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Neurodegenerative diseases, such as Alzheimer’s disease, are the major preliminary cause of dementia. Research shows that physiological changes occur years before the onset of symptoms. Therefore, accurate clinical tests for early and discriminative diagnosis across various types of neurodegenerative diseases are crucial. For this, we need robust biomarkers, which represent measurable traits that can objectively detect and evaluate various biological processes. Early detection with sensitive biomarkers could allow effective treatment options and expand opportunities for monitoring and managing disease progression. Biofluid protein biomarkers are key to precision medicine, providing invaluable insights into underlying disease mechanisms. In body fluids, biomarkers coexist with a plethora of other molecules, potentially interfering with antibody-epitope interactions. Conformational changes or aggregation of proteins may further obstruct or disguise epitopes, preventing their accurate detection and quantification. Therefore, understanding the solvent accessibility of protein biomarkers, or in other words, the game of hide-and-seek is essential for accurate detection and quantification. This study bridges computational and experimental methodologies to delve into protein biomarkers associated with dementia. Due to the diversity of tools and topics involved in this research work, in Chapter 1, we first discuss all the main areas, advances in computational and wet-lab techniques, as well as knowledge gaps leading to the questions explored. In Chapter 2, we explored how hydrophobic (sticky) protein surfaces are and we analyse the implications of exposed hydrophobic surfaces. Chapter 3 attempts to demystify one of the possible mechanisms for hidden (inaccessible) epitopes. Chapter 4 presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia. In the following chapter, we zoom in on one of the

Summary Proteins tend to bury hydrophobic residues inside their core during the folding process to provide stability to the protein structure and to prevent aggregation. Nevertheless, proteins do expose some ‘sticky’ hydrophobic residues to the solvent. These residues can play an important functional role, e.g. in protein–protein and membrane interactions. Here, we first investigate how hydrophobic protein surfaces are by providing three measures for surface hydrophobicity: the total hydrophobic surface area, the relative hydrophobic surface area and—using our MolPatch method—the largest hydrophobic patch. Secondly, we analyze how difficult it is to predict these measures from sequence: by adapting solvent accessibility predictions from NetSurfP2.0, we obtain well-performing prediction methods for the THSA and RHSA, while predicting LHP is more challenging. Finally, we analyze implications of exposed hydrophobic surfaces: we show that hydrophobic proteins typically have low expression, suggesting cells avoid an overabundance of sticky proteins.

Proteomics studies have shown differential expression of numerous proteins in dementias but have rarely led to novel biomarker tests for clinical use. The Marie Curie MIRIADE project is designed to experimentally evaluate development strategies to accelerate the validation and ultimate implementation of novel biomarkers in clinical practice, using proteomics-based biomarker development for main dementias as experimental case studies. We address several knowledge gaps that have been identified in the field. First, there is the technology-translation gap of different technologies for the discovery (e.g., mass spectrometry) and the large-scale validation (e.g., immunoassays) of biomarkers. In addition, there is a limited understanding of conformational states of biomarker proteins in different matrices, which affect the selection of reagents for assay development. In this review, we aim to understand the decisions taken in the initial steps of biomarker development, which is done via an interim narrative update of the work of each ESR subproject. The results describe the decision process to shortlist biomarkers from a proteomics to develop immunoassays or mass spectrometry assays for Alzheimer's disease, Lewy body dementia, and frontotemporal dementia. In addition, we explain the approach to prepare the market implementation of novel biomarkers and assays. Moreover, we describe the development of computational protein state and interaction prediction models to support biomarker development, such as the prediction of epitopes. Lastly, we reflect upon activities involved in the biomarker development process to deduce a best-practice roadmap for biomarker development., QC 20220810

Proteins tend to bury hydrophobic residues inside their core during the folding process to provide stability to the protein structure and to prevent aggregation. Nevertheless, proteins do expose some 'sticky' hydrophobic residues to the solvent. These residues can play an important functional role, for example in protein-protein and membrane interactions. Here, we investigate how hydrophobic protein surfaces are by providing three measures for surface hydrophobicity: the total hydrophobic surface area, the relative hydrophobic surface area, and - using our MolPatch method - the largest hydrophobic patch. Secondly, we analyse how difficult it is to predict these measures from sequence: by adapting solvent accessibility predictions from NetSurfP2.0, we obtain well-performing prediction methods for the THSA and RHSA, while predicting LHP is more difficult. Finally, we analyse implications of exposed hydrophobic surfaces: we show that hydrophobic proteins typically have low expression, suggesting cells avoid an overabundance of sticky proteins.