Peptide location fingerprinting reveals modification-associated biomarkers of ageing in human tissue proteomes

Ozols, Matiss; Eckersley, Alexander; Mellody, Kieran T.; Mallikarjun, Venkatesh; Warwood, Stacey; O’Cualain, Ronan; Knight, David; Watson, Rachel E.B.; Griffiths, Christopher E.M.; Swift, Joe; Sherratt, Michael J.

doi:10.1101/2020.09.14.296020

Cited by 4 publications

(2 citation statements)

References 70 publications

(70 reference statements)

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“…In order to compare the predictive capabilities of PROSPER and DeepCleave against a complex proteome, we decellularized a post-confluent culture of HDFs and digested the remaining ECM with MMP9. Over 60 proteins were identified by LC–MS/MS in the baseline (non-MMP9 digested) matrix, many of which (COL6A1, COL6A2, COL6A3, EMILIN1, COL1A1, COL1A2) are found in the human dermis [ 44 , 45 ]. Putative MMP9 cleavage sites were identified in 40 out of 60 proteins, (including COL6A1, EMILIN1, FBLN2, COL1A2) resulting in 390 putative MMP9 cleavage sites ( Figure 1 d).…”

Section: Resultsmentioning

confidence: 99%

“…This is consistent with the ability of defensins to act as antioxidant proteins [ 63 ]. Crucially, MPSC not only predicts that known photoaging candidate biomarkers (LOX, TIMP3) will be susceptible to UVR/ROS degradation [ 44 , 64 , 65 ] but also highlights the potential susceptibility (TSHB, CTSB, CTSZ) and resistance (DCD, EMCN, CALM5 and APOC1) of new skin aging biomarker candidates ( Figure 3 c). In complex tissues, degradative mechanisms do not act in isolation [ 36 , 37 ], and we, and others, have shown that UVR exposure can enhance protease-mediated-proteolysis [ 7 , 66 ].…”

Section: Resultsmentioning

confidence: 99%

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Predicting Proteolysis in Complex Proteomes Using Deep Learning

Ozols

Eckersley

Platt

et al. 2021

IJMS

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Both protease- and reactive oxygen species (ROS)-mediated proteolysis are thought to be key effectors of tissue remodeling. We have previously shown that comparison of amino acid composition can predict the differential susceptibilities of proteins to photo-oxidation. However, predicting protein susceptibility to endogenous proteases remains challenging. Here, we aim to develop bioinformatics tools to (i) predict cleavage site locations (and hence putative protein susceptibilities) and (ii) compare the predicted vulnerabilities of skin proteins to protease- and ROS-mediated proteolysis. The first goal of this study was to experimentally evaluate the ability of existing protease cleavage site prediction models (PROSPER and DeepCleave) to identify experimentally determined MMP9 cleavage sites in two purified proteins and in a complex human dermal fibroblast-derived extracellular matrix (ECM) proteome. We subsequently developed deep bidirectional recurrent neural network (BRNN) models to predict cleavage sites for 14 tissue proteases. The predictions of the new models were tested against experimental datasets and combined with amino acid composition analysis (to predict ultraviolet radiation (UVR)/ROS susceptibility) in a new web app: the Manchester proteome susceptibility calculator (MPSC). The BRNN models performed better in predicting cleavage sites in native dermal ECM proteins than existing models (DeepCleave and PROSPER), and application of MPSC to the skin proteome suggests that: compared with the elastic fiber network, fibrillar collagens may be susceptible primarily to protease-mediated proteolysis. We also identify additional putative targets of oxidative damage (dermatopontin, fibulins and defensins) and protease action (laminins and nidogen). MPSC has the potential to identify potential targets of proteolysis in disparate tissues and disease states.

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Section: Resultsmentioning

confidence: 99%