Protein arginine methyltransferases (PRMTs) are enzymes that catalyze the methylation of protein arginine residues, resulting in the formation of monomethylarginine, and/or asymmetric or symmetric dimethylarginines. Although understanding of the PRMTs has grown rapidly over the last few years, several challenges still remain in the PRMT field. Here, we describe the development of two techniques that will be very useful in investigating PRMT regulation, small molecule inhibition, oligomerization, protein-protein interaction, and substrate specificity, which will ultimately lead to the advancement of the PRMT field. Studies have shown that having an N-terminal tag can influence enzyme activity and substrate specificity. The first protocol tackles this problem by developing a way to obtain active untagged recombinant PRMT proteins. The second protocol describes a fast and efficient method for quantitative measurement of AdoMet-dependent methyltranseferase activity with protein substrates. In addition to being very sensitive, this method decreases the processing time for the analysis of PRMT activity to a few minutes compared to weeks by traditional methods, and generates 3000-fold less radioactive waste. We then used these methods to investigate the effect of truncating the NT of human PRMT1 variant 1 (hPRMT1-V1) on enzyme activity, protein-protein interactions, and substrate specificity. Our studies show that the NT of hPRMT1-V1 influences enzymatic activity and protein-protein interactions. In particular, methylation of a variety of protein substrates was more efficient when the first 10 amino acids of hPRMT1v1 were removed, suggesting an autoinhibitory role for this small section of the N-terminus. Likewise, as portions of the NT were removed, the altered hPRMT1v1 constructs were able to interact with more proteins. Overall, my studies suggest the the sequence and length of the NT of hPRMT1v1 is capable of enforcing specific protein interactions.

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Information fusion aims to develop intelligent approaches of integrating information from complementary sources, such that a more comprehensive basis is obtained for data analysis and knowledge discovery. Our Protein Biological Unit (ProtBuD) database is the first database that integrated the biological unit information from the Protein Data Bank (PDB), Protein Quaternary Server (PQS) and Protein Interfaces, Surfaces and Assemblies (PISA) server, and compared the three biological units side-by-side. The statistical analyses show that the inconsistency within these databases and between them is significant. In order to improve the inconsistency, we studied interfaces across different PDB entries in a protein family using an assumption that interfaces shared by different crystal forms are likely to be biologically relevant. A novel computational method is proposed to achieve this goal. First, redundant data were removed by clustering similar crystal structures, and a representative entry was used for each cluster. Then a modified k-d tree algorithm was applied to facilitate the computation of identifying interfaces from crystals. The interface similarity functions were derived from Gaussian distributions fit to the data. Hierarchical clustering was used to cluster interfaces to define the likely biological interfaces by the number of crystal forms in a cluster. Benchmark data sets were used to determine whether the existence or lack of existence of interfaces across multiple crystal forms can be used to predict whether a protein is an oligomer or not. The probability that a common interface is biological is given. An interface shared in two different crystal forms by divergent proteins is very likely to be biologically important. The interface data not only provide new interaction templates for computational modeling, but also provide more accurate data for training sets and testing sets in data-mining research to predict protein-protein interactions. In summary, we developed a framework which is based on databases where different biological unit information is integrated and new interface data are stored. In order for users from the biology community to use the data, a stand-alone software program, a web site with a user-friendly graphical interface, and a web service are provided. / Computer and Information Science

It remains a major challenge in biophysics to understand the conformational dynamics of biomolecules. As powerful tools, molecular dynamics (MD) simulations have become increasingly important in studying the full atomic details of conformational dynamics of biomolecules. In addition, many statistical models have been developed to give insight into the big datasets from MD simulations. In this work, I first describe three statistical models used to analyze MD simulation data: Lifson-Roig Helix-Coil theory, Bayesian inference models, and Markov state models. Then I present the applications of each model in analyzing MD simulations and revealing insight into the conformational dynamics of biomolecules. These statistical models allow us to bridge microscopic and macroscopic mechanisms of biological processes and connect simulations with experiments. / Chemistry

There is currently a large increase in the speed of production of DNA sequence data as next generation sequencing technologies become more widespread. As such there is a need for rapid computational techniques to functionally annotate data as it is generated. One computational method for the functional annotation of protein-coding genes is via detection of interaction partners. If the putative partner has a functional annotation then this annotation can be extended to the initial protein via the established principle of “guilt by association”. This work presents a method for rapid detection of functional interaction partners for proteins through the use of the comparative method. Functional links are sought between proteins through analysis of their patterns of presence and absence amongst a set of 54 eukaryotic organisms. These links can be either direct or indirect protein interactions. These patterns are analysed in the context of a phylogenetic tree. The method used is a heuristic combination of an established accurate methodology involving comparison of models of evolution the parameters of which are estimated using maximum likelihood, with a novel technique involving the reconstruction of ancestral states using Dollo parsimony and analysis of these reconstructions through the use of logistic regression. The methodology achieves comparable specificity to the use of gene coexpression as a means to predict functional linkage between proteins. The application of this method permitted a genome-wide analysis of the human genome, which would have otherwise demanded a potentially prohibitive amount of computational resource. Proteins within the human genome were clustered into orthologous groups. 10 of these proteins, which were ubiquitous across all 54 eukaryotes, were used to reconstruct a phylogeny. An application of the heuristic predicted a set of functional protein interactions in human cells. 1,142 functional interactions were predicted. Of these predictions 1,131 were not present in current protein-protein interaction databases.

Receptor proteins on the surfaces of cells equip them to communicate with each other and to sense and interact with their environment. One receptor family, the αβ T-cell receptors (TCRs), allow T lymphocytes to detect and respond to pathogens via interactions with antigen-presenting major histocompatibility complex (MHC) molecules on target cells. A degree of TCR cross-reactivity (e.g. through structural similarity between peptide-MHC (pMHC) complexes) is essential to account for all possible pathogens, but can also lead to the misinterpretation of self antigens as foreign, and thereby elicit an autoimmune response, resulting in diseases such as multiple sclerosis (MS). Structural studies of pMHC and TCR-pMHC complexes have been key to developing of an understanding of the molecular basis of TCR cross reactivity, and the first strand of this thesis describes attempts to express and purify a highly cross-reactive MS patient-derived TCR for structural characterisation. The formation, purification and crystallisation of a TCR-self pMHC complex including another autoreactive TCR is also described. Another family of receptors, the fibronectin leucine-rich transmembrane proteins (FLRTs), has been implicated in roles in embryonic development including cell sorting and adhesion. In the second strand of this thesis, the nature of homotypic interactions between FLRTs, which may underlie adhesion between FLRT transfected cells, is investigated. Biophysical analyses demonstrate that these interactions may be mediated by the extracellular leucine-rich repeat (LRR) domain, and crystal structures of all three FLRT LRR domains suggest how interactions between them may underlie FLRT self-association at the cell surface. Residues which contribute to these interactions are conserved across different members of the FLRT family and different species. These findings confirm that FLRTs induce homotypic cell-cell adhesion, and suggest that this behaviour is mediated by self association at the cell surface via the LRR domain.