Computational approaches to predict protein functional families and functional sites.

Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features

A növényi RHO (ROP) GTP-áz kapcsolt receptor-szerű citoplazmatikus kinázok aktiválását befolyásoló aminosav motívumok azonosítása

The Rho-type GTPases have central roles in cellular processes associated with cytoskeletal dynamics (e.g. cell movement, cell division, cell shape, and cell polarity). These proteins operate as molecular switches: they activate signal transduction pathways when they are in GTP-bound conformation, but their signalling activity cease when they are GDP-bound. If the Rho GTPase is in the GTP-bound form, it can further activate a diverse set of downstream signalling effector proteins. There are some upstream regulator proteins which regulate the activity of ROPs. One of these proteins are the guanine nucleotide exchange factor or GEF proteins which catalyse the GDP to GTP exchange activating ROPs. In contrast, the GAPs (GTPase accelerator proteins) inactivate ROPs via the promotion of GTP hydrolisis, while the GDIs (guanine nucleotide dissociation inhibitors) stabilize the inactive state of ROPs. Plants has a specific group of Rho-type GTPases, the „Rho of plants” (ROP) family. Our knowledge about the signalling pathways associated with ROPs is yet incomplete. ROPs differ from other Rho-type GTPases in the regions which are responsible for effector binding, suggesting that ROP GTPases have specific effectors. Indeed, plants lack the Rho GTPase-activated PAK kinases, which are very important mediators of Rho GTPase signalling in yeast as well as in animals. Therefore our question was: are there any ROP GTPase-activated kinases, which may have PAK-like functions in plants? Due to a yeast two-hybrid screening approach two ROP-interacting kinases could be identified. These kinases interacted with the GTP- but not with the GDP-bound ROP GTPase form what is typical for ROP GTPase effectors. Furthermore, the in vitro activity of these kinases was dependent on the presence of GTP-bound ROP GTPase. These ROP GTPase-activated kinases belong to the subfamily VI of receptor-like cytoplasmic kinases (RLCKs) of Arabidopsis. They have a receptor kinase-like catalytic domain, but they don’t have extracellular or transmembrane regions and that’s why they can found in the cytoplasm. Based on their primary structure, the 14 Arabidopsis RLCK VI kinases can be classified into two groups (A and B). Only the members of group A have ROP GTPase-binding ability, but it was not observed in the case of group B kinases, their activity is ROP GTPase independent

The evolution of protein kinase specificity

All research conducted at EMBL-EBI under the supervision of Dr. Pedro Beltrao. Work on the PhD project was paused temporarily in the Spring of 2017 for me to undertake a 3-month internship at EMBO Press (in Heidelberg).Protein phosphorylation represents one of the most important post-translational modifica- tions (PTMs) for cell signalling, and is is catalysed by a group of enzymes called protein kinases. Through this activity they serve as key regulators of almost all cellular processes. This is achieved at any time by a network of different kinases that are transiently active. The fidelity of cell systems control therefore requires that each kinase targets only a restricted set of substrates. This specificity is achieved partly by contextual factors that separate kinases spatially and temporally, but also by sequence features that are encoded in the kinase domain itself. For this thesis I focus on elements of kinase specificity that are encoded in the the active site of the enzyme. During these investigations I have tried to address three main questions: 1) How is specificity for residues surrounding the phosphorylation site determined in the kinase? 2) How did these specificities evolve? and 3) To what extent does kinase evolution correlate with the evolution of its substrates? First, I developed a sequence-based method for the automated detection of kinase speci- ficity determining residues (SDRs). The putative determinants were then rationalised using available structural data, and in two specific cases were validated experimentally. I also used mutation data from The Cancer Genome Atlas (TCGA) to demonstrate that kinase SDRs are often targeted during cancer. Second, a global analysis of SDR evolution was performed for kinases following gene duplication and speciation, revealing that SDRs often diverge between paralogues but not between orthologues. This global analysis is followed by a detailed case study of G-protein coupled receptor kinase (GRKs) evolution using ancestral sequence reconstructions. Third, I inferred global substrate preferences in a taxonomically broad range of species using phosphoproteome data. I then related the evolution of substrate motif sequences to that of their cognate effector kinases where possible. The results strongly suggest that many of the motifs emerged in a universal eukaryotic ancestor. I finish by summarising the major findings of this doctoral research, which to my knowl- edge represents the most comprehensive analysis to date of protein kinase specificity and its evolution.BBSR

Functional classification of protein domain superfamilies for protein function annotation

Proteins are made up of domains that are generally considered to be independent evolutionary and structural units having distinct functional properties. It is now well established that analysis of domains in proteins provides an effective approach to understand protein function using a `domain grammar'. Towards this end, evolutionarily-related protein domains have been classified into homologous superfamilies in CATH and SCOP databases. An ideal functional sub-classification of the domain superfamilies into `functional families' can not only help in function annotation of uncharacterised sequences but also provide a useful framework for understanding the diversity and evolution of function at the domain level. This work describes the development of a new protocol (FunFHMMer) for identifying functional families in CATH superfamilies that makes use of sequence patterns only and hence, is unaffected by the incompleteness of function annotations, annotation biases or misannotations existing in the databases. The resulting family classification was validated using known functional information and was found to generate more functionally coherent families than other domain-based protein resources. A protein function prediction pipeline was developed exploiting the functional annotations provided by the domain families which was validated by a database rollback benchmark set of proteins and an independent assessment by CAFA 2. The functional classification was found to capture the functional diversity of superfamilies well in terms of sequence, structure and the protein-context. This aided studies on evolution of protein domain function both at the superfamily level and in specific proteins of interest. The conserved positions in the functional family alignments were found to be enriched in catalytic site residues and ligand-binding site residues which led to the development of a functional site prediction tool. Lastly, the function prediction tools were assessed for annotation of moonlighting functions of proteins and a classification of moonlighting proteins was proposed based on their structure-function relationships