Biology subjects

Brennsteiner, V.

Publications and source records attributed to Brennsteiner, V..

5 recordsLinked to original sources

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↗

bioRxiv2026

AlphaPeptTools: scverse-native analysis of mass spectrometry-based proteomics

Mass spectrometry (MS)-based proteomics now routinely profiles proteomes at scale, but extracting biological insight and integrating complementary modalities remains challenging. Here, we present AlphaPeptTools, a Python package for the analysis of MS-proteomics data built on the AnnData data structure of the scverse. AlphaPeptTools implements scalable and modular strategies for MS-data ingestion, quality control, preprocessing, and statistical analysis while seamlessly integrating with the scverse ecosystem, unlocking multi-level, spatial, and multimodal analyses.

bioinformatics↗

bioRxiv2026

HIP-MS: An ultra-high-throughput, sensitive, and versatile affinity enrichment platform for static and dynamic interactome profiling

Although protein-protein interactions govern virtually all cellular processes, systematic interactome mapping by affinity enrichment mass spectrometry (AE-MS) is constrained by manual sample preparation and lengthy liquid chromatography (LC)-MS/MS acquisition. Here we present High-throughput Interactome Profiling by MS (HIP-MS), an automated, end-to-end pipeline that overcomes these limitations. It leverages the compact, high-affinity ALFA tag for on-plate nanobody capture in 384-well format, combined with on-plate tryptic digestion. It can process almost 10,000 samples per week from protein expression up to MS measurement and can be combined with ultra-fast gradient LC-MS acquisition of 500 samples per day. HIP-MS remains sensitive down to low-microgram lysate inputs, a 4,000-fold reduction compared to recent large-scale screens. Our pipeline recovers complexes from diverse cellular compartments and resolves endogenous membrane receptor signaling. HIP-MS establishes a scalable foundation for systematic interrogation of protein interactions across conditions, perturbations, and time, and for the generation of large-scale datasets for computational modeling.

systems biology↗

bioRxiv2025

A simplified perchloric acid workflow with neutralization (PCA-N) for democratizing deep plasma proteomics at population scale

Large scale plasma proteomics studies offer tremendous potential for biomarker discovery but face significant challenges in balancing analytical depth, throughput and cost-effectiveness. We present an optimized perchloric acid-based workflow with neutralization - PCA-N - that addresses these limitations. By introducing a neutralization step following protein precipitation, PCA-N enables direct enzymatic digestion without additional purification steps, reducing sample volume requirements to only 5 {micro}L of plasma while maintaining deep plasma proteome coverage. The streamlined protocol allows preparation of over 10,000 samples per day using 384-well formats at costs comparable to undepleted plasma analysis (NEAT). Rigorous validation according to the recently introduced CLSI C64 guideline demonstrated that despite somewhat higher technical variability compared to NEAT, PCA-N maintained excellent biological resolution and reproducibility. We confirmed the workflows exceptional stability through analysis of 1,500 quality control samples systematically interspersed among 36,000 plasma samples measured continuously over 311 days. Technical performance remained consistent across multiple instruments, sample preparation batches and nearly a year of measurements. Compared to NEAT plasma proteomics, PCA-N doubled the proteomic depth while maintaining comparable reagent costs and throughput. The minimal sample reequipments, operational simplicity while using only common laboratory chemical and exceptional scalability positions PCA-N as an attractive approach for population-level plasma proteomics, democratizing access to deep plasma proteome analysis. HighlightsO_LIPerchloric acid with neutralization (PCA-N) allows drastic upscaling of plasma proteomics C_LIO_LIMinimal sample input (5 {micro}L) in 384-well format enables thousands of samples processed daily C_LIO_LIPCA-N democratizes deep plasma proteomics using only common chemicals C_LIO_LIValidation per CLSI C64 demonstrates excellent repeatability and reproducibility C_LIO_LIPCA-N showed exceptional stability across 311 days of continuous MS acquisition C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=106 HEIGHT=200 SRC="FIGDIR/small/645089v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@11eb315org.highwire.dtl.DTLVardef@7f4f41org.highwire.dtl.DTLVardef@2c41feorg.highwire.dtl.DTLVardef@1a09925_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

AlphaDIA enables End-to-End Transfer Learning for Feature-Free Proteomics

Mass spectrometry (MS)-based proteomics continues to evolve rapidly, opening more and more application areas. The scale of data generated on novel instrumentation and acquisition strategies pose a challenge to bioinformatic analysis. Search engines need to make optimal use of the data for biological discoveries while remaining statistically rigorous, transparent and performant. Here we present alphaDIA, a modular open-source search framework for data independent acquisition (DIA) proteomics. We developed a feature-free identification algorithm particularly suited for detecting patterns in data produced by sensitive time-of-flight instruments. It naturally adapts to novel, more eTicient scan modes that are not yet accessible to previous algorithms. Rigorous benchmarking demonstrates competitive identification and quantification performance. While supporting empirical spectral libraries, we propose a new search strategy named end-to-end transfer learning using fully predicted libraries. This entails continuously optimizing a deep neural network for predicting machine and experiment specific properties, enabling the generic DIA analysis of any post-translational modification (PTM). AlphaDIA provides a high performance and accessible framework running locally or in the cloud, opening DIA analysis to the community.

bioinformatics↗