Predicting and characterising protein damage in the extracellular matrix

Chronic UVR exposure of human skin can result in photo-ageing which manifests both externally and internally (as remodelling of skin layers including the extracellular matrix-rich dermis). However, the intermittent nature of UVR exposure over a timescale of decades combined with the longevity of many structural dermal proteins makes the identification of dermal photo-ageing targets and mechanisms challenging. Over the past ten years work in our group has demonstrated that: (i) proteins which are rich in amino acid chromophores are susceptible to physiologically relevant doses of solar simulated radiation, (ii) this protein degradation is mediated primarily by the photodynamic production of reaction oxygen species and (iii) UVR-chromophore rich proteins are located in tissue regions where they may act as endogenous sunscreens. We have also shown that ECM proteases selectively degrade UVR-damaged assemblies in vitro and have developed new machine learning-based models to predict protease cleavage sites and hence relative protease susceptibilities in the human skin proteome. The recent development of peptide location fingerprinting applied to conventional mass spectrometry datasets has allowed the identification of novel candidate biomarkers of UVR-induced damage as a consequence of photo-ageing. For example, this approach is able to identify structure-associated modifications in collagen VI alpha chains, although collagen VI remodeling is not evident by conventional histological analysis. These methods and resources (skin proteome database, protein susceptibility prediction and peptide location fingerprinting analysis) are available for use at www.manchesterproteome.manchester.ac.uk. Key areas for future research include using peptide location fingerprinting to: (i) characterise the structural hallmarks of ageing and photo-ageing mechanisms across different phototypes and (ii) evaluate the efficacy of rejuvenating treatments against novel protein biomarkers.