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Covalent modifications of histone tails play essential roles in mediating chromatin structure and epigenetic regulation. JmjD3 is a JumonjiC domain containing histone demethylase, belongs to the KDM6 subfamily, and catalyses the removal of methyl groups on methylated lysine 27 on histone 3 (H3K27), a critical mark to promote polycomb mediated repression and gene silencing. The importance of JmjD3 has been implicated in development, cancer biology and immunology. In this thesis, I report the recombinant production of active human JmjD3, development of two in vitro screening assays, a cell-based assay, and structural determination of JmjD3 in complex with the inhibitor 8-hydroxy-5-carboxyquinoline (8HQ). A highly selective and potent small molecule inhibitor GSK-J1 was subsequently identified. The inhibitor is active in HeLa cells and promotes a dose-dependent increase of global H3K27 methylation. The inhibitor GSK-J1 was used in two different cell assay systems related to inflammation and differentiation, to understand how H3K27 demethylation controls cellular functions. By inhibiting H3K27me3 demethylation, it is demonstrated that tumor necrosis factor (TNF) and other pro-inflammatory cytokines are regulated by H3K27 demethylase inhibition in M1- type macrophages derived from healthy volunteers and rheumatoid arthritis patients. It is also shown that inhibition of H3K27me3 demethylation abrogates cellular fusion of M2- type macrophages. During RANKL induced osteoclast differentiation, JmjD3 is up-regulated and promotes the expression of the key transcription factor NFATc1. By inhibiting JmjD3, NFATc1 expression is reduced and osteoclastogenesis is inhibited. This mechanism demonstrates a novel anti-resorptive principle of potential utility in conditions of excess bone resorption such as osteoporosis, bone erosion in inflammatory arthritis or cancer of the bone. These experiments further resolve the ambiguity between scaffold and catalytic function associ- ated with the H3K27 demethylase in these biological systems, and demonstrate that its enzymatic activity is crucial for epigenetic regulation of macrophage and osteoclast function.

Hedgehog (Hh) morphogens play fundamental roles in development whilst dysregulation of Hh signalling leads to disease. Multiple receptors are involved in the modulation of Hh morphogens at the cell surface. Among these, the interactions of Hh ligands with glycosaminoglycan (GAG) (for example heparan or chondroitin sulphate) chains of proteoglycans in the extracellular matrix play a key role in shaping morphogen gradients and fulfil important functions in signal transduction. Several high resolution crystal structures of Sonic Hh (Shh)-GAG complexes have been determined. The interaction determinants, confirmed by binding studies and mutagenesis reveal a novel Hh site for GAG interactions, which appears to be common to all Hh proteins. This novel site is supported by a wealth of published functional data, and resides in a hot spot region previously found to be crucial for Hh receptor binding. Crystal packing analysis combined with analytical ultracentrifugation on Hh-GAG complexes suggest a potential mechanism for GAG-dependent multimerisation. A key step in the Hh pathway is the transduction of the Hh signal into the receiving cell. The Hh signal transducer, Smoothened, is a key target drug target in the pathway with several modulators in clinical trials, despite an absence of structural data. Smoothened is required to activate all levels of Hh signalling. Recent evidence points to the conserved N-terminal ectodomain (ECD) in regulating Smo activity, from vertebrates to invertebrates. Despite the central importance of the ECD, its precise function remains elusive. A crystal structure of the ECD at 2.2 Å resolution is reported here. Structural analysis and biophysical experiments are discussed with reference to the potential function of this intriguing domain.

GATA1 and KLF1 are transcription factors that regulate genes which are important for the development of erythroid cells. The GATA1 transcriptional co-factor FOG1 has been shown to be essential in a wide range of GATA1 dependent cellular functions. Here we tried to understand the diverse mechanisms by which GATA1 and KLF1 recognize their binding sites, how the GATA1 recognition mechanisms are affected by complexation with either FOG1 or KLF1 and how the GATA1 recognition mechanisms affect the transcriptional regulation of the erythroid differentiation. We profiled the DNA binding specificities/affinities of a GATA1 fragment (mGATA1NC), that contains only the two DNA binding domains (N-terminal and C-terminal Zn finger), and the DNA binding specificities/affinities of a KLF1 fragment (mKLF1257-358), that contains the three DNA binding domains, using a novel methodology that combines EMSA with high throughput sequencing (EMSA-seq (Wong et al., 2011a)). We also profiled the DNA binding specificities of the C-terminal Zn finger of GATA1 alone (mGATA1C), the wt-mGATA1, the wt-mGATA1/wt-mFOG1 complex and the mGATA1NC/mKLF1257-358 complex. At first, we confirmed that the N-terminal Zn finger of GATA1 has a strong preference for the “GATC” motif, whereas the C-terminal Zn finger of GATA1 has a strong preference for the “GATA” motif. Next, we found that in the mGATA1NC, both DNA binding domains can bind simultaneously a wide range of different positional combinations of the co-occurring “GATA” and “GATC” motifs, on the same DNA sequence. The wt-mGATA1 did not show the ability to bind in the same co-occurring motifs implying an effect of the non-DNA binding domains of the protein in the regulation of its DNA binding specificities. On the contrary, complexation of wt-mGATA1 with the wt-mFOG1 partially restored its ability to bind in a now limited range of different positional combinations of the co-occurring “GATA” and “GATC” motifs, on the same DNA sequence. Similar observations were made for other pairs of GATA1 N-terminal and C-terminal Zn finger specific motifs. We then projected the GATA1 DNA binding specificities/affinities in vivo and we classified the GATA1 ChIP-seq peaks in low, medium or high affinity based on the number of the GATA1 motifs. We noticed that high affinity GATA1 ChIP-seq peaks tend to appear in regions with low nucleosome occupancy. We also noticed that GATA1 ChIP-seq peaks in the enhancer regions are usually high affinity whereas GATA1 ChIP-seq peaks in the proximal promoter regions are usually low affinity. Additionally, we observed that high affinity GATA1 ChIP-seq peaks are usually found in regions with increased levels of H3K4me2 and are associated with a higher decrease in the H3K4me3 levels on the TSS of the nearby genes. None of these GATA1 related in vivo observations were found for the KLF1 ChIP-seq positions. These findings significantly advance our understanding of the DNA binding properties of GATA1, KLF1 and their complexes and give an insight on the importance of the GATA1 DNA binding affinities in the regulation of the erythroid transcriptional program. Overall the work establishes an experimental and analytical framework to investigate how transcriptional co-factors can change the DNA binding specificities of specific transcription factors and how integration of the transcription factor DNA binding affinities with in vivo data can give novel insights into the transcriptional regulation.

Sequence alignment is one of the most intensely studied problems in bioinformatics, and is an important step in a wide range of analyses. An issue that has gained much attention in recent years is the fact that downstream analyses are often highly sensitive to the specific choice of alignment. One way to address this is to jointly sample alignments along with other parameters of interest. In order to extend the range of applicability of this approach, the first chapter of this thesis introduces a probabilistic evolutionary model for protein structures on a phylogenetic tree; since protein structures typically diverge much more slowly than sequences, this allows for more reliable detection of remote homologies, improving the accuracy of the resulting alignments and trees, and reducing sensitivity of the results to the choice of dataset. In order to carry out inference under such a model, a number of new Markov chain Monte Carlo approaches are developed, allowing for more efficient convergence and mixing on the high-dimensional parameter space. The second part of the thesis presents a directed acyclic graph (DAG)-based approach for representing a collection of sampled alignments. This DAG representation allows the initial collection of samples to be used to generate a larger set of alignments under the same approximate distribution, enabling posterior alignment probabilities to be estimated reliably from a reasonable number of samples. If desired, summary alignments can then be generated as maximum-weight paths through the DAG, under various types of loss or scoring functions. The acyclic nature of the graph also permits various other types of algorithms to be easily adapted to operate on the entire set of alignments in the DAG. In the final part of this work, methodology is introduced for alignment-DAG-based sequence annotation using hidden Markov models, and RNA secondary structure prediction using stochastic context-free grammars. Results on test datasets indicate that the additional information contained within the DAG allows for improved predictions, resulting in substantial gains over simply analysing a set of alignments one by one.

The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.

As depicted in Chapter I, 2-oxoglutarate- (2OG) dependent oxygenases are ubiquitous in living systems and display a wide range of cellular functions, spanning metabolism, transcription, and translation. Although functionally diverse, the 2OG oxygenases share a high degree of structural similarities between their catalytic sites. From a medicinal chemistry point of view, the combination of biological diversity and structural similarity presents a rather challenging task for the development of selective small molecules for functional studies in vivo. The non-selective metal chelator 8-hydroxyquinoline (8HQ) was used as a template for the generation of tool compound <b>I</b> for the KDM4 subfamily of histone demethylases via application of the Betti reaction. Structural analogue <b>II</b> was used as the corresponding negative control (Figure A). These compounds were characterised in vitro against a range of 2OG oxygenases and subsequently used for studies in cells. <b>I</b> displays selectivity for KDM4 and increases the level of the H3K9me3 histone mark in cells. It has an effect on the post-translational modification pattern of histone H3, but not other histones, and reduces the viability of lung cancer cells, but not normal lung cells, derived from the same patient. <b>I</b> also stabilises hypoxia-inducable factor HIF in cells via a mechanism which seems to be independent from prolyl hydroxylase inhibition. This work is described in Chapters II and III. The chemical biology research in epigenetics is complemented by qualitative analysis conducted in the social sciences at Said Business School. With a global view on how innovation occurs and may actively be fostered, Chapter IV focuses on the potential of epigenetics in drug discovery and how this process may actively be promoted within the framework of open innovation. Areas of focus include considerations of incremental and disruptive technology; how to claim, demarcate, and control the market; how knowledge brokering occurs; and insights about process, management, organisation, and culture of open innovation. In contrast to the open-skies approach adopted for the development of a tool compound in Chapters II and III, a focused-library approach was taken for the generation of a tool compound for the OGFOD1 ribosomal prolyl hydroxylase. The development of a suitable in vitro activity assay for OGFOD1 in Chapter V enabled the development of lead compound <b>III</b> in Chapter VI. <b>III</b> is selective for OGFOD1 against the structurally closely related prolyl hydroxylase PHD2.