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Browsing by Subject "Sequence Alignment"
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Item The human mu opioid receptor: modulation of functional desensitization by calcium/calmodulin-dependent protein kinase and protein kinase C(Society for Neuroscience, 1995-03) Mestek, A.; Hurley, J.H.; Bye, L.S.; Campbell, A.D.; Chen, Y.; Tian, M.; Liu, J.; Schulman, H.; Yu, L.; Medical and Molecular Genetics, School of MedicineOpioids are some of the most efficacious analgesics used in humans. Prolonged administration of opioids, however, often causes the development of drug tolerance, thus limiting their effectiveness. To explore the molecular basis of those mechanisms that may contribute to opioid tolerance, we have isolated a cDNA for the human mu opioid receptor, the target of such opioid narcotics as morphine, codeine, methadone, and fentanyl. The receptor encoded by this cDNA is 400 amino acids long with 94% sequence similarity to the rat mu opioid receptor. Transient expression of this cDNA in COS-7 cells produced high-affinity binding sites to mu-selective agonists and antagonists. This receptor displays functional coupling to a recently cloned G-protein-activated K+ channel. When both proteins were expressed in Xenopus oocytes, functional desensitization developed upon repeated stimulation of the mu opioid receptor, as observed by a reduction in K+ current induced by the second mu receptor activation relative to that induced by the first. The extent of desensitization was potentiated by both the multifunctional calcium/calmodulin-dependent protein kinase and protein kinase C. These results demonstrate that kinase modulation is a molecular mechanism by which the desensitization of mu receptor signaling may be regulated at the cellular level, suggesting that this cellular mechanism may contribute to opioid tolerance in humans.Item SP5 : Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model(PLOS, 2008-06-04) Zhang, Wei; Liu, Song; Zhou, Yaoqi; BioHealth Informatics, School of Informatics and ComputingHow to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2, SP3, SP4) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP4 based on two independent benchmarks. Moreover, SP5 makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP4 for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP5 server is available at http://sparks.informatics.iupui.edu.