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In their idealized forms, enzymes can facilitate complex reactions with extreme specificity and selectivity. Additionally, in this imaginative form, they only require mild reaction conditions, resulting in low energy consumption, and they are biodegradable, efficient, reusable, and sustainable. Unfortunately, this idealized form often deviates significantly from reality, where enzymes are more likely to be associated with marginal stability and low reaction rates, leaving them less than desirable for many industrial applications. As such, if we could master the process of engineering the configuration of a protein towards a given task, the implications could be staggering.
This thesis aims to contribute to the process of protein engineering, mainly how computational tools can be used to make the protein engineering process more efficient and accessible.
Article I explores the current state of the art in machine learning-guided directed evolution and serves as a foundation for Article II, which is a concrete application of these techniques to an engineering campaign. Despite successfully improving overall activity and selectivity, we also observe limitations and constraints within the methodology. Article III then delves into these drawbacks and attempts to lay the foundation for a more generalizable and, more importantly, efficient engineering workflow, balancing the strengths and weaknesses of computational techniques with advances in gene synthesis. We then validated this novel pipeline in Article IV, where we show the potential of this methodology. Article V describes a more standard protein engineering campaign on squalene-hopene cyclases for potentially interesting products in the flavor and fragrance industry. Lastly, Article VI outlines a PyMol plugin for molecular docking.
Background: Computational tools for the investigation of transcriptional regulation, in particular of transcription factor binding sites (TFBS), in evolutionary context are developed. Existing sequence based tools prediction such binding sites do not consider their actual functionality, although it is known that besides the base sequence many other aspects are relevant for binding and for the effects of that binding. In particular in Eukaryotes a perfectly matching sequence motif is neither necessary nor sufficient for a functional transcription factor binding site. Published work in the field of transcriptional regulation frequently focus on the prediction of putative transcription factor binding sites based on sequence similarity to known binding sites. Furthermore, among the related software, only a small number implements visualization of the evolution of transcription factor binding sites or the integration of other regulation related data. The interface of many tools is made for computer scientists, although the actual interpretation of their outcome needs profound biological background knowledge. Results and Discussion: The tool presented in this thesis, "ReXSpecies" is a web application. Therefore, it is ready to use for the end user without installation providing a graphical user interface. Besides extensive automation of analyses of transcriptional regulation (the only necessary input are the genomic coordinates of a regulatory region), new techniques to visualize the evolution of transcription factor binding sites were developed. Furthermore, an interface to genome browsers was implemented to enable scientists to comprehensively analyze their regulatory regions with respect to other regulation relevant data. ReXSpecies contains a novel algorithm that searches for evolutionary conserved patterns of transcription factor binding sites, which could imply functionality. Such patterns were verified using some known transcription factor binding sites of genes involved in pluripotency. In the appendix, efficiency and correctness of the used algorithm are discussed. Furthermore, a novel algorithm to color phylogenetic trees intuitively is presented. In the thesis, new possibilities to render evolutionary conserved sets of transcription factor binding sites are developed. The thesis also discusses the evolutionary conservation of regulation and its context dependency. An important source of errors in the analysis of regulatory regions using comparative genetics is probably to find and to align homologous regulatory regions. Some alternatives to using sequence similarity alone are discussed. Outlook: Other possibilities to find (functional) homologous regulatory regions (besides whole-genome-alignments currently used) are BLAST searches, local alignments, homology databases and alignment-free approaches. Using one ore more of these alternatives could reduce the number of artifacts by reduction of the number of regions that are erroneously declared homologous. To achieve more robust predictions of transcription, the author suggests to use other regulation related data besides sequence data only. Therefore, the use and extension of existing tools, in particular of systems biology, is proposed.
Age is the single biggest risk factor for most major human diseases. As such, understanding the intricate molecular changes that drive biological aging holds great promise in attempting to slow
the onset of systemic diseases and thereby increase the effective health-span in modern societies.
This thesis explores several computational approaches to capture and analyze the molecular biological alterations triggered by intrinsic and extrinsic aging using skin as a model tissue to deliver genes and pathways as potential targets for intervention strategies.
Publication 1 demonstrates the utility of multi-omics data integration strategies for aging research, leading to the identification of four latent aging phases in skin tissue through an integrated cluster analysis of gene expression and DNA methylation data. The four phases improved the detection of molecular aging signals and were shown to be associated with sunbathing habits of the test subjects. Deeper analysis revealed extensive non-linear alterations in various biological pathways particularly at the transition into the fourth aging phase, coinciding with menopause, with potentially wide-reaching functional implications. Publication 2 describes the development of a novel type of age clock, that provides a new level of interpretability by embedding biological pathway information in the architecture of an artificial neural network. The clock not only generates meaningful biological age estimates from gene expression data, but further allows simultaneous monitoring of the aging states of various biological processes through the activations of intermediate neurons. Analyses of the inner workings of the clock revealed a wide-spread impact of aging on the global pathway landscape. Simulation experiments using the transcriptomic clock recapitulated known functional aging gene associations and allowed deciphering of the pathways by which accelerated aging conditions such as chronic sun exposure and Hutchinson-Gilford progeria syndrome exert their effects. Publication 3 further explores the molecular alterations caused by the pro-aging effector UV irradiation in the skin. The multi-omics data analysis of repetitively irradiated skin revealed signs of the immediate acquisition of aging- and cancer-related epigenetic signatures and concurrent wide-spread transcriptional changes across various biological processes. Investigations into the varying resilience to irradiation between subjects revealed prognostic biomarker signatures capable of predicting individual UV tolerances, with accuracies far surpassing the traditional Fitzpatrick classification scheme. Further analysis of the transcripts and pathways associated with UV tolerance identified a form of melanin-independent DNA damage protection in individuals with higher innate UV resilience.
Together, the approaches and findings described in this thesis explore several new angles to advance our understanding of aging processes and external drivers of aging such as UV irradiation in the human skin and deliver new insight on target genes and pathways involved.