Biology subjects

Holstein, T.

Publications and source records attributed to Holstein, T..

9 recordsLinked to original sources

bioRxiv2026

Interplay of action-based prediction and top-down attention: EEG evidence for joint modulation of late perceptual processing

Voluntary action shapes our perception through sensory predictions of its consequences. These predictions are thought to inhibit perceptual processing of predicted outcomes, leading to sensory attenuation. However, some studies have reported findings that contrast with this effect, suggesting that the influence of predictions may reflect, or interact with, attentional processes. Here, we investigated the interplay of action-based prediction and top-down attention on early and late perceptual processing, where prediction and attention refer to the likelihood of a sensory event and its behavioral relevance, respectively. Electroencephalography (EEG) was recorded while participants viewed two sequential gratings. The orientation of the first was either predicted and self-generated or unpredicted and externally generated. Participants judged the tilt direction of the second grating relative to the first but responded only when the grating appeared in a task-relevant color. EEG analyses revealed no modulation of early visual potentials (N1a), but modulations in later processing stages (P3b). Specifically, the P3b exhibited reduced amplitude for predicted and self-generated stimuli compared to unpredicted and externally generated ones, but only when they were task-relevant. Multivariate pattern analysis further showed that the significant temporal cluster supporting decoding of the first gratings orientation was largest for relevant, predicted and self-generated stimuli. Our results suggest an optimization process whereby action-based prediction, when aligned with task goals, reduces the amount of evidence needed and increases its accumulation speed, while preserving sensory representations as accurate as in the absence of prediction/self-generation. HighlightsO_LIFeature-based attention and prediction were orthogonally manipulated in an agency context. C_LIO_LINo early sensory modulation by attention or action-based prediction was observed. C_LIO_LIReduction of both P3b amplitude and latency emerged for predicted/self-generated and task-relevant stimuli. C_LIO_LIMultivariate analysis and behavior results support an interpretation whereby relevant action-based predictions optimize evidence accumulation process. C_LI

neuroscience↗

bioRxiv2025

Limitations of de novo sequencing in resolving sequence ambiguity

De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectra create ambiguity, making unambiguous identification challenging. Recent deep learning advances have produced numerous de novo models that predict sequences and refine peptide-spectrum matches under such conditions. Yet, their relative strengths, weaknesses, and ability to handle spectrum ambiguity remain unclear. Here, we benchmark eight state-of-the-art models on three publicly available proteomics datasets, comparing performance using established metrics and quantifying inter-model agreement. We assess post-processing approaches, including iterative refinement, rescoring, and reranking, for their ability to improve identification accuracy, and perform an error analysis to identify common mispredictions and their causes. Model performance varied, with considerable overlap of correct identifications. Post-processing yielded no or only modest improvements. Most sequencing errors were model-independent and driven by limited fragment ion coverage, a limitation also observed in database searches with large search spaces.

bioinformatics↗

bioRxiv2025

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing

MotivationA central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times. ResultsWe present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer-based approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool. Availability and implementationMegaPXs source code and all related documentation files are freely available in a GitHub repository (https://github.com/rki-mf2/MegaPX) under the MIT License. ContactMuthT@rki.de Supplementary informationAll supplementary tables and figures are included at the end of the preprint.

bioinformatics↗

bioRxiv2025

Boosting metaproteomics identification rates and taxonomic specificity with MS2Rescore

BackgroundMetaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the identification sensitivity of current identification algorithms, which were primarily designed for single-species analyses. Addressing this, we evaluated the machine learning-driven MS{superscript 2}Rescore post-processing tool on multiple metaproteomics datasets from diverse microbial environments and benchmark studies. ResultsWe demonstrate that machine learning-driven rescoring outperforms traditional metaproteomics identification workflows. It significantly increases peptide identification rates compared to Sage, which itself already implements basic rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal to no sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater confidence in downstream taxonomic annotation. ConclusionsOur findings show that MS{superscript 2}Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved confidence in taxonomic annotation. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.

bioinformatics↗

bioRxiv2024

PathwayPilot: A User-Friendly Tool for Visualizing and Navigating Metabolic Pathways

BackgroundMetaproteomics, the study of collective proteomes in environmental communities, plays a crucial role in understanding microbial functionalities affecting ecosystems and human health. Pathway analysis offers structured insights into the biochemical processes within these communities. However, no existing tool effectively combines pathway analysis with peptide- or protein-level data. ResultsThis manuscript introduces PathwayPilot, a user-friendly web application for exploring and visualizing metabolic pathways. PathwayPilot can compare functional annotations across different samples or organisms within a sample. A case study on the impact of caloric restriction on gut microbiota demonstrated the tools efficacy in deciphering complex metaproteomic data. The re-analysis revealed significant shifts in enzyme expressions related to short-chain fatty acid biosynthesis, aligning with existing research findings and showcasing PathwayPilots capability for accurate functional annotation and comparison across different microbial communities. ConclusionsPathwayPilot represents a significant advancement in metaproteomic data analysis, offering a user-friendly interface for exploring and visualizing metabolic pathways. This study not only validates the tools applicability in real-world scenarios but also highlights its potential for broader research implications in microbial ecology and health sciences.

bioinformatics↗

bioRxiv2024

The Peptonizer2000: graphical model based taxonomic identifications of metaprotoemic samples

Metaproteomics, the large-scale study of proteins from microbial communities, faces challenges in identifying species due to similarities in protein sequences across different organisms. Current methods often rely on simple counting of matches between proteins and taxa, which can lead to low accuracy. We introduce the Peptonizer2000, a new tool that uses advanced modeling to provide more precise taxonomic identifications along with confidence scores. It combines peptide scores from any proteomic search engine with peptide-to-taxon links from the Unipept database. By applying statistical models, the Peptonizer2000 improves taxonomic resolution and delivers more reliable results. We validate its performance using publicly available datasets, demonstrating its ability to produce high-confidence identifications. Our results suggest that the Peptonizer2000 improves the specificity and confidence of taxonomic assignments in metaproteomics, providing a valuable resource for the study of complex microbial communities.

bioinformatics↗

bioRxiv2024

MultiStageSearch: a multi-step proteogenomic workflow for taxonomic identification of viral proteome samples adressing database bias

The recent years, with the global SARS-Cov-2 pandemic, have shown the importance of strain level identification of viral pathogens. While the gold-standard approach for unkown viral sample identification remains genomics, studies have shown the necessity and advantages of orthogonal experimental approaches such as proteomics, based on proteomic database search methods. The databases required as references for both proteins and genome sequences are known to be biased towards certain taxa, such as pathogenic strains or species, or common model organisms. Aditionally, the proteomic databases are not as comprehensive as the genomic databases. We present MultiStageSearch, an iterative database search approach for the taxonomic identification of viral samples combining proteomic and genomic databases. The potentially present species and strains are inferred using a generalist proteomic reference database. MultiStageSearch then automatically creates a proteogenomic database. This database is further pre-processed byfiltering for duplicates as well as clustering of identical ORFs to address potential bias present in the genomic database. Furthermore, the workflow is independent of the strain level NCBI taxonomy, enabling the inference of strains that are not present in the NCBI taxonomy. We performed a benchmark on several viral samples to demonstrate the performance of the strain level taxonomic inference. The benchmark shows superior performance compared to state of the art methods for untargeted strain level inference using proteomic data while being independent of the NCBI taxonomy at strain level.

bioinformatics↗

bioRxiv2022

PepGM: A probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores

MotivationInferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, correct taxonomic identification is crucial when identifying different viral strains with high sequence homology - considering, e.g., the different epidemiological characteristics of the various strains of SARS-CoV-2. Additionally, many viruses mutate frequently, further complicating the correct assignment of virus proteomic samples. ResultsWe present PepGM, a probabilistic graphical for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence score, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on species level, which PepGM clearly indicates by lower confidence scores. Availability and ImplementationPepGM is written in Python and embedded into a Snakemake workflow. Its is available at https://github.com/BAMeScience/PepGM

bioinformatics↗

bioRxiv2019

A combined strategy of neuropeptide predictions and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone Nematostella vectensis

Neuropeptides are a class of bioactive peptides and are responsible for various physiological processes including metabolism, development and reproduction. Although accumulated genome and transcriptome data have reported a number of neuropeptide candidates, it still remains difficult to obtain a comprehensive view of neuropeptide repertoires due to their small and variable nature. Neuropeptide prediction tools usually work only for those peptides for which sequentially related homologs have previously been identified. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, obtaining reliable identifications of endogenous peptides is still difficult using a conventional tandem mass spectrometry-based peptide identification approach using protein database because a large search space has to be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of the prediction of in silico cleaved endogenous neuropeptides followed by peptide-spectrum matching enabling highly sensitive and reliable neuropeptide identification. This approach effectively reduces the search space of peptide-spectrum matching, and thus increases search sensitivity. To identify neuropeptides in Nematostella vectensis, a basal eumetazoan having one of the most primitive nervous systems, we scanned the Nematostella protein database for sequences displaying structural hallmarks of metazoan neuropeptides, including C/N-terminal structures and modifications. Peptide-spectrum matching was performed against the in silico cleaved peptides and successfully identified dozens of neuropeptides at high confidence. The identification of Nematostella neuropeptides structurally related the tachykinin, GnRH/AKH, neuromedin-U/pyrokinin peptide families indicate that these peptides already originated in the eumetazoan ancestor of all animal species, most likely concomitantly with the development of a nervous system.

biochemistry↗