Amoebae are commonly studied to understand embryogenesis, and the best-characterized amoebozoan species is Dictyostelium discoideum (Dd). Dd has a very simple life cycle with a range of developmental stages, among which we are most interested in the stage of a migrating slug. It has been observed that different sizes of Dd slugs maintain a proportional distribution of prestalk cells and prespore cells: prestalk cells occupy the anterior 20% of the slug, while prespore cells occupy the posterior 80%. However, it remains unknown how the migrating slug forms and preserves this anterior-posterior proportional pattern under so many different dynamics including cell movement, signaling, and cell differentiation. Therefore, we constructed a mathematical model to simulate the cell movement and chemical distribution during slug migration, and we conducted numerical experiments to explore possible factors for this pattern. In particular, we divided the problem of interest into the following three parts to be investigated. (1) differential motion: the ability of prestalk cells to move through all the prespore cells and stay in the anterior region of the slug; (2) signaling: how cells of different types produce, receive, and respond to the signals in the environment; (3) cell differentiation: how prestalk and prespore cells differentiate into each other under the regulation of signaling. We finally combined and balanced these mechanisms appropriately to achieve the desired patterns observed in migrating slugs.

No description available.

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The major evolutionary transition from single-celled to multicellular life is believed to have occurred independently of the main metazoan lineages in the cellular slime moulds, of which Dictyostelium discoideum is the best-studied species. Unusually, in this case multicellular development is a facultative trait and part of an asexual life cycle. It is triggered by starvation and involves aggregation of hitherto independent and possibly unrelated free-living cells. The consequences of multicellularity in D.discoideum are strongly influenced by the environment and meaningful external perturbations are easily carried out. This makes the organism ideally suited to a study of epigenetic factors that regulate development. In an attempt to understand how conserved epigenetic pathways are integrated within the developmental framework, two likely players were chosen for investigation - heat shock protein 90 (Hsp90) and histone deacetylases (HDACs). Hsp90 has been implicated in diverse biological processes such as protein folding, cell cycle control, signal transduction, and morphological evolution. The role of Hsp90 in D.discoideum life cycle was studied using a specific inhibitor, geldanamycin. Inhibition of Hsp90 function in D.discoideum caused a delay in aggregation and an arrest of development at the ‘mound’ stage. A reduction in Hsp90activity in starving cells of D.discoideum resulted in the generation of a range of phenotypes. The study suggests that Hsp90 is required for a specific developmental transition of the social amoeba and is important in generating a reliable outcome of the developmental process. Histone acetylation regulates gene expression and leads to the establishment and maintenance of cellular phenotypes during development of plants and animals. To study the roles of HDACs in D.discoideum, biochemical, pharmacological and genetic approaches were employed. The inhibition of HDAC activity by trichostatin A resulted in histone hyperacetylation and a delay in cell aggregation and differentiation. Cyclic AMP oscillations were normal in starved amoebae treated with trichostatin A but the expression of a subset of cAMP-regulated genes was delayed. Bioinformatic analysis indicated that there are four genes encoding putative HDACs in D.discoideum. One of these four genes, hdaB, was found to be dispensable for growth and development under laboratory conditions; but formed spores with lower efficiency than the wild type in chimeras. The work shows that HDAC activity is important for regulating two aspects of multicellular development: (a) heterochrony, namely the relative timing of developmental events, and (b) modulating the behaviour of single cells in a manner that is sensitive to their social environment.

A hallmark feature of Dictyostelium development is the establishment and maintenance of precise cell-type proportions. In the case of D. discoideum, roughly 20% of the cells that aggregate form the stalk while the remaining 80% form the spores. In order to identify genes involved in cell-type proportioning Jaiswal et al. (2006) carried out random insertional mutagenesis (REMI) of the D. discoideum genome. This led to the identification of a novel gene, which was named trishanku (triA). A knock-out of triA did not show any defects during growth and early development but multiple defects later during development. To understand the reasons for the multiple developmental defects in the absence of triA, I looked at the genomic organization and the pattern of expression of the triA gene. In silico analysis points to the presence of more than one consensus D. discoideum promoter sequence upstream to exons1 and 2, raising the possibility that the triA gene could code for more than one transcript. Northern blot analysis confirms this prediction and provides evidence for the presence of two transcripts: triA1-2-3 (~ 2.9 kb, containing exons 1+2+3) and triA2-3 (~ 2 kb, containing exons 2+3). Both transcripts have exons 2 and 3 in common. In triA- cells, the REMI cassette is inserted in exon 2, which is common to both transcripts; thus, the absence of triA results in the lack of both. The transcripts are absent in vegetative cells but expressed during development. triA2-3 is expressed earlier, by 3h, while triA1-2-3 is expressed later, by 9h, and both remain till the end of development. triA2-3 and triA1-2-3 are differentially regulated by different aspects of the extracellular environment which include mode of development of cells (solid substratum versus shaken suspension), the presence of a high level of extracellular cAMP and formation of stable cell-cell contacts. The expression of triA2-3 and triA1-2-3 in triA- cells, one at a time under a constitutive promoter (Actin15 promoter), suggests that the two transcripts have both specific as well as overlapping functions in the cell. The triA2-3 transcript can specifically restore spore forming efficiency and stalk thickness, while the triA1-2-3 transcript can rescue the stream break up defect. Both the transcripts can rescue the sub-terminal position of the sorus, spore shape and spore viability. To address the question of stream break-up during mid to late aggregation in triA- cells, I have looked at the cell adhesion profile of triA- cells and compared it with the wild type (Ax2). triA- cells show transient disaggregation in buffer and a 2h delay in agglutination in presence of buffer with 10mM EDTA. This aberrant cell adhesion profile seen in triA- cells is in accordance with the expression pattern of genes encoding known cell adhesion molecules. triA- cells also overproduce an extracellular factor which significantly decreases the aggregate size of both Ax2 and triA-. The nature of the extracellular factor overproduced by in triA- cells is currently unknown, but it is not the same as cell-counting factor which is overproduced by smlA null cells. To look at the mis-expression of cell type-specific genes, I have monitored the movement of prestalk cells into the prespore region and vice versa in both Ax2 and triA- slugs. My studies show that there is extensive movement of prestalk cells into the prespore region and of prespore cells into the prestalk region in triA- slugs, which is absent in Ax2 slugs. Also, cells that move into the ‘wrong’ region show a change their cell fate (transdifferentiate) appropriate to the new location; whether transdifferentiation precedes or succeeds cell movement is not yet clear. Transdifferentiation is observed to a certain extent in Ax2 slugs, but only after prolonged migration; triA- slugs show enhanced transdifferentiation even in the absence of migration. To find out the possible reason(s) for the formation of a sub-terminal spore mass in the absence of triA, I have checked whether the defect lies in the ability of the prespore cells to rise up the stalk or in the ability of the upper cup (cells present above the spore mass contributed by a subset of prestalk cells and anterior like-cells) to pull the spore mass to the top. To see which of the two reasons could be responsible for the formation of a sub-terminal spore mass in triA-, I carried out transplantation experiments where the anterior one-fourth region of an Ax2 or triA- slug is grafted to the posterior four-fifth region of a triA- or Ax2 slug and the morphology of the fruiting body is observed. My studies show that the sub-terminal position of the spore mass in triA- is not due to an inability of the prespore cells to rise to the top but to a defect in the upper cup. The upper cup in triA- remains motile but is unable to remain attached to the prespore mass during culmination. It detaches, rises up the stalk and is present at the tip of the stalk. Mixing a minority of triA- cells (20%) with an excess of Ax2 (80%) results in an upper up formed by Ax2 alone. In this situation, the wild type upper cup is able to lift the triA- prespore mass to the top. Thus, the presence of triA (a prespore-specific gene) is essential for the proper functioning of the upper cup cells (which belong to the prestalk class) in order to enable prespore cells to ascend to the top of the stalk.

Η ριβονουκλεάση Ρ (RNase P) είναι ένα ριβονουκλεοπρωτεϊνικό ένζυμο, απολύτως απαραίτητο για την βιωσιμότητα του κυττάρου, καθώς είναι υπεύθυνο για την ωρίμανση του 5΄ άκρου των προδρόμων μορίων tRNA. Δραστικότητα RNase P έχει απομονωθεί από όλους τους οργανισμούς που έχουν μελετηθεί μέχρι σήμερα και από τις τρεις φυλογενετικές περιοχές (Βακτήρια, Αρχαία και Ευκαρυώτες), όπως επίσης και από τα ημιαυτόνομα υποκυτταρικά οργανίδια, μιτοχόνδρια και χλωροπλάστες [Frank και Pace 1998, Xiao et al. 2002]. Το ένζυμο αυτό διαθέτει μια RNA υπομονάδα απαραίτητη για την κατάλυση ενώ ο αριθμός των πρωτεϊνών που συμμετέχουν στο ριβονουκλεοπρωτεϊνικό σύμπλοκο ποικίλλει από μια στα βακτήρια έως και δέκα στην RNase P του ανθρώπου [Frank και Pace 1998, Chamberlain et al. 1998, Jarrous 2002]. Η RNA υπομονάδα από τα Βακτήρια και ορισμένα Αρχαία παρουσιάζει καταλυτική δραστικότητα απουσία πρωτεϊνών in vitro, σε υψηλή ιοντική ισχύ [Guerrier-Takada et al. 1983, Pannucci et al. 1999]. Παρότι μέχρι στιγμής καμία τέτοια ιδιότητα δεν έχει εντοπιστεί σε ευκαρυωτική RNA υπομονάδα, πιστεύεται ότι στην πραγματικότητα πρόκειται για ένα ριβοένζυμο [Frank et al. 2000]. Η RNase P από το Dictyostelium discoideum είναι ένα ριβονουκλεοπρωτεϊνικό σύμπλοκο που αποτελείται από RNA και πρωτεϊνικές υπομονάδες οι οποίες είναι απαραίτητες για την δραστικότητα του ολοενζύμου. Η πυκνότητα επιπολής που υπολογίσθηκε για την RNase P από το D. discoideum είναι πολύ χαμηλή σε σχέση με τα χαρακτηρισμένα ολοένζυμα ευκαρυωτικής προέλευσης και είναι παρόμοια με αυτή ενός πρωτεϊνικού μορίου [Stathopoulos et al. 1995]. Παρότι έχει αποδειχθεί ότι το ολοένζυμο αποτελείται από RNA και πρωτεΐνες, πολύ λίγα είναι γνωστά για την ακριβή σύσταση του ριβονουκλεοπρωτεϊνικού συμπλόκου. Πρόσφατα εντοπίστηκε το γονίδιο της RNA υπομονάδας της RNase P από το D. discoideum μέσω συγκριτικής φυλογενετικής ανάλυσης, μήκους 369 νουκλεοτιδίων [Marquez et al. 2005]. Χρησιμοποιώντας τις πρωτεϊνικές υπομονάδες Rpp20 και Rpp40 της RNase P του ανθρώπου πραγματοποιήθηκε αναζήτηση στη τράπεζα δεδομένων της αλληλούχισης του γενωμικού DNA του D. discoideum. Το αποτέλεσμα της αναζήτησης ήταν η εύρεση δύο ανοιχτών πλαισίων ανάγνωσης (drpp20 και drpp40) που κωδικοποιούν δύο πρωτεΐνες (DRpp20 και DRpp40) οι οποίες παρουσιάζουν σημαντική ομολογία με τις υπομονάδες. Η επαγόμενη πρωτεΐνη DRpp20 έχει προβλεπόμενο μοριακό βάρος 26,4 KD, pI 5,6 και επιδεικνύει σημαντική ομοιότητα με την χαρακτηρισμένη πρωτεϊνική υπομονάδα Rpp20 του ανθρώπου (34% ταυτότητα, 56% ομοιότητα σε μήκος 140 αμινοξέων). Όμοια, η πρωτεΐνη DRpp40 έχει προβλεπόμενο μοριακό βάρος 48,2 KD, pI 5,5 και παρουσιάζει σημαντική ομοιότητα με την πρωτεϊνική υπομονάδα Rpp40 (26% ταυτότητα, 45% ομοιότητα σε μήκος 302 αμινοξέων). Παρά την συνολική ομοιότητα, τα μοριακά βάρη των DRpp20 και DRpp40 διαφέρουν σημαντικά σε σχέση με αυτά των ομόλογων πρωτεϊνών τους. Η DRpp20 διαθέτει μια περιοχή χαμηλής πολυπλοκότητας, πλούσια σε κατάλοιπα θρεονίνης, γλουταμίνης και λυσίνης που πιθανόν να συνεισφέρει στο επιπλέον μοριακό βάρος όπως φαίνεται από την στοίχιση με το Clustal W. Τόσο οι επαναλήψεις τρινουκλεοτιδίων γενωμικών περιοχών όσο και οι περιοχές χαμηλής πολυπλοκότητας σε επίπεδο πρωτεΐνης υπάρχουν σε αφθονία στο D. discoideum [Eichinger et al. 2005] και παραμένει να αποδειχτεί εάν αυτά τα χαρακτηριστικά συνεισφέρουν δομικά ή λειτουργικά στις DRpp. Από βιοπληροφορική ανάλυση προκύπτει ότι καμία από τις υπομονάδες των Αρχαίων ή τις εννέα υπομονάδες της ζύμης δεν παρουσιάζει ομοιότητα με τις DRpp20 και DRpp40. Επιπρόσθετα, με την βοήθεια του Pfam αλλά και των προγραμμάτων που συνδέονται με τον MetaServer εντοπίσαμε στην περιοχή 56-126 αμινοξέα της πρωτεΐνης DRpp20 το δομικό μοτίβο των Alba πρωτεϊνών. Η μελέτη των δύο πρωτεϊνών με βάση τον αλγόριθμο PSORT υποδεικνύει ότι και οι δύο πρωτεΐνες έχουν μεγαλύτερη πιθανότητα για χωροθέτηση στον πυρήνα παρά σε οποιοδήποτε άλλο υποκυτταρικό διαμέρισμα. Στην παρούσα εργασία τα υπό μελέτη γονίδια drpp20 και drpp40 κλωνοποιούνται σε φορέα υπερέκφρασης pET-29 και εισάγονται σε δεκτικά κύτταρα BL21(DE3)pLysS. Οι ανασυνδυασμένες πρωτεΐνες απομονώνονται από το κυτταρικό εκχύλισμα με χρωματογραφία συγγενείας σε στήλη νικελίου. Οι πρωτεΐνες DRpp20 και DRpp40 με την μέθοδο που απομονώνονται παραλαμβάνονται σχεδόν στην φυσική τους μορφή όπως προκύπτει και από τα φάσματα του κυκλικού διχρωϊσμού. Οι πρωτεΐνες αυτές χρησιμοποιούνται για την παραγωγή πολυκλωνικών αντισωμάτων καθώς επίσης και για λειτουργικές μελέτες οι οποίες περιγράφονται παρακάτω. Όπως αποδεικνύεται οι πρωτεΐνες DRpp20 και DRpp40 αποτελούν τμήματα του μακρομοριακού συμπλόκου της RNase P. Πολυκλωνικά αντισώματα έναντι των συγκεκριμένων πρωτεϊνών ανιχνεύουν μία ζώνη που συνεκλούεται με την δραστικότητα του ολοενζύμου σε ανάλυση κατά Western. Επιπρόσθετα, η ισχύς αυτής της αλληλεπίδρασης επιτρέπει την κατακρήμνιση καταλυτικά δραστικού ενζύμου με την χρήση των πολυκλωνικών αντισωματών anti-DRpp20 και anti-DRpp40. Μεταξύ των πρωτεϊνών και της RNA υπομονάδας καθώς επίσης του tRNA υποστρώματος αναμένεται να υπάρχουν αλληλεπιδράσεις RNA πρωτεϊνών. Για το λόγο αυτό ελέγχθηκε η ικανότητα των πρωτεϊνών DRpp20 και DRpp40 να αλληλεπιδρούν με μόρια RNA και ιδιαίτερα με μόρια tRNA. Σε μία σειρά πειραμάτων που πραγματοποιήθηκαν δοκιμάστηκαν μόρια tRNA, ολικό RNA αλλά και πλασμιδιακό DNA χωρίς όμως κάποιο αποτέλεσμα στις συνθήκες που πραγματοποιήθηκε η αντίδραση, παρότι άλλες πρωτεΐνες που φέρουν το μοτίβο των Alba πρωτεϊνών έχουν την ικανότητα να αλληλεπιδρούν με μόρια DNA ή δίκλωνα τμήματα RNA. Τέλος, για τις DRpp20, DRpp40 αλλά και το ολοένζυμο, πραγματοποιήθηκε έλεγχος για δραστικότητα ΑΤΡασης κυρίως εξαιτίας της ομολογίας της πρώτης με την Rpp20 του ανθρώπου που διαθέτει τέτοια ιδιότητα, χωρίς να ανιχνεύεται μέσω βιοπληροφορικής ανάλυσης σημαντική ομολογία με αντίστοιχα ένζυμα. Στις συνθήκες που δοκιμάστηκαν δεν ανιχνεύτηκε δραστικότητα ΑΤΡασης που να σχετίζεται με κάποια από τις δύο πρωτεΐνες ή το ολοένζυμο. Ο απώτερος στόχος μας είναι ο προσδιορισμός της ελάχιστης λειτουργικής δομής καθώς και η χαρτογράφηση των αλληλεπιδράσεων πρωτεΐνης-πρωτεΐνης και RNA-πρωτεΐνης στο ολοένζυμο της RNase P. Η ολοκλήρωση της μελέτης θα συμβάλλει στην κατανόηση του καταλυτικού μηχανισμού και της εξέλιξης της ριβονουκλεάσης Ρ από ένα αρχέγονο ριβοένζυμο σε ένα υψηλά οργανωμένο ριβονουκλεοπρωτεϊνικό σύμπλοκο. / Ribonuclease P (RNase P) is a ubiquitous and essential ribonucleoprotein enzyme that matures the 5´ end of all primary tRNA transcripts. It has been studied from a variety of organisms, representing the three domains of life (Bacteria, Archaea and Eukarya), as well as from the major subcellular organelles, mitochondria and chloroplasts [Frank and Pace 1998, Xiao et al. 2002]. RNase P enzymes contain a similar in size RNA subunit which is absolutely required for catalysis. However, the size and number of protein subunits of the holoenzyme varies significantly, from one small subunit in bacteria to ten subunits in human RNase P [Frank and Pace 1998, Chamberlain et al. 1998, Jarrous 2002]. The RNA subunit from bacteria and some archaea is catalytically active in vitro in high ionic strength and in the absence of the protein fraction of RNase P [Guerrier-Takada et al.1983, Pannucci et al. 1999]. No such activity has been proven yet for eukaryotic RNA subunit but is still considered to be intrinsically a ribozyme [Frank et al. 2000]. Dictyostelium discoideum RNase P holoenzyme is a ribonucleoprotein complex, consisted of RNA and proteins essential for catalytic activity. Considering its buoyant density, D. discoideum RNase P exhibits one of the most proteinaceous idiosyncrasies, among the characterized holoenzymes of eukaryotic origin [Stathopoulos et al. 1995]. Although it has been established that this enzyme contains both RNA and protein components, very little is known on the exact composition of the ribonucleoprotein complex. A recent report identified a putative RNA subunit of D. discoideum RNase P of length of 369 nucleotides through phylogenetic comparative analysis [Marquez et al. 2005]. Genomic analysis of the available data from D. discoideum sequencing projects, revealed among others the existence of two open reading frames (drpp20 and drpp40) encoding two proteins (DRpp20 and DRpp40) that show significant similarity to previously characterized proteins subunits Rpp20 and Rpp40 from human RNase P. The encoded protein DRpp20 has a predicted molecular mass of 26,4 KD, pI 5,6 and exhibits significant similarity to characterized human RNase P protein subunit, Rpp20 (34% identity, 56% similarity at a length of 140 amino acids). Likewise, the protein DRpp40 of a predicted mass of 48,2 KD and pI 5,5, displays significant similarity to its human counterpart, Rpp40 (26% identity, 45% similarity at a length of 302 amino acids). DRpp20 harbors a region of low complexity (rich in threonine residues) which confers to higher MW in comparison with the human homologue. Such regions have not been encountered so far in proteins of this kind in other organisms. Tandem repeats at the genomic and the protein level, are abundant in D. discoideum [Eichinger et al. 2005] and it remains to be proven if these features contribute to the structure and function of DRpp proteins. To the best of our knowledge no homologues of DRpp20 and DRpp40 have been identified in yeast and archaeal RNase P enzymes. Additionally, pattern search of the D. discoideum protein sequences using MetaServer and Pfam prediction tools identified a DRpp20 region (amino acids 56 to 126) that bears similarity to the Alba domain. PSORT analysis of DRpp20 and DRpp40 predicts that these proteins are likely to localise into the nucleus. In this study the putative ORFs were subcloned into pET-29 expression vector and the recombinant vectors were used for the transformation of BL21(DE3)pLysS. The recombinant polypeptides were purified from the cell extract using Ni2+-nitriloacetic acid agarose column. The purified proteins are isolated in their native form as supported by circular dichroism analysis of the preparations. These preparations were used for the production of polyclonal antibodies as well as functional studies as described below. DRpp20 and DRpp40 are functionally associated with the RNase P ribonucleoprotein catalytic complex. Using anti-DRpp20 and anti-DRpp40 polyclonal antibodies we ascertained the concurrence of DRpp20 and DRpp40 with purified RNase P activity after standard purification schemes. Moreover, the nature of this association permits the precipitation of RNase P activity through antigen-antibody interaction using the same antibodies. RNA-proteins interactions between the protein subunits, the RNA moiety and/or the RNA substrate are expected in the holoenzyme complex, and therefore the ability of DRpp40 and DRpp20 to bind to RNA molecules was investigated. In a series of experiments using a variety of binding partners (plasmids, tRNAs and total RNA), we did not detect any DNA or RNA binding properties for DRpp20 and DRpp40, although other proteins that contain the Alba core interact with DNA or double stranded RNA regions. Although neither DRpp20 nor DRpp40 harbours an ATPase domain, we tested DRpp40 and DRpp20 for ATPase activity mostly due to the latter homology with human Rpp20, which was shown to have ATPase activity. We could not detect any ATPase activity associated with aforementioned proteins or holoenzyme. Our future prospects are the determination of minimal catalytic core and the complete mapping of all protein-protein and RNA-protein interactions within RNase P holoenzyme. The completion of this project will contribute in a decisive manner to the understanding of both the catalytic mechanism and the evolution of RNase P from a primordial ribozyme to a highly organized ribonucleoprotein complex.

No description available.

Molecular chaperones are proteins that interact with and aid in stabilization and activation of other proteins. Chaperones help proteins attain their three dimensional conformation, without forming a part of the final structure. Many of the chaperones are stress proteins known as Heat shock proteins (Hsps). Their expression is upregulated in response to various kinds of stress such as heat stress, oxidative stress etc., which threaten the protein homeostasis, by structurally destabilizing cellular proteins, and increasing the concentration of aggregation-prone folding intermediates. The Hsps are classified according to their molecular weight into Hsp40, Hsp60, Hsp70, Hsp90, Hsp100, and the small Hsp families. Some of them are constitutively expressed and play a fundamental role in de novo protein folding. They further aid in proteome maintenance by assisting in oligomeric assembly, protein trafficking, refolding of stress denatured protein, preventing protein aggregation and protein degradation. Heat shock protein 90 (Hsp90) are one of the important representatives of this class of proteins. Hsp90 are highly conserved class of molecular chaperones. They are found in bacteria, eukaryotes, but not in archaea. In contrast to the eukaryotes which require a functional cytoplasmic Hsp90 for viability, the bacterial counterpart (HtpG) is typically nonessential. Hsp90 is an ATP dependent chaperone. Hsp90 form dimers, with each protomer consisting of three functional domains: N- terminal, ATP binding domain, Middle domain and C-terminal domain. Hsp90 is a dynamic protein, and undergoes an elaborate conformational cycle during its ATPase cycle, which is essential for its chaperoning activity. The Hsp90 chaperone cycle is regulated by interaction with diverse cochaperones. Hsp90 interacts with specific set of substrate proteins. Many of these substrate proteins function at the heart of several cellular processes like signalling, cell cycle, apoptosis. Studies from protozoans like Leishmania, Plasmodium, Trypanosoma etc. have also implicated the role of Hsp90 in their growth and stage transitions. Thus, selective inhibition of Hsp90 has been explored as an intervention strategy against important human diseases such as cancer, malaria and other protozoan diseases. The ATP binding N-terminal domain (NTD), has been explored as the target domain for inhibition of Hsp90 using competitive inhibitors of ATP. Several chemical classes of Hsp90 inhibitors are known, including ansamycins, macrolides, purines, pyrazoles, and coumarin antibiotics. However, many inhibitors are observed to be toxic, less soluble and unstable. Hence, there is a requirement for new approach to design inhibitors which are more soluble and less toxic and serve as effective therapeutic drugs.inhibitors are observed to be toxic, less soluble and unstable. Hence, there is a requirement for new approach to design inhibitors which are more soluble and less toxic and serve as effective therapeutic drugs. The work presented in this thesis mainly concerns with the structural studies and biochemical and biophysical characterization of Hsp90 from two different sources viz. Dictyostelium discoideum, a cellular slime mould and a plant source Oryza sativa (rice). The structural analyses of these two proteins have been carried out by X-ray crystallography. Though yeast has been explored extensively as a model system to understand the different roles of Hsp90, it lacks the various signalling pathways essential for growth and development present in case of higher eukaryotes. D. discoideum has been employed as a model system to understand multicellular development, which occurs in response to starvation induced stress. D. discoideum has the advantages due to its ease of manipulation. The organism's genome also shows many signalling pathway for growth and differentiation that are conserved between D. discoideum and mammals. With this motivation, we have studied several structural aspects of the cytosolic isoform of Hsp90 from D. discoideum called HspD. HspD was also observed to play a role in the multicellular development of D. discoideum. It has been demonstrated that the treatment of D. discoideum with inhibitors like Geldanamycin or Radicicol causes an arrest in the multicellular development at the mound stage, and the few which escaped this arrest gave rise to abnormal fruiting bodies. A subset of the proteins involved in this mound arrest phenotype, were observed to have homologs in humans, which are clients of Hsp90. Therefore, a structural perspective of HspD can aid in better understanding of the role of this protein in the organism, as well as, elucidate any structural differences observed as compared to other species, which may have an impact on its activity. Studies on the physiological role of Hsp90 in plants began much later as compared to fungi and humans. In plants Hsp90 are involved in various abiotic stress responses. In addition, their roles have also been implicated in plant growth and development, innate immune response and buffering genetic variations. However, the molecular mechanisms of these various actions are not clearly understood. Also, the structural aspects of plant Hsp90 are yet to be explored. The structure of the NTD of Hsp90 from barley is the only one available from a plant source till now. We have initiated the studies on rice Hsp90 with the objective to understand the mechanism of Hsp90 in plants, which may aid in improving stress tolerance in plants. The thesis has been divided into five chapters. The first chapter introduces the various aspects of Hsp90 protein. The chapter starts with a general overview of concept of molecular chaperones and describes briefly the different classes of molecular chaperones. This is followed by a detailed description of different aspects of Hsp90 with main emphasis on the structure and its conformational flexibility. The chapter describes the association of Hsp90 with other accessory proteins like cochaperones and its interaction with its substrate proteins and explains the functional significance of Hsp90 as a drug target and the need for the development of new class of inhibitors, followed by the significance of the study of Hsp90 in the two model systems (D. discoideum and rice) chosen to be studied. The second chapter gives a brief overview of the principles behind the different experimental methods employed during the course of this research, which includes the tools of X-ray crystallography and other biochemical and biophysical techniques employed for the characterization of the protein. Chapter 3 describes the crystal structure of NTD of Hsp90 from D. discoideum. The structure of NTD was solved in two different native (ligand-free) forms viz. monoclinic and hexagonal. The two forms differed in local structural rearrangement of a segment of NTD known as the lid region. The lid region in the hexagonal form showed a shift in its position as compared to the other solved structures of NTD. The structure of NTD was also solved in complex with various ligands which include ADP, substrate analogs and an inhibitor molecule. A comparison of all the structures showed that the overall structure is well-conserved. One of the crystal structures of NTD showed a heptapeptide (part of the vector) bound at the active site. The peptide was observed to make several complementary interactions with the residues of the ATP binding pocket and retain several interactions which the nucleotide makes with the NTD. The NTD showed subtle conformational differences when compared with the NTD of Hsp90 from yeast. Chapter 4 details the structural and functional characteristics of full length Hsp90 protein from D. discoideum. Due to the large size and flexibility, the full length protein did not crystallize in spite of several attempts. Hence, HspD was studied using different solution studies like Small Angle X-ray Scattering (SAXS) and Dynamic Light Scattering (DLS). Both the studies showed the presence of higher oligomers. The SAXS data showed the presence of tetramers and hexamers while, the addition of the ligand shifts the protein from a dimer to a higher oligomer as observed from DLS studies. The chapter also describes the study of interaction of HspD with a cochaperone protein p23. The interactions were studied using ITC, which showed a strong binding. The ATPase activity was also evaluated in the presence of increasing concentrations of p23, which was observed to decline with increasing concentrations of p23. In chapter 5, we describe the biochemical characterization of Hsp90 from Oryza sativa (rice) and the crystallographic analysis of its NTD. Binding of the rice Hsp90 to ATP and an inhibitor were studied by fluorescence. The ATPase activity of rice Hsp90 was checked by radioactive assay and the protein was observed to be active. The NTD of rice Hsp90 crystallized as a monomer in complex with a substrate analog AMPPCP and the structure was determined.

Durant ce travail, deux approches ont été explorées. <br /> Dans la première, j'ai quantifié le dégonflement osmotique de liposomes remplis d'un gel d'agarose. La fabrication de tels systèmes reconstitués vise à permettre de mimer le comportement de cellules soumises aux mêmes contraintes. En particulier, j'ai observé que ces liposomes fonctionnalisés acquièrent des morphologies crénelées lors de leur dégonflement pour une concentration du gel comprise entre 0.07 et 0.18 % en masse. Ces formes originales ressemblent à celles d'échinocytes parfois prises par les globules rouges. Le gel est responsable de l'apparition de ces formes, ne modifie pas les cinétiques de dégonflement mais sa pression élastique arrête précocement le dégonflement comparativement aux liposomes aqueux, mettant en évidence un phénomène de rétention d'eau.<br /> Dans la deuxième approche, j'ai étudié l'effet de contraintes hydrodynamiques sur des amibes Dictyostelium adhérentes à un substrat et ai quantifié la réorganisation mécanosensible du cytosquelette de ces cellules vivantes. Pour obtenir les cinétiques de relocalisation de protéines majeures du cytosquelette en réponse aux forces d'un flux, j'ai marqué l'actine et la myosine-II avec des protéines fluorescentes et ai fabriqué une chambre à flux permettant de changer rapidement la direction du flux. J'ai montré que les cellules s'orientent contre les forces du flux et se réorientent contre en inversant leur polarité après une inversion du flux : d'abord l'actine dépolymérise puis des protrusions sont émises contre les nouvelles forces mécaniques, et 15 sec plus tard, l'arrière rétracte en utilisant la myo-II. De plus, la contractilité du système actine-myosine n'est pas nécessaire pour sentir les forces. Des expériences similaires en inversant la direction d'un gradient de chimioattractants montrent que ce processus de réorientation cellulaire n'est pas spécifique d'expériences sous flux. Ce travail met en évidence l'existence d'un signal inhibiteur rapide menant à la dépolymérisation de l'actine, signal qui n'est pas pris en compte dans les modèles actuels expliquant la réponse chimiotactique. Enfin, les outils de visualisation que j'ai développés permettent d'étudier le rôle de protéines et de structures cellulaires dans la mécanotransduction.

Functional interaction of proteins is a broad term encompassing many different types of associations that are observed amongst proteins. It includes direct non-covalent interactions where the interacting proteins physically associate using an interface. There are also many protein-protein interactions where the proteins concerned are not involved in direct physical interactions but affect each other’s functions. Central focus of this thesis is to understand the various aspects of functionally interacting proteins. Chapter 1 of this thesis provides an introduction to functional interactions between proteins and discusses the key developments available in the literature. This chapter discusses the different types of functional associations observed commonly between proteins. Various approaches developed over time to elucidate such interactions have also been discussed. This chapter highlights how functional interactions between proteins have been helpful in understanding different cellular processes such as organization of metabolic pathways. The chapter emphasizes the importance of functional interactions between proteins, providing a motivation for development of methods with enhanced accuracy and sensitivity for the prediction of functional interactions. In this thesis, domain families which are found to co-exist in multidomain proteins have been used to understand and subsequently predict functional associations amongst proteins. Domains in proteins typically serve as modules associated with specific functions. There exist proteins with a single domain which describes the entire function of a protein, while there also exist proteins containing multiple domains, where various domains in unison describe the complete function of the multidomain protein. Therefore, by virtue of “guilt by association” domain families found together in multidomain proteins are functionally linked. This forms the basic premise for understanding functional association amongst proteins and is explained in great detail in the Introduction chapter. Using domain families which co-occur in multidomain proteins as the basis for functional association has many merits. First, as stated before, constituent domain families act as effective descriptors of function(s) of proteins. For example, members of SH3 domain family mediate protein-protein interactions by binding to regions with polyproline conformation irrespective of the multidomain protein in which it occurs. Thus, studies of domain families co-existing in multidomain proteins act as an accurate resource of functional associations between proteins. Also, assignment of domains to a protein relies on homology detection which has achieved a high level of reliability, thus, resulting in reasonably accurate prediction of functions. Such approaches enable exhaustive coverage of many diverse proteins including many multidomain proteins leading to detection of large numbers of functional associations between domains of multidomain proteins. Given the advantages attributed to functionally linked domain families in further understanding of functional associations, it is imperative to exhaustively enumerate all possible pairs of functionally linked domain families in multidomain proteins and study their various properties. This aspect is covered in the second chapter of the thesis. In the second chapter, analysis of domain families which co-occur in multidomain proteins, termed as 'tethered domain families', has been reported. For this analysis, a large dataset of multidomain proteins was considered from a diverse set of fully sequenced genomes from many eukaryotic and prokaryotic organisms. In every multidomain protein, all possible pairs of unique domain family pairs have been considered and they are assumed to be under the same functional/evolutionary constraint. Thus, from the entire dataset of multidomain proteins, all possible pairs of tethered domain families are obtained. For a given domain family, the number of other uniquely tethered families is referred to as the tethering number of a domain family. Therefore, tethering number of a domain family is an indicator of the diverse functional contexts in which a particular domain family is involved. Further analysis was carried out to understand various other attributes of domain families and its relation to tethering number. The results are summarized in the following points: 1) Distribution of tethering numbers of domain families in the entire dataset is found to be highly heterogeneous. Nearly 88% of domain families (10783 out of 12249 domain families) have tethering number of 10 or less and only 78 domain families show more than 100 unique associations. Further analysis reveals bias in functions of families showing high and low tethering numbers. The domain families with high tethering numbers are involved in processes such as signaling and protein-protein interactions. The domain families with low tethering numbers are often found to be involved in metabolic processes. 2) Differences are also observed in the type of organisms containing the domain families and their tethering numbers. Typically, domain families with high tethering numbers are ubiquitously found across almost all the kingdoms of life. In contrast, most of the domain families exclusively found in a kingdom have low tethering numbers. Furthermore, for the ubiquitously occurring domain families with high tethering numbers, the number of associations made and the type of associations are not strictly conserved across the kingdoms. Thus, the tethering preferences of such domain families vary across the kingdoms depending on their function. For instance, the protein kinase domain family which is a key regulator of signaling processes in eukaryotes, has a high tethering number in eukaryotes (270), and low tethering number in prokaryotes (96). 3) Tethering number of domain families is found to be correlated with the number of members (population) comprising a family. A Pearson correlation coefficient of 0.78 at a p-value ≤0.001 is obtained for the correlation between tethering number of domain families and their population. 4) Tethering numbers of domain families are also found to be well correlated with sequence and functional diversity within families. Thus, domain families with high tethering numbers comprise of members showing diversity in both sequence and functions. Thus, the work presented in second chapter provides a framework for understanding the tethering preferences of domain families. The use of tethered domain families to identify functional association amongst proteins is the central theme of third and fourth chapters of this thesis. The use of tethered domain families for the prediction of functionally interacting proteins originates from the initial idea of “Rosetta stone” approach, which was proposed by Ouzounis and coworkers and Eisenberg and coworkers in 1999. Rosetta stone approach demonstrated the use of fused genes in predicting functional interaction. It stems from the observation that in many organisms, genes corresponding to proteins acting in a metabolic pathway are found fused in another organism. Thus, enumeration of 'fused genes' in a template database could provide a good basis for prediction of functionally interacting proteins in target organisms in which the homologous genes are not found to be fused. The method has been shown, by others, to work quite effectively in prokaryotes, especially in the identification of interactions between metabolic proteins. Chapter 3 of this thesis explores the idea of “Rosetta stones” at the level of domain families, by considering tethered domain families as analogs to the fused genes. In this analysis, tethered domain families derived from multidomain proteins comprises the template dataset. If members of two domain families occurring in a multidomain protein are found to occur independently in two different proteins in the target organism then an interaction is predicted between these two proteins (collection of such predicted interactions is henceforth referred as TEDIP database, Tethered Domain-based Interaction Prediction). During this analysis, care is taken such that none of the proteins in the template dataset belongs to the target organisms. The entire analysis has been conducted on 6 model organisms which act as the target dataset where functional interactions between proteins are predicted. The effectiveness of tethered domain families in functional interaction prediction is compared with two other datasets 1) all experimentally known interactions and 2) interactions predicted on the basis of their homology with interacting domain families with known structure. Subsequently, an attempt has been made to answer these questions: 1) how effective is the information on tethered domain families in predicting functional linkages amongst proteins operating in pathways in eukaryotic organisms? 2) what is the false positive rate of the predictions? The above mentioned datasets show very little overlap in the coverage of functional interactions. This is largely attributed to insufficient sampling and inherent bias existing in each of the methods. The TEDIP datasets in the six organisms led to an average three-fold more functional interaction predictions in cellular pathways than the other two datasets. Nearly 90% of the predicted interactions derived from tethered domain families are amongst proteins across different pathways. In yeast, more than 60% of such interactions were found to be overlapping with a recent large scale genetic interaction screen based on synthetic lethality especially performed for metabolic proteins, thus establishing the effectiveness of this approach in understanding pathway crosstalk. Along with efficacy in identifying functional interactions, an assessment based on co-localization, co-expression and overall functional similarity based on Gene Ontology (GO) terms was carried out. It was found that the TEDIP predictions and experimentally found interactions show poor correspondence with co-expression and co-localization data (10% and 20% respectively for the two methods). Additionally, it was found that functional similarity between predicted interacting proteins in TEDIP dataset is low (5%) and is comparable to experimentally known interactions that shows 10% similarity in functions based on a scoring function for GO term similarity. From Chapter 3, it was concluded that the use of tethered domain families is effective in exhaustive enumeration of functionally associated proteins. However, the low co-expression and functional similarity measures are a cause for concern. On the one hand, co-expression and GO functional similarity have been found to be weak predictors of functional interactions, explaining the low values obtained for both predictions in the TEDIP datasets and experimentally known interactions. On the other hand, the poorer values shown for predictions in the TEDIP datasets suggest that further improvement in prediction accuracy is possible. Chapter 4 explores the use of machine learning in improving the accuracy of functional interaction prediction based on TEDIP dataset. In Chapter 4, two distinct machine learning approaches have been employed on a training dataset derived exclusively from yeast. Since the objective of the work is to improve the accuracy of prediction of functional interactions, the GO based functional similarity measures have been used to define positive and negative datasets. Thus, in the training dataset, positive interactions comprises of protein pairs which show high GO similarity in functions as defined in chapter 3 and 10% of this data overlaps with experimentally known interactions, while the negative dataset consists of protein pairs with no or insignificant similarity in their functions and additionally do not show similarity to any experimentally known interactions. Two machine learning approaches, namely Support vector machine (SVM) and Random forest, have been used on this training dataset. Use of two distinct approaches helps in addressing the weakness, if any, of these methods. Fourteen carefully chosen features have been utilized during the training process to aid in the process of distinguishing potentially correctly predicted interactions from incorrect predictions. Out of 14 features, some of the features chosen for the analysis are involved in quantifying the extent of similarity between the template proteins containing the fused domain families and the target protein pairs predicted to interact. The analysis also incorporates graph theory based parameters which are derived from a domain family based graph. In such a graph, each of the domain families which are involved in forming multidomain proteins represents the nodes and an edge is constructed between domain families which are found to co-exist in at least one multidomain protein. Graph theory based parameters such as clustering coefficient, degree and topological overlap have been employed. These are useful in down weighting appropriately the domain family pairs showing large number of associations which are expected to be promiscuous in their functions. These features also enable in identifying domain family pairs which are functionally related. Apart from the above mentioned features, coevolution and phylogenetic profiling of tethered domain families is also utilized to identify functionally related domain family pairs. Utilizing all these features in training, the machine learning approach yielded an accuracy of 94% using SVM and 92% using Random forest against the training data. Furthermore, the importance of using all these features has been addressed by performing principle component analysis, training both SVM and Random forest by removing one feature at a time and by quantifying the sensitivity by using only one feature. All of these suggest that the features used provide non-redundant information and contributed significantly to the classification. The models so generated were finally used on all the predicted functional interactions after the removal of the training dataset in yeast. The true positives observed were 56% using SVM and 63% using Random forest with around 80% of the interactions common between the two methods. Further analysis has been carried out on these interactions by first imparting a confidence score to these interactions using support vector regression that provides a probabilistic measure for SVM classification. Based on a cutoff of 0.5, 62455 interactions in total were termed as high confidence interactions. Further analysis was carried out for the high confidence interactions. Out of these, in 2855 interactions, both the proteins predicted to interact could be associated with a pathway in KEGG database. In-depth case studies have been performed on this dataset of 2855 interactions. Literature mining suggested that many known cross-pathway interactions such as between TCA and glycolysis are captured as high confidence interactions using TEDIP dataset. A few other case studies of high confidence interactions with supporting literature evidence are also presented in the chapter. These predictions could further aid in experimental characterization of pathway cross-talk between important metabolic and signaling pathways. So far, the thesis discussed analyses involving functional interactions and their prediction. In the subsequent chapters, analyses pertaining to two different types of functional interactions are discussed. Chapters 5 and 6 involve analyses incorporating metabolic proteins in diverse pathways in the pathogenic organism Plasmodium falciparum. Chapter 5 attempts to improve the coverage of the repertoire of metabolic proteins in P.falciparum while in Chapter 6 interactions and pathways prevalent in different stages in the life cycle of the parasite are deciphered and discussed. Apart from functionally interacting proteins in metabolic pathways, physically and transiently interacting proteins have been analyzed and discussed in Chapters 7 and 8. In Chapter 5, metabolic proteins participating in pathways in Plasmodium falciparum have been analyzed. P.falciparum is the causative agent of malaria, a disease which affects large populations in the subtropical regions. P.falciparum genome is atypical and is rich in Adenine/Thymine pairs, and there is presence of large stretches of amino acid repeats encoded in protein coding regions. Various sequence-related features of P.falciparum proteins when compared with those of other organisms show extensive divergence. All of these have made reliable function prediction, by homology to other proteins with known functions, daunting. Like other proteins in P.falciparum, metabolic proteins have also diverged significantly from their functional counterparts in model eukaryotes such as yeast. Metabolic pathways play an important role in the survival of the organism and hence are amenable towards the identification of proteins susceptible to drugs, thereby combating pathogenesis. Chapter 5 of the thesis aims at furthering knowledge pertaining to metabolic proteins by first quantifying the extent of divergence observed in the already characterized metabolic proteins. This knowledge is further used in identification of potential metabolic proteins which are not identified as proteins involved in metabolic pathways by other annotation efforts undertaken for P.falciparum. In the first part of the chapter, the extent of divergence in the sequences of metabolic proteins in P.falciparum has been determined by comparing the P.falciparum proteins with their functional counterparts from 34 completely sequenced unicellular eukaryotic organisms. Comparison of domain architectures between the P.falciparum proteins with their functional counterparts reveals that in nearly 54% of metabolic pathways, proteins show nearly the same domain architecture as the other functional counterparts. Inversion, deletion and duplication of domains are observed in rest of the proteins. Further analysis reveals that P.falciparum proteins are longer than their functional counterparts. It was also observed in nearly 15% of the cases, the domains are characterized by the presence of large non-conserved or plasmodium genus specific inserts within the domain assigned regions. There is also prevalence of unassigned regions in the N- and C- terminal regions in P.falciparum proteins when compared with their functional counterparts. Finally, it was also observed that metabolic proteins of P.falciparum show significantly low sequence similarity when compared with other functional counterparts. From this analysis, it can be clearly seen that metabolic proteins of P.falciparum have significantly diverged from such proteins in other organisms, thus making function prediction by homology very difficult. There are several steps in metabolic pathways in P.falciparum which are expected to be active based on experimental analysis. However, some of these proteins with expected functions have not been identified so far. One of the reasons for this apparent incompleteness is the high divergence observed in the metabolic proteins of P. falciparum. To overcome this limitation, in the second part of the chapter, a sensitive approach based on domain family assignment (MulPSSM), developed in-house, has been used to identify proteins which are potentially involved in metabolic pathways. The approach is based on reverse PSI–BLAST, where multiple sequence profiles for each family are used to search against sequence databases. This approach has been shown to be better or at-par with other remote homology detection procedures. Using this approach, 15 P. falciparum proteins have been identified which can potentially function as metabolic proteins and were not characterized in P.falciparum so far. All the proteins identified by the approach show low sequence similarity to other well characterized proteins and contain significant fractions of unassigned regions thus, making function recognition non-trivial. Supporting literature and other data is provided to demonstrate the robustness of the homology-based annotation of the identified pathway proteins. Chapter 6 is an analysis of the dynamic changes occurring in the metabolic network of P.falciparum during its life cycle. In this chapter, two aspects of P. falciparum metabolic proteins have been integrated and analyzed. First, the dataset of protein-protein interactions derived from experimental studies and second, the datasets of microarray analysis providing information on stage specific expression of P. falciparum genes corresponding to the metabolic proteins. As a first step, protein-protein interaction information for the metabolic proteins was gathered. A total of 810 interactions have been obtained, where one or both proteins are involved in a pathway. Subsequently, these interactions were compared with 14070 interactions involving metabolic proteins from free-living and non-pathogenic unicellular eukaryote yeast. Comparison across the two organisms shows wide discrepancy in the number of proteins involved in interactions and also the pathways in which they participate. Out of the 810 interactions in P.falciparum, 173 are found uniquely in plasmodium where both or one of the protein have no identifiable homolog in yeast. Insufficient sampling of interactions made by proteins in P.falciparum in comparison to yeast, is one of the reasons for the observed discrepancy. However, the differences due to the parasitic lifestyle of P.falciparum could also be a potential reason. Further analysis of the protein-protein interactions by the metabolic proteins revealed that a large fraction of interactions are made between a metabolic protein and a non-metabolic protein. For instance, interaction observed between glycolytic protein phospoglycerate kinase with MAP kinase. This trend is observed in both plasmodium and yeast where 65% and 77% of the interactions, respectively, involve proteins not directly participating in metabolic pathways. Further, interactions between proteins belonging to different pathways and lastly, interactions between proteins in the same pathway are uncovered. All of these interactions depict the different modes by which metabolic pathways are regulated through protein-protein interactions. Another aspect explored in this analysis is the stage specific expression of genes encoding these metabolic proteins. The analysis is especially relevant in the parasite because its entire life cycle is divided into seven distinct stages. Upon integrating the protein-protein interactions with the gene expression data, it became apparent that the trophozoite, schizont and gametocyte stages show large fractions of co-expressed genes encoding proteins involved in protein-protein interactions within metabolic pathways. The high preponderance of co-expressed genes encoding for interacting protein pairs in these stages is also consistent with metabolic requirement of plasmodium in the various stages. Glycolytic pathway is central to energy production in the parasite and is discussed at length in this chapter. Members of this pathway are involved in interactions with other glycolytic proteins (9 such interactions), they also interact with proteins involved in other pathways (30 interactions) and with proteins not involved directly in any metabolic pathway (75 interactions). Nearly 70% of the interactions made by the glycolytic proteins are encoded by genes found to be co-expressed across the various stages. Integration of gene expression data along with protein-protein interaction information for metabolic pathways such as the glycolytic pathway thus, highlights the complex mode of regulation underlying this pathway. The analysis carried out in this chapter emphasizes on the intricacies involved in the regulation of metabolic proteins in P.falciparum. Chapter 7 describes an in-depth analysis carried out to understand the basis for interaction specificity between small monomeric GTPases and their regulators, the Guanine nucleotide Exchange Factors (GEFs). Monomeric GTPases are involved in binding to guanine nucleotide. These proteins can bind to both GTP and GDP. However, transition from GDP bound to GTP bound form occurs with large conformational changes and requires binding of the GEFs. The conformational changes that arise due to the nucleotide exchange are required for the GTPases to bind to its various effectors. For the analysis carried out in Chapter 7, GTPases belonging to the Ras superfamily have been considered. The superfamily is further subdivided into 5 distinct families based on their functions. The 5 families are Ras, Ran, Rab, Arf and Rho. Members belonging to each of these families are involved in a wide array of cellular processes such as signaling and cytoskeletal remodeling. Members of each of these GTPase families bind to structurally distinct GEFs, and in some cases, multiple GEFs are involved in nucleotide exchange within a family. It is intriguing therefore, to understand how GTPases belonging to the same structural family maintain specificity across the highly dissimilar GEFs and this forms the main objective of this analysis. So far, 13 distinct complexes between GTPases and their cognate GEFs have been solved using X-ray crystallography. This set of structural complexes forms the starting point of the analysis. As a first step, pairwise structural comparison of the interfaces has made between various pairs of complex structures. Based on these comparisons, it is apparent that most of the interfaces in the GTPase and GEF complexes comprise of residue positions which are topologically not equivalent suggesting different modes of binding across these complexes. Further analysis was carried out to probe the extent of specificity underlying these complexes. This is achieved by determining interface residues which are found to be conserved in a family specific manner. Such residue positions have been obtained by using a statistically robust algorithm Contrast Hierarchical Alignment and Interaction Network (CHAIN) that extracts sequence patterns most distinguishing two sets of homologous sequences. The analysis indicated the presence of family specific residues at the GTPase and GEF interface. Such residues could be implicated in maintaining the specific interactions between the GTPases and the GEFs. The robustness in the specificity of the interactions was further interrogated by providing an energetic basis to the specificity in the interactions mediated by the cognate GTPases and the GEFs and also understanding how crosstalk is prevented across the non-cognate complexes. For each of the 13 cognate complexes, empirical interaction energies have been estimated using FoldX. The interaction energy is compared to non-cognate complexes which are obtained by swapping the interface residues of the cognate GTPase with the non-cognate GTPase residues. For most of the complexes, it was observed that the interaction energies for the cognate complexes are much lower than the non-cognate complexes. Energy values across the non-cognate complexes are usually indicative of reduced stability, thereby precluding such interactions from occurring. Such large energy differences between cognate and non-cognate interactions arise due to drastic substitutions at the interface patch due to difference in the charge or other stereochemical aspects of the amino acids. Both evolutionary and energy based analysis indicates the presence and importance of few family specific residues in the cognate complexes and also the presence of unfavorable residues in the non-cognate complexes thus preventing crosstalk. However, apart from changes at the interfaces, many positions outside the interface also undergo changes across the various homologs within the same family/subfamily of GTPase. Coevolutionary analysis of GTPase and GEFs from multiple eukaryotic organisms has been carried out in these complexes and it was observed that most of the coevolving positions are not found at the interface. Many of these residue positions are near the active site or near the interface. Identification of such coevolving positions, where residue variations in the GTPase are strongly coupled to the GEF, may provide initial clues to the possible allosteric path adopted in connecting the binding of GEF to the vast structural changes observed during GTP exchange in GTPases. Thus, the analysis provides a comprehensive framework to understand how interaction specificity has evolved between the GTPase and GEF complexes. Chapter 8 discusses another example of transient protein-protein interaction observed between proteins implicated in signaling process in Dictyostelium discoideum. The work reported in this chapter was carried out in collaboration with Prof. Nanjundaiah and coworkers from Molecular Reproduction and Developmental Genetics department, Indian Institute of Science. All the experimental analyses mentioned in this chapter were carried out by Prof. Nanjundaiah and coworkers and the author carried out all the computational analysis. Experimental analysis indicated the presence of a ribosomal protein S4 in D. discoideum which mediates interactions with CDC24 and CDC42. The protein is speculated to be a functional analog of yeast scaffolding protein Bem1. However, the exact structural and sequence features of the protein which can accommodate its non-ribosomal function as a scaffold by mediating protein-protein interactions are not clearly understood. With the aid of structural modeling, a 3-D structure was generated for the C-terminal regions of D. discoideum protein S4. The modeled structure, as in the template used for modelling, resembled the fold of SH3 domain which has been shown to be involved in protein-protein interactions. Structural and sequence analyses were carried out to evaluate the potential mode by which interactions could be mediated by this protein. The hypothesis generated was further corroborated by experimental analysis. Thus, both experimental and computational analysis provide evidence for the functional role of the ribosomal protein S4 from Dictyostelium discoideum as a scaffold. Chapter 9 summarizes the conclusions reached in various chapters of the thesis. The thesis embodies analyses probing various aspects of functional interactions between proteins. A frame work has been provided to elucidate functional interactions using tethered domain families in multidomain proteins. Further, the role of these functional interactions have been explored in different scenarios by exhaustively analyzing metabolic proteins and their regulation in pathogenic organism Plasmodium falciparum and by also analyzing two distinct types of transient protein-protein interactions.