Date | 07 December 2012 |
| Creators | Fu, Guoxing |
| Publisher | Digital Archive @ GSU |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Biology Dissertations |
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