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The complexity of living systems exceeds everything else studied by natural sciences. Sophisticated networks of intimately intertwined signaling pathways coordinate cellular functions. Clear understanding how the integration of multiple inputs produces coherent behavior is one of the major challenges of cell biology. Integration via perfectly timed highly regulated protein-protein interactions and precise targeting of the "output" proteins to particular substrates is emerging as a common theme of signaling regulation. This often involves specialized scaffolding proteins, whose key function is to ensure that correct partners come together in an appropriate place at the right time. Defective or faulty signaling underlies many congenital and acquired human disorders. Several pioneering studies showed that ectopic expression of existing proteins or their elements can restore functions destroyed by mutations or normalize the signaling pushed out of balance by disease and/or current small molecule-based therapy. Several recent studies show that proteins with new functional modalities can be generated by mixing and matching existing domains, or via functional recalibration and fine-tuning of existing proteins by precisely targeted mutations. Using arrestins as an example, we describe how manipulation of individual functions yields signaling-biased proteins. Creative protein redesign generates novel tools valuable for unraveling the intricacies of cell biology. Engineered proteins with specific functional changes also have huge therapeutic potential in disorders associated with inherited or acquired signaling errors.
Pubmed ID: 22664341 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. SEVENS summarizes GPCR (G-protein coupled receptor) genes that are identified with high accuracy from 43 eukaryote genomes, by a pipeline integrating such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. This treats a larger data space (than that in currently available other databases), which should include not only the expressed sequences but also the newly identified sequences that cannot be detected by in vivo experiments, although they definitely exist on the genome sequence and are just waiting for the opportunity to express their functions. SEVENS provides the infrastructure of general information of GPCR universe for comparative genomics. We developed an automatic system for identifying GPCR (G-protein coupled receptor) genes from various kinds of genomes, by integrating such software as a gene finder, a sequence alignment tool, a motif and domain assignment tool, and a transmembrane helix predictor. SEVENS enables us to perform a genome-scale overview of the GPCR universe using sequences that are identified with high accuracy (99.4% sensitivity and 96.6% specificity). Using this system, we surveyed the complete genomes of 7 eukaryotes and 224 prokaryotes, and found that there are 4 to 1016 GPCR genes in the 7 eukaryotes, and only a total of 16 GPCR genes in all the prokaryotes. Our preliminary results indicate that 11 subfamilies of the Class A family, the Class 2(B) family, the Class 3(C) family and the fz/smo family are commonly found among human, fly, and nematode genomes. We also analyzed the chromosomal locations of the GPCR genes with the Kolmogorov-Smirnov test, and found that species-specific families, such as olfactory, taste, and chemokine receptors in human and nematode chemoreceptor in worm, tend to form clusters extensively, whereas no significant clusters were detected in fly and plant genomes. How we found GPCR sequences: Candidate GPCR genes were collected from 32 eukaryote genomes by using the GPCR gene discovery pipeline, composed of two stages: (1) the gene finding stage, and (2) the GPCR gene screening stage. 1)Gene finding stage (i.e., translation of genomic sequences into amino acid sequences). 2)GPCR gene screening stage of GPCR candidates by assessing genes with sequence search, motif- and domain assignment, and transmembrane helix (TMH) prediction. Details available at the website. Acknowledgment: We are pleased to acknowledge the use of the BLAST package from NCBI, the SOSUI from Dr. T. Hirokawa, the ALN from Dr. O. Gotoh, the HMMER from Dr. A. Bateman. This work was supported by KAKENHI (208059) (Grant-in-Aid for Publication of Scientific Research Results) of Japan Society for the Promotion of Science (JSPS).
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