Johan A. Malmström

Multi-Enzyme Data-Independent Acquisition Mass Spectrometry for Proteoform-Aware Single-shot Proteomics

The paper introduces mE-DIA, a multi-enzyme DIA-MS workflow that improves proteoform detection in proteomics by integrating analysis and interpretation into a single toolset.

Temporal changes in platelet activation and organ dysfunction during invasive Streptococcus pyogenes infection

This study examines temporal changes in platelet activation and organ dysfunction during invasive Streptococcus pyogenes infection in mice, showing platelets contribute to liver dysfunction and may be therapeutic targets.

Learning proteomic disease trajectories with flow matching

This paper introduces proteome velocity, a framework using flow matching to infer continuous proteome trajectories from proteomics data, showing its application in mouse sepsis and clinical COVID-19.

Structural profiling of the pneumolysin epitope landscape uncovers a cross-species neutralising site across cholesterol-dependent cytolysins

The paper maps the structural epitope landscape of pneumolysin and identifies a conserved cross-species neutralising site among cholesterol-dependent cytolysins.

ppIRIS: deep learning for proteome-wide prediction of bacterial protein-protein interactions

ppIRIS predicts bacterial protein-protein interactions using deep learning, achieving high accuracy and enabling proteome-wide screening, with applications in identifying virulence pathways and host-pathogen interactions.

Design of a Streptolysin O Epitope-Centric Nanoparticle Vaccine Against Streptococcus pyogenes

This paper designs an epitope-centric nanoparticle vaccine against Streptococcus pyogenes using structural mass spectrometry and deep learning to enhance immunogenicity.

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

The paper develops a proteomics strategy to infer the origin of proteins in human plasma using a proteome atlas and RNA/protein atlases, demonstrating its application in disease cohorts.

Cumulating MS Signal enables polyclonal antibody analysis

This paper introduces a novel MS score framework for analyzing polyclonal antibodies, demonstrating its ability to identify antibody subgroups and predict interactions.

Navigating the peptide sequence space in search for peptide binders with BoPep

BoPep identifies peptide binders to specific proteins by efficiently navigating sequence space, reducing docking evaluations and discovering novel therapeutic peptides.

Multiplex genome editing eliminates the Warburg Effect without impacting growth rate in mammalian cells

This study eliminates the Warburg Effect in mammalian cells by knocking out lactate dehydrogenase and feedback regulators, maintaining growth rate while reducing lactate production.

Streptolysin O Accelerates the Conversion of Plasminogen to Plasmin

This study shows that streptolysin O enhances plasminogen to plasmin conversion by interacting with plasminogen and altering its conformation to increase sensitivity to proteolytic processing.

Community-based peptidomic clustering reveals pathogen-specific proteolytic signatures in wounds

The paper develops a computational workflow to cluster peptides in peptidomic data, revealing pathogen-specific proteolytic signatures in wound fluids.

Explainable machine learning for the identification of proteome states via the data processing kitchen sink

The paper introduces DPKS, an explainable machine learning framework for identifying proteome states by calculating protein importance in predictive models.

A pharmacoproteomic landscape of organotypic intervention responses in gram-negative sepsis

This study uses pharmacoproteomics to analyze treatment responses in a murine sepsis model, identifying distinct response patterns and drug interactions across organs and time.

Interpreting biologically informed neural networks for enhanced biomarker discovery and pathway analysis

This paper introduces BINNs, a deep learning method that integrates protein abundance and pathways to improve biomarker discovery and subphenotype classification in proteomics data.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

This study develops multienzyme deep learning models for de novo peptide sequencing from mass spectrometry data, showing improved generalizability and sequence coverage across diverse protease digests and species.

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

This paper introduces GPS, a machine learning-based method for improving peptide and protein identification accuracy and rates in DIA proteomics by using an external dataset, and demonstrates its effectiveness in identifying biomarkers for acute kidney injury.

Structural determination of Streptococcus pyogenes M1 protein interactions with human immunoglobulin G using integrative structural biology

This study determines the structural interactions between Streptococcus pyogenes M1 protein and human IgG using integrative structural biology, revealing conserved mechanisms to inhibit phagocytosis.

A Proteogenomic workflow reveals distinct molecular phenotypes related to breast cancer appearance

The paper improves a proteogenomics workflow to reveal molecular phenotypes linked to breast cancer appearance, showing differences in protein networks and biomarker abundance.