Leyuan Li

π-MNovo improves de novo peptide sequencing for metaproteomics by adapting to microbial spectra and selecting candidates based on evidence, achieving higher recall than existing models.

This study maps metaproteomic responses of human gut microbiota to 312 drugs, identifying 47 compounds that significantly alter microbial protein profiles and reveal functional state shifts.

The paper introduces PhyloFunc, a new functional beta-diversity metric for metaproteomic data that incorporates phylogenetic relationships to better capture functional dissimilarities between microbial communities.

π-PrimeNovo is a non-autoregressive deep learning model for de novo peptide sequencing that achieves higher accuracy and 69x faster performance than existing methods.

The paper introduces π-HelixNovo, a Transformer-based model for de novo peptide sequencing that outperforms existing methods and improves taxonomic resolution in gut metaproteomes.

This study introduces a streamlined TMT labeling method using pre-aliquoted dry reagents, improving efficiency and reducing variability in proteomic and metaproteomic analyses.

This study quantifies protein-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics and network analysis, revealing high redundancy and environmental influences on functional organization.

MetaProClust-MS1 rapidly clusters metaproteomes using short MS1 profiles, showing robust microbiome shifts and correlated cluster topologies compared to traditional tandem mass spectrometry.

MetaLab 2.0 improves PTM identification and quantification in metaproteomics by providing an open search workflow with higher accuracy and sensitivity than traditional methods.

pepFunk improves functional analysis of metaproteomic gut microbiome data by focusing on peptides rather than proteins, identifying more enriched KEGG pathways.

The paper introduces a chemoenzymatic method to quantitatively analyze N-glycan types on glycoproteins, demonstrating its application in characterizing glycan heterogeneity in IgG1 and MCF7 cells.