Johan A. Malmström

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.

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.

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.

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

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.

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

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.

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

This paper develops a combined systems antigenomics-serology workflow to characterize circulating IgG against Group A Streptococcus, revealing antigen repertoire and functional properties of antibodies in healthy and infected individuals.

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

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

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.

This study uses multi-modal mass spectrometry to identify a conserved protective epitope in Streptolysin O from S. pyogenes, which could inform vaccine and therapeutic development.

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

This study shows that increasing Fc flexibility in an IgG1-IgG3 hybrid monoclonal antibody enhances opsonic function and protection against group A streptococci.

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

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

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

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.

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.

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

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