Rodriguez, E. H. | Search bioRxiv

bioRxiv2026

FastaLake selects compact metaproteomic databases from large sequence reservoirs by de novo peptide lookup

Metaproteomics analyses require protein sequence databases, yet the microbial proteins present in complex communities are not known in advance. Searching reference catalogs containing hundreds of millions of candidate sequences reduces identification sensitivity, while generating sample-specific databases through metagenomic sequencing adds time and cost. We introduce FastaLake, a computational workflow that uses full-length de novo peptide predictions by AlphaNovo to retrieve complete proteins from broad sequence resources and selects compact databases for conventional searching. In a defined eight-member community, FastaLake approached reference-search peptide yields without knowing its members. Using public sequence catalogs, FastaLake identified about as many peptides as matched metagenomic databases in two stool cohorts and 20-35% more in intestinal fluid at the same nominal 1% peptide false discovery rate. The resulting proteomes reveal personal functional profiles that persist across intestinal regions despite differences in leading species between intestinal fluid and stool. DNA, RNA and protein profiles agreed on broad differences between patients but only weakly on individual functions. All 726 acquisition-specific databases were smaller than the human reference proteome. Compact databases allowed non-specific searches of all 347 CapScan acquisitions, revealing histidine enrichment at peptide C termini in intestinal fluid. FastaLake enables metaproteomics analysis without matched metagenomic sequencing by constructing search databases from sequence resources and de novo peptide sequencing.

bioinformatics↗

bioRxiv2025

Tree-based quantification infers proteoform regulation in bottom-up proteomics data

Quantitative readout is essential in proteomics, yet current bioinformatics methods lack a framework to handle the inherent multi-level nature of the data (fragments, MS1 isotopes, charge states, modifications, peptides and genes). We present AlphaQuant, which introduces tree-based quantification. This approach organizes quantitative data into a hierarchical tree across levels. It allows differential analyses at fragment and MS1 level, recovering up to 50-fold more regulated proteins compared to a state-of-the-art approach. Using gradient boosting on tree features, we address the largely unsolved challenge of scoring quantification accuracy, as opposed to precision. Our method clusters peptides with similar quantitative behavior, providing a new approach to the protein grouping problem and enabling identification of regulated proteoforms directly from bottom-up data. Combined with deep learning classification, we infer phosphopeptides from proteome data alone, validating our findings with EGFR stimulation data. We then describe proteoform diversity across mouse tissues, revealing distinct patterns of post translational modifications and alternative splicing.

bioinformatics↗