Περίληψη
Στα πλαίσια της παρούσας διατριβής μελετήθηκε εκτενώς, με υπολογιστικές μεθόδους, η πολύ ενδιαφέρουσα υπεροικογένεια των GPCRs, καθώς και οι G-πρωτεΐνες. Αναπτύχθηκαν δύο μέθοδοι:Η μέθοδος GPCRpipe (http://bioinformatics.biol.uoa.gr/GPCRpipe/) χαρακτηρίζει πρωτεΐνες ως πιθανούς GPCRs με μόνη πληροφορία την αλληλουχία, και στηρίζεται σε ειδικά μοτίβα που περιγράφονται από pHMMs της βάσης Pfam, καθώς και σε ένα HMM το οποίο έχει κατασκευαστεί ειδικά για τους GPCRs και επιτρέπει την πρόγνωση της ακριβούς τους τοπολογίας.Η μέθοδος GprotPRED (http://bioinformatics.biol.uoa.gr/GprotPRED/) σχεδιάστηκε για να εντοπίζει με ταχύτητα και αξιοπιστία G-πρωτεΐνες με μόνη πληροφορία την αλληλουχία τους. Ειδικότερα, με τα ειδικά κατασκευασμένα pHMMs που περιλαμβάνει, μπορεί να εντοπίσει Gα υπομονάδες και μάλιστα να τις ταξινομήσει στις τέσσερις βασικές οικογένειες που συναντώνται στα θηλαστικά (Gs, Gi/o, Gq/11,G12/13), ενώ, με τα δύο y their sequence as input. It is based on specific patterns described by Pfam pHMMs and on a specially designed by our work GPCR specific HMM, which allows the prediction of their topology. GprotPRED (http://bioinformatics.biol.uoa.gr/GprotPRED/) was designed to accurately detect Gproteins with solely their sequence as input. Using the specific pHMMs that were built, it detects Gα proteins, and classifies them in the four basic mammal families (Gs, Gi/o, Gq/11, G12/13). Moreover, with two additional pHMMs for the β and γ subunits, identification of G-proteins is complete. The sheer volume of data on G-coupled protein receptors led to the creation of two new databases: Human-gpDB is available at http://bioinformatics.biol.uoa.gr/human_gpdb/ and contains information regarding human GPCRs, G-proteins, effectors and their interactions. PLHG-DBhttp://bioinformatics.bio.uoa.gr/plhg_db/) includes all peptide ligands of human GPCRs. Specifically, information on the sequence of the peptide ligand, its function, the precursor protein and the interaction with the respective receptor, all accompanied by the respective citations, is presented to the user. We analyzed the structures and amino acid sequences of G-proteins and identified certain surfaces of Gα subunits that may, in many cases, participate in binding both receptors and effectors. The differences displayed in the sequence and structure of these sites may perhaps account for Gα specificity towards their binding partners. Furthermore, the diversity in the electrostatic potential of Gα surfaces, combined with observed electrostatic properties of various effectors and RGS structures, suggests that electrostatic complementarity is, most probably, an important factor in the regulation of effectors by G-proteins, as well as Gα interactions with RGS proteins. Finally, we studied all missense SNPs on human, class A GPCRs and their associations with diseases. A set of 650 human class A GPCRs from UniProtKB/SwissProt and RefSeq databases along with21746 missense SNPs through dbSNP, UniProtKB/SwissProt, SNPdbe and ClinVar was created. Using statistical analysis, a tendency for the SNPs to be more abundant in some domains (cytoplasmic loops), and less abundant in others (transmembrane segments) was observed. Consolidation of the data disclosed a total of 441 SNPs, located in 39 receptors, which were found to have a clinical impact or/and an association with disease. For these receptors, both secondary structure diagrams and 3D models were created and all SNPs were mapped on them. In some of the constructed models, pathogenic SNPs tend to accumulate in certain regions.
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