Descrizione del progetto
Analisi innovativa dei dati proteomici per identificare in modo completo le isoforme proteiche
Le isoforme proteiche, che sono versioni diverse di una proteina da un singolo gene, contribuiscono alla diversità molecolare della vita. Tuttavia, mancano prove esaurienti sulle isoforme proteiche a livello di proteine, in quanto durante l’analisi fino all’80 % di tutti i dati proteomici rimane inutilizzato. Per ovviare a questo problema, il progetto ORIGIN, finanziato dal CER, propone un nuovo approccio che si basa sulla scoperta delle impronte multidimensionali deterministiche (origini, da cui il nome del progetto) che le isoforme proteiche generano nelle misurazioni proteomiche. ORIGIN identificherà sistematicamente le isoforme proteiche sfruttando una nuova strategia di analisi dei dati proteomici basata sulla spettrometria di massa. L’approccio prevede l’addestramento di reti neurali profonde per prevedere le origini che vengono successivamente utilizzate per identificare e quantificare le isoforme proteiche.
Obiettivo
Did you know that ~80% of all proteomic data is not utilized? Proteins play a vital role in all biological processes and organisms. We believe that different versions of a single gene product – protein isoforms – shape the molecular diversity of life. However, comprehensive evidence on protein-level is not available. Chromatography-coupled tandem mass spectrometry (LC-MS/MS) is the de-facto standard for measuring proteomes, but it is not good at identifying isoforms because at least 80% of the recorded information is never used. I argue that isoforms leave a deterministic multi-dimensional fingerprint (ORIGINs) representing their physicochemical properties in each proteomic measurement. Therefore, the central aim of this project is to discover and quantify protein isoforms systematically by a novel MS-based proteomics data analysis strategy. By tapping into the wealth of data the proteomics community has already amassed, I will train deep neural networks that allow the prediction of ORIGINs. Second, I will implement an innovative data analysis strategy that utilizes ORIGINs to identify and quantify isoforms. Third, I will demonstrate that ORIGINs can be used to substantially broaden our understanding of the molecular diversity of life by showcasing its application on four emerging and challenging questions in proteome research of varying biological and technical complexity. This will allow me to address a fundamental open question in biology: to which extent and prevalence isoforms are actually translated and what functional roles they might be associated with. ORIGINs will improve the sensitivity, biological resolution and accuracy at which proteins and their isoforms can be identified and quantified. Beyond this, the concept of ORIGINs can be applied to and improve any proteomics experiments, and thus holds the potential to revolutionize MS-based proteomics as a technology and elevate the whole field of protein-based research.
Campo scientifico
- natural sciencescomputer and information sciencesdata science
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsproteomics
- natural scienceschemical sciencesanalytical chemistrymass spectrometry
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
80333 Muenchen
Germania