Johannes Müller

FastaLake constructs compact metaproteomic databases from sequence resources to improve peptide identification without requiring matched metagenomic sequencing.

The paper introduces pSPEC, a reproducible proteomic sample preparation method for biofluids that increases protein identification by ~20% compared to in-solution plasma.

The paper develops a machine learning model to detect pOXA-48-carrying Enterobacterales using MALDI-TOF MS spectra, achieving high accuracy across different cohorts.

The paper identifies PIF as a placenta-derived peptide that inhibits KV1.3 channels in neutrophils, altering their function during pregnancy.

This study uses multi-platform plasma proteomics to identify metabolic signatures distinguishing elite athletes' acute and chronic exercise adaptations.

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 paper introduces PCA-N, a simplified perchloric acid workflow for plasma proteomics that enables high-throughput, cost-effective analysis of large population samples.

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 study evaluates whether chromatographic performance affects proteome coverage under high-speed data acquisition, finding that proteome depth is now more influenced by mass spectrometry than chromatography.

The paper introduces a novel hybrid mass spectrometer, Stellar MS, enabling rapid and sensitive clinical biomarker validation using 15N labeled proteins.