Biology subjects

Malmström, L.

Publications and source records attributed to Malmström, L..

10 recordsLinked to original sources

bioRxiv2026

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

Streptococcus pneumoniae remains a major public health concern, largely due to the limited serotype coverage and other constraints of pneumococcal conjugate vaccines, as well as the increasing antimicrobial resistance among circulating strains. In the pursuit of protein-based pneumococcal vaccines, pneumolysin (PLY), a secreted multifunctional cholesterol-dependent cytolysin, represents a promising target. To date, the relationship between the B-cell epitope landscape and neutralising PLY-specific antibody responses has remained elusive, hindering the rational design of effective PLY-based vaccines. Using a panel of PLY-specific monoclonal antibodies, functional assays, multimodal protein mass spectrometry and data-driven computational modelling, we mapped the structural epitope landscape of native PLY and linked epitope-paratope interactions to neutralising potency. We further refined and structurally characterised a protective cross-species epitope conserved among homologous cholesterol-dependent cytolysins (CDC). This epitope provides a promising foundation for rational, epitope-focused vaccine design, offering a pathway toward species-independent vaccines targeting the CDC protein superfamily.

immunology↗

bioRxiv2025

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

Streptococcus pyogenes (Group A Streptococcus, GAS) is a significant human pathogen for which no licensed vaccine is currently available. Here, we report a de novo designed epitope-centric protein-based nanoparticle vaccine against GAS. By integrating structural mass spectrometry techniques and deep learning approaches, we re-engineered a protective epitope (D3m) present in domain 3 of streptolysin O, a prominent pore-forming toxin produced by GAS. D3m was displayed on the surface of a self-assembling icosahedral nanoparticle (D3m-NP) to enhance epitope presentation and immunogenicity. Mice immunised with D3m-NP mounted haemolysis-neutralising titres and displayed a more uniform, epitope-centric antibody response than those receiving the community-standard detoxified full-length streptolysin O. Our findings highlight a promising strategy for GAS vaccine development by combining multimodal protein mass spectrometry, protein design and a versatile protein-based nanoparticle vaccine platform.

immunology↗

bioRxiv2024

Streptolysin O Accelerates the Conversion of Plasminogen to Plasmin

Streptococcus pyogenes is a human-specific bacterial pathogen that produces several proteins that exploit the plasminogen (PLG)-plasmin (PLM) fibrinolysis system to dismantle blood clots and to facilitate spread and survival within the host. In this study, we explored the interactions between streptolysin O (SLO), a key cytolytic toxin produced by S. pyogenes, and human plasma proteins using affinity-enrichment mass spectrometry. SLO binds specifically to PLG, and this interaction accelerates the conversion of PLG to PLM by tissue-type plasminogen activator (tPA). To further investigate the molecular detail of the PLG-SLO interaction, we employed hydrogen/deuterium exchange mass spectrometry combined with targeted cross-linking mass spectrometry, uncovering that SLO binding induces local conformational shifts in PLG. These changes lead to the formation of a stabilized intermediate PLG-SLO complex that becomes significantly more sensitive to proteolytic processing by tPA. Our findings reveal a conserved moonlighting pathomechanistic role for SLO extending beyond the well characterized cytolytic activity. Additionally, the work underscores the diversity of functional proteomics in identifying and clarifying new host-pathogen interactions.

molecular biology↗

bioRxiv2023

Multi-Modal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O

An important element of antibody-guided vaccine design is the use of neutralizing/opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multi-modal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass spectrometry-based de novo sequencing, followed by chemical crosslinking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in Domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse-engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in Domain 3, likely preventing oligomerization and subsequent secondary structure changes critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.

biochemistry↗

bioRxiv2023

The increased hinge flexibility of an IgG1-IgG3 hybrid monoclonal enhances Fc-mediated protection against group A streptococci

Antibodies are central to the immune response against microbes. We have previously generated a protective IgG1 monoclonal antibody targeting the M protein, a critical virulence factor of Streptococcus pyogenes. Here, we generated this antibody in all human IgG subclasses and evaluated their function. Despite significantly reduced binding, the IgG3 subclass antibody demonstrated remarkably enhanced opsonic function. We hypothesized that increased Fc flexibility could explain this improved efficacy. We engineered a hybrid IgG subclass antibody, IgGh, containing the backbone of IgG1 with the hinge of IgG3, leaving the Fabs unchanged. The IgGh maintained a similar binding ability as IgG1 while gaining the strong opsonic function seen with IgG3. Molecular dynamics simulations of the different antibodies showed altered IgG Fab-antigen interactions, reflecting the differences observed in affinity. More importantly, when the antibodies were bound to the antigen, the simulations showed that the Fc of both IgGh and IgG3 exhibited extensive movement in 3D space relative to the M protein. The increased flexibility of IgGh directly translated to enhanced opsonic function and significantly increased the protection against infection with Streptococcus pyogenes in mice. Our findings demonstrate how altering Fc flexibility can improve Fc-mediated opsonic function and how modifications in the constant domain can regulate Fab-antigen interactions. In addition, the enhanced in vivo function of a more flexible IgG provides new therapeutic opportunities for monoclonal antibodies. One sentence summaryAntibody Fc flexibility in 3D space correlates with efficient Fc-mediated phagocytosis of streptococci

immunology↗

bioRxiv2023

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

The advent of novel methods in mass spectrometry-based proteomics allows for the identification of biomarkers and biological pathways which are crucial for the understanding of complex diseases. However, contemporary analytical methods often omit essential information, such as protein abundance and protein co-regulation, and therefore miss crucial relationships in the data. Here, we introduce a generalized workflow that incorporates proteins, their abundances, and associated pathways into a deep learning-based methodology to improve biomarker identification and pathway analysis through the creation and interpretation of biologically informed neural networks (BINNs). We successfully employ BINNs to differentiate between two subphenotypes of septic acute kidney injury (AKI) and COVID-19 from the plasma proteome and utilize feature attribution-methods to introspect the networks to identify which proteins and pathways are important for distinguishing between subphenotypes. Compared to existing methods, BINNs achieved the highest predictive accuracy and revealed that metabolic processes were key to differentiating between septic AKI subphenotypes, while the immune system was more important to the classification of COVID-19 subphenotypes. The methodology behind creating, interpreting, and visualizing BINNs were implemented in a free and open source Python-package: https://github.com/InfectionMedicineProteomics/BINN.

bioinformatics↗

bioRxiv2022

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

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systematically varying the composition and size of the training set. We assessed the generated models performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training sets size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEM models extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEM models proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

bioinformatics↗

bioRxiv2021

Opsonization by non-neutralizing antibodies can confer protection to SARS-CoV-2 despite Spike-dependent modulation of phagocytosis

Spike-specific antibodies are central to effective COVID19 immunity. Research efforts have focused on antibodies that neutralize the ACE2-Spike interaction but not on non-neutralizing antibodies. Antibody-dependent phagocytosis is an immune mechanism enhanced by opsonization, where typically, more bound antibodies trigger a stronger phagocyte response. Here, we show that Spike-specific antibodies, dependent on concentration, can either enhance or reduce Spike-bead phagocytosis by monocytes independently of the antibody neutralization potential. Surprisingly, we find that both convalescent patient plasma and patient-derived monoclonal antibodies lead to maximum opsonization already at low levels of bound antibodies and is reduced as antibody binding to Spike protein increases. Moreover, we show that this Spike-dependent modulation of opsonization seems to affect the outcome in an experimental SARS-CoV-2 infection model. These results suggest that the levels of anti-Spike antibodies could influence monocyte-mediated immune functions and propose that non-neutralizing antibodies could confer protection to SARS-CoV-2 infection by mediating phago-cytosis.

immunology↗

bioRxiv2021

Streptococcus pyogenes forms serotype and local environment-dependent inter-species protein complexes

Streptococcus pyogenes is known to cause both mucosal and systemic infections in humans. In this study, we used a combination of quantitative and structural mass spectrometry techniques to determine the composition and structure of the interaction network formed between human plasma proteins and the surface of different S. pyogenes serotypes. Quantitative network analysis revealed that S. pyogenes form serotype-specific interaction networks that are highly dependent on the domain arrangement of the surface-attached M protein. Subsequent structural mass spectrometry analysis and computational modelling on one of the M proteins, M28 revealed that the network structure changes across different host microenvironments. We report that M28 binds secretory IgA via two separate binding sites with high affinity in saliva. During vascular leakage mimicked by increasing plasma concentrations in saliva, the binding of secretory IgA was replaced by binding of monomeric IgA and C4BP. This indicates that an upsurge of C4BP in the local microenvironment due to damage of the mucosal membrane drives binding of C4BP and monomeric IgA to M28. The results suggest that S. pyogenes has evolved to form microenvironment-dependent host-pathogen protein complexes to combat the human immune surveillance during both mucosal and systemic infections.

microbiology↗

bioRxiv2020

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

Proteogenomics approaches have enabled the generation of extensive information levels when compared to single omics technology studies, although burdened by massive experimental efforts. Here, we developed four improvements of a data independent acquisition mass spectrometry proteogenomics workflow to reveal distinct molecular phenotypes related to breast cancer appearance. We confirm mutational processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Our analyses also validated previously established enrichments of estrogen receptor-dependent molecular features relating to transcription factor expression, and provided evidence for molecular differences related to the presence of mammographic appearances in spiculated tumors. In addition, several transcript-protein pairs displayed radically different abundance correlations depending on the overall clinical and pathological properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant sample groups, and that these protein networks influence both cancer biology as well as the abundance of potential biomarkers and drug targets.

systems biology↗