Sebastian Virreira Winter

The paper identifies urinary protein signatures linked to LRRK2 dysfunction in Parkinson's disease using multi-cohort proteomics and machine learning, showing cross-species relevance and potential as pharmacodynamic markers.

This study evaluates bead-based and conventional plasma proteomics workflows, finding bead methods enhance low-abundance protein detection but are prone to contamination bias, while perchloric acid-based methods resist contamination better.

This paper introduces Transposon-Display, a method to identify and optimize AI-designed protein binders for intracellular use, enabling controlled protein manipulation in human cells.

The paper uses proteomics and machine learning to identify potential biomarkers in cerebrospinal fluid for Parkinson's disease, showing altered protein levels and neuroinflammation signatures.

This study uses urinary proteomics and machine learning to identify biomarkers for stratifying Parkinson's disease patients and LRRK2 mutation carriers.

MaxQuant.Live enables real-time global targeting of over 25,000 peptides by combining shotgun and targeted proteomics approaches.