On Computable Protein Functions

Proteins are biological machines that perform the majority of functions necessary for life. Nature has evolved many different proteins, each of which perform a subset of an organism’s functional repertoire. One aim of biology is to solve the sparse high dimensional problem of annotating all proteins with their true functions. Experimental characterisation remains the gold standard for assigning function, but is a major bottleneck due to resource scarcity. In this thesis, we develop a variety of computational methods to predict protein function, reduce the functional search space for proteins, and guide the design of experimental studies. Our methods take two distinct approaches: protein-centric methods that predict the functions of a given protein, and function-centric methods that predict which proteins perform a given function. We applied our methods to help solve a number of open problems in biology. First, we identified new proteins involved in the progression of Alzheimer’s disease using proteomics data of brains from a fly model of the disease. Second, we predicted novel plastic hydrolase enzymes in a large data set of 1.1 billion protein sequences from metagenomes. Finally, we optimised a neural network method that extracts a small number of informative features from protein networks, which we used to predict functions of fission yeast proteins

Broad functional profiling of fission yeast proteins using phenomics and machine learning

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions

Dynamic changes in the brain protein interaction network correlates with progression of A?42 pathology in Drosophila

Alzheimer’s disease (AD), the most prevalent form of dementia, is a progressive and devastating neurodegenerative condition for which there are no effective treatments. Understanding the molecular pathology of AD during disease progression may identify new ways to reduce neuronal damage. Here, we present a longitudinal study tracking dynamic proteomic alterations in the brains of an inducible Drosophila melanogaster model of AD expressing the Arctic mutant Aβ42 gene. We identified 3093 proteins from flies that were induced to express Aβ42 and age-matched healthy controls using label-free quantitative ion-mobility data independent analysis mass spectrometry. Of these, 228 proteins were significantly altered by Aβ42 accumulation and were enriched for AD-associated processes. Network analyses further revealed that these proteins have distinct hub and bottleneck properties in the brain protein interaction network, suggesting that several may have significant effects on brain function. Our unbiased analysis provides useful insights into the key processes governing the progression of amyloid toxicity and forms a basis for further functional analyses in model organisms and translation to mammalian systems

Christianity as Public Religion::A Justification for using a Christian Sociological Approach for Studying the Social Scientific Aspects of Sport

The vast majority of social scientific studies of sport have been secular in nature and/or have tended to ignore the importance of studying the religious aspects of sport. In light of this, Shilling and Mellor (2014) have sought to encourage sociologists of sport not to divorce the ‘religious’ and the ‘sacred’ from their studies. In response to this call, the goal of the current essay is to explore how the conception of Christianity as ‘public religion’ can be utilised to help justify the use of a Christian sociological approach for studying the social scientific aspects of sport. After making a case for Christianity as public religion, we conclude that many of the sociological issues inherent in modern sport are an indirect result of its increasing secularisation and argue that this justifies the need for a Christian sociological approach. We encourage researchers to use the Bible, the tools of Christian theology and sociological concepts together, so to inform analyses of modern sport from a Christian perspective

Ultrafast energy transfer in biomimetic multistrand nanorings.

We report the synthesis of LH2-like supramolecular double- and triple-stranded complexes based upon porphyrin nanorings. Energy transfer from the antenna dimers to the π-conjugated nanoring occurs on a subpicosecond time scale, rivaling transfer rates in natural light-harvesting systems. The presence of a second nanoring acceptor doubles the transfer rate, providing strong evidence for multidirectional energy funneling. The behavior of these systems is particularly intriguing because the local nature of the interaction may allow energy transfer into states that are, for cyclic nanorings, symmetry-forbidden in the far field. These complexes are versatile synthetic models for natural light-harvesting systems