Biology subjects

Geyer, P. E.

Publications and source records attributed to Geyer, P. E..

13 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↗

bioRxiv2025

Exploring chromatographic dimensions for state-of-the-art proteomics applications

The evolution of mass spectrometry (MS)-based proteomics has been driven by continuous technological advances in sample preparation, instrumentation, and data acquisition. While chromatographic separation has historically been considered a critical bottleneck in achieving comprehensive proteome coverage, recent developments in ultra-fast data acquisition fundamentally challenge this paradigm. We investigated whether the traditional paradigm that chromatographic performance directly correlates with proteome depth still holds true. Spanning a matrix of experiments with five distinct stationary phases, including C18 chemistries, C8, and Phenyl-Hexyl, across eight column lengths (40-140 mm), we evaluate protein identification performance using data-independent acquisition (DIA) on the Orbitrap Astral mass spectrometer. Despite substantial chromatographic differences, we observed remarkably convergent proteome coverage metrics. All C18 and C8 phases consistently achieved over 150,000 precursor- and approximately 9,000 protein group identifications, regardless of column length variations. While distinct selectivity fingerprints persisted across chemistries, these chromatographic differences did not translate into meaningful variations in proteome coverage under high-speed acquisition conditions at 200 Hz. We conclude that the analytical bottleneck has fundamentally shifted from chromatographic resolution to mass spectrometric sampling efficiency, where comprehensive peptide identification is now gained through advanced spectral deconvolution rather than physical separation alone. This paradigmatic shift is reflected in modern proteomics by method development priorities being directed beyond traditional separation optimization, with greater emphasis placed on operational robustness, analytical throughput, and reproducibility.

biochemistry↗

bioRxiv2025

Transposon-Display of AI-designed binders enables manipulation of the proteome in human cells

Transposon-Display is a highly scalable screening method that links proteins to their encoding DNA during expression in E. coli via a mutant transposase. Leveraging this system, we identified AI-designed binders targeting four intracellular human proteins, saturation-mutagenized top candidates, and charge-balanced variants to improve compatibility with the intracellular environment while preserving target binding. The resulting neutral binders can be fused to EGFP allowing antibody-free intracellular staining. Expressing neutral binders in human cells as fusions with functional domains can drive small molecule-controlled protein aggregation and trigger proteasomal degradation of endogenous target proteins in living cells. Manipulating the proteome of living cells using libraries of AI-designed binders may provide a new avenue to screen for disease-relevant protein functions and large-scale functional data may help to refine protein design algorithms.

bioengineering↗

bioRxiv2025

Pre-Analytical Drivers of Bias in Bead-Enriched Plasma Proteomics

Bead-based enrichment has emerged as a promising strategy to improve depth in plasma proteomics by overcoming the dynamic range barrier. However, its robustness against pre-analytical variation has not been sufficiently characterized. Here, we systematically evaluate five plasma proteomics workflows--including three bead-based methods and two conventional protocols--using controlled spike-ins of low-abundance proteins and defined cellular contaminants. We find that bead-based approaches enhance detection of low-abundance proteins but can be highly susceptible to systematic bias from platelet and PBMC contamination, even at low levels. This can easily inflate results by thousands of proteins, likely accounting for some of the very high literature-reported numbers. In contrast, a perchloric acid-based workflow shows notable resistance to erythrocyte and platelet-derived contamination. We further investigate how centrifugation conditions, anticoagulant choice, and buffer-bead combinations modulate contamination profiles and demonstrate that bias can partially be mitigated by optimized sample handling. In total, we identify more than 13,000 different protein groups from various conditions, including cellular components from the circulating proteome. Our results provide a quantitative framework for assessing workflow performance under variable sample quality and offer guidance for both biomarker discovery and quality control in clinical proteomics studies.

biochemistry↗

bioRxiv2023

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are increasingly employed in microbiome studies to obtain a holistic view of molecular changes occurring within microbial communities exposed to different conditions. However, it is not always clear to what extent each omics data type contributes to our understanding of the community dynamics and whether they are concordant with each other. Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods complement each other in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.

microbiology↗

bioRxiv2021

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed OmicLearn (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researchers results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/434053v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@7b95corg.highwire.dtl.DTLVardef@11a5357org.highwire.dtl.DTLVardef@15586c2org.highwire.dtl.DTLVardef@2357f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discovery C_LIO_LIThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledge C_LIO_LIOmicLearns web-based interface provides an easy-to-follow platform for classification and gaining insights into the dataset C_LIO_LISeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integrated C_LIO_LIAll results, settings and method text can be exported in publication-ready formats C_LI

biochemistry↗

bioRxiv2021

A new parallel high-pressure packing system enables rapid multiplexed production of capillary columns

Reversed-phase high performance liquid chromatography (HPLC) is the most commonly applied peptide separation technique in mass spectrometry (MS)-based proteomics. Particle-packed capillary columns are predominantly used in nano-flow HPLC systems. Despite being the broadly applied standard for many years capillary columns are still expensive and suffer from short lifetimes, particularly in combination with ultra-high-pressure chromatography systems. For this reason, and to achieve maximum performance, many laboratories produce their own in-house packed columns. This typically requires a considerable amount of time and trained personnel. Here, we present a new packing system for capillary columns enabling rapid, multiplexed column production with pressures reaching up to 3000 bar. Requiring only a conventional gas pressure supply and methanol as driving fluid, our system replaces the traditional setup of helium pressured packing bombs. By using 10x multiplexing, we have reduced the production time to just under 2 minutes for several 50 cm columns with 1.9 {micro}m particle size, speeding up the process of column production 40 to 800 times. We compare capillary columns with various inner diameters (ID) and length packed under different pressure conditions with our newly designed, broadly accessible high-pressure packing station. One sentence summaryA newly constructed parallel high-pressure packing system enables the rapid multiplexed production of capillary columns. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/433033v2_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@4d224forg.highwire.dtl.DTLVardef@79dd3org.highwire.dtl.DTLVardef@aa047org.highwire.dtl.DTLVardef@1a0b837_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

A paired liver biopsy and plasma proteomics study reveals circulating biomarkers for alcohol-related liver disease

Existing tests for detecting liver fibrosis, inflammation and steatosis, three stages of liver disease that are still reversible are severely hampered by limited accuracy or invasive nature. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to identify biomarkers in a cross-sectional alcohol-related liver disease cohort of nearly 600 individuals. Metabolic functions were downregulated whereas fibrosis-associated signaling and novel immune responses were upregulated, but only half of tissue proteome changes were transmitted to the circulation. Machine learning models based on our biomarker panels outperformed existing tests, laying the foundation for a generic proteomic liver health assessment.

biochemistry↗

bioRxiv2020

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decision-making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/084897v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@433689org.highwire.dtl.DTLVardef@b18068org.highwire.dtl.DTLVardef@1656084org.highwire.dtl.DTLVardef@a38201_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Accurate MS-based Rab10 phosphorylation stoichiometry determination as readout for LRRK2 activity in Parkinson’s disease

Pathogenic mutations in the Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinsons disease (PD). They increase its activity, resulting in augmented Rab10-Thr73 phosphorylation and conversely, LRRK2 inhibition decreases pRab10 levels. However, there is no assay to quantify pRab10 levels for drug target engagement or patient stratification. We developed an ultra-sensitive targeted mass spectrometry (MS)-based assay for determining Rab10-Thr73 phosphorylation stoichiometry in human samples. It uses synthetic stable isotope-labeled (SIL) analogues for both phosphorylated and non-phosphorylated tryptic peptides surrounding Rab10-Thr73 to directly derive the percentage of Rab10 phosphorylation from attomole amounts of the endogenous phosphopeptide. We test the reproducibility of our assay by determining Rab10-Thr73 phosphorylation stoichiometry in human neutrophils before and after LRRK2 inhibition. Compared to healthy controls, neutrophils of LRRK2 G2019S and VPS35 D620N carriers robustly display 1.4-fold and 3.7-fold increased pRab10 levels, respectively. Our generic MS-based assay further establishes the relevance of pRab10 as a prognostic PD marker and is a powerful tool for determining LRRK2 inhibitor efficacy and for stratifying PD patients for LRRK2 inhibitor treatment.

biochemistry↗

bioRxiv2019

Mass Spectrometry-based Plasma Proteomics: Considerations from Sample Collection to Achieving Translatable Data

The proteomic analyses of human blood and blood-derived products (e.g. plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g. inter-individual variability), analysis of biospecimen (e.g. sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest whilst enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=51 SRC=\"FIGDIR/small/716563v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@1faf719org.highwire.dtl.DTLVardef@174c1b1org.highwire.dtl.DTLVardef@5860baorg.highwire.dtl.DTLVardef@366c21_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2018

Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies

Plasma and serum are rich sources of information regarding an individuals health state and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative read-out of the plasma proteome. Here we employ Plasma Proteome Profiling to define contamination marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma and whole blood of 20 individuals (>6000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments we determine contamination-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource (www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

epidemiology↗

bioRxiv2018

A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics

To further integrate mass spectrometry (MS)-based proteomics into biomedical research and especially into clinical settings, high throughput and robustness are essential requirements. They are largely met in high-flow rate chromatographic systems for small molecules but these are not sufficiently sensitive for proteomics applications. Here we describe a new concept that delivers on these requirements while maintaining the sensitivity of current nano-flow LC systems. Low-pressure pumps elute the sample from a disposable trap column, simultaneously forming a chromatographic gradient that is stored in a long storage loop. An auxiliary gradient creates an offset, ensuring the re-focusing of the peptides before the separation on the analytical column by a single high-pressure pump. This simplified design enables robust operation over thousands of sample injections. Furthermore, the steps between injections are performed in parallel, reducing overhead time to a few minutes and allowing analysis of more than 200 samples per day. From fractionated HeLa cell lysates, deep proteomes covering more than 130,000 sequence unique peptides and close to 10,000 proteins were rapidly acquired. Using this data as a library, we demonstrate quantitation of 5200 proteins in only 21 min. Thus, the new system-termed Evosep One-analyzes samples in an extremely robust and high throughput manner, without sacrificing in depth proteomics coverage.

biochemistry↗