Biology subjects

Szymczak, P.

Publications and source records attributed to Szymczak, P..

11 recordsLinked to original sources

bioRxiv2026

Designing antimicrobials with programmable mechanism and safety

Antimicrobial peptides (AMPs) are a promising solution to antimicrobial resistance, yet generative models for their design cannot control the physicochemical properties and motifs that shape activity and selectivity. Here, we present OmegAMP, a conditional diffusion framework controlling net charge, mean hydrophobicity, and sequence length, supporting de novo, analog, and motif-guided design. Across 204 wet-lab characterized peptides, de novo generation yielded antimicrobials with broad activity against multidrug-resistant Gram-negative isolates. Analog generation converted six inactive prototypes into antimicrobials, with the prototype determining each analog's membrane-disruption mode and mammalian-cell safety. Motif-guided analog generation preserved lipopolysaccharide engagement of active prototypes, and a redesigned non-antimicrobial leucine zipper acquired antimicrobial activity while retaining DNA-perturbing character in vitro. In murine skin and thigh infection models, leads reduced bacterial burden, with a motif-guided DNA-perturbing lead matching the fluoroquinolone control systemically. OmegAMP opens a programmable route to new peptide antibiotics whose mechanism and safety follow from the chosen prototype.

bioinformatics↗

bioRxiv2026

BATTLE-AMP: Benchmarking Antimicrobial Peptide Predictors

As antimicrobial resistance outpaces antibiotic development, antimicrobial peptides (AMPs) have emerged as a promising class of alternative antibacterials, and computational predictors are increasingly used to prioritize AMP candidates. Such predictors are typically evaluated on binary AMP/non-AMP classification, which does not test whether they can identify peptides with clinically relevant potency against specific pathogens. We present BATTLE-AMP, a benchmarking framework that evaluates AMP predictors against experimentally measured minimum inhibitory concentrations (MICs) across clinically relevant bacterial species and strains. We surveyed 48 published methods, finding fewer than 25% reproducible, and benchmarked 10 model families (21 variants) using experimental MIC data, synthetic sequence perturbations, activity cliff analyses, and all-atom molecular dynamics (MD) simulations. Four findings emerge: (i) models trained on MIC data outperform binary classifiers regardless of architecture; (ii) the best model depends on the target pathogen, so model selection must be guided by the biological question; (iii) most models cannot distinguish active peptides from inactive sequences with identical amino acid composition; and (iv) activity cliffs remain unresolved by both machine learning and MD, marking a limit of current computational methods. BATTLE-AMP is released as an open Snakemake framework at https://github.com/szczurek-lab/battleamp-snakemake for benchmarking new models and scoring novel candidate libraries.

bioinformatics↗

bioRxiv2026

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients; 263 samples, complemented by 9 healthy controls and 25 post-COVID-19 patients, yielding [~]2.4 million single cells across 28 cell types), and extracted daily clinical data from the electronic health record (>15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

immunology↗

bioRxiv2026

Integrated kidney and urine proteomics define encrypted antimicrobial peptides as effectors of host defence in human pyelonephritis

Antimicrobial peptides (AMPs) are key effectors of host defence, however, their functional deployment across renal tissue and urine in pyelonephritis (PN) remains incompletely understood. Here, we integrate kidney and urine proteomics with urinary peptidomics and computational prediction to define AMP organisation and function. Proteomic analysis indicated coordinated induction of multiple AMPs in infected kidneys. These patterns were recapitulated in the urinary proteome, where AMP abundance correlated with leukocyte counts. Multiplex immunofluorescence microscopy localised these AMPs to myeloid cells, identifying them as central effector sources. Importantly, analysis of the urine peptidome revealed multiple encrypted AMPs (EPs), which arise from proteolytic processing of precursor proteins. To systematically assess their relevance for host defence, we applied an ensemble of machine learning-based predictors to prioritise candidates with activity in the urinary environment. This approach identified several potential EPs, among which the S100A12-derived peptide Calcitermin was confirmed in patient urine. Furthermore, it exerts antibacterial activity against uropathogenic E. coli (UPEC) and modulates myeloid cell responses. Together, these findings define a coordinated and compartmentalised AMP defence programme in human PN that extends beyond increased peptide expression, highlighting EPs as functionally relevant effectors with therapeutic potential.

immunology↗

bioRxiv2025

PepGlider: Property Regularized VAE for interpretable and controllable peptide design

AO_SCPLOWBSTRACTC_SCPLOWComputational peptide design requires precise control over peptide properties that often exhibit complex correlations. Existing generative models for peptide design rely on simplistic discrete conditioning mechanisms rather than precise targeting of specific property values. We present PepGlider, a continuous property regularization framework that enables direct control over their specific values. The method achieves structured latent space with superior disentanglement quality and displays smooth property gradients along regularized dimension. In silico experimental results demonstrate that PepGlider enables independent control of naturally correlated properties, and supports both de novo generation and targeted optimization of existing peptides. PepGlider applied to antimicrobial peptide design allows generation of candidates with desired antibacterial activity profile and maintained low toxicity profile. Unlike existing approaches, PepGlider provides precise control over continuous property distributions while maintaining generation quality, thus offering a generalizable solution for therapeutic and materials applications requiring exact property specifications.

bioinformatics↗

bioRxiv2025

Engineering of interconnected microcapillary networks at the mesoscale via magnetic assembly of endothelial-cell 'seeds'.

Despite significant developments in endothelial-cell (EC) manipulation techniques, a proper in vitro model of a functional microvasculature with controlled local interconnectivity under well-defined global architecture is still lacking. Here, we report the generation of such controlled multi-scale vascular net-works via manipulation of tens of sprouting EC seeds. We exploit magnetic patterning to assemble EC-coated superparamagnetic microbeads into ordered arrays and establish effective growth rules gov-erning the development of interconnectivity and directionality of the networks depending on the applied seed-seed spacing. The EC-seed-based approach offers a range of advantages over conventional EC-manipulation techniques including: (i) expedited sprouting, (ii) spatial control over interconnections, (iii) reduction in cell consumption by >100x, and (iv) native high-throughput format. We co-develop multiparametric morphometric analysis tool and demonstrate high-content assessment of drug-induced vascular remodeling in 3D tumor microenvironments. Overall, we propose a uniquely precise and stand-ardized vascular-microtissue engineering tool with applications, e.g., in angiogenesis research, high-throughput drug testing including personalized therapies, and with possible extension to organ-on-chip and tissue-regenerative approaches.

bioengineering↗

bioRxiv2024

Hydrodynamic Radii of Intrinsically Disordered Proteins: Fast Prediction by Minimum Dissipation Approximation and Experimental Validation

The diffusion coefficients of globular and fully unfolded proteins can be predicted with high accuracy solely from their mass or chain length. However, this approach fails for intrinsically disordered proteins (IDPs) containing structural domains. We propose a rapid predictive methodology for estimating the diffusion coefficients of IDPs. The methodology uses accelerated conformational sampling based on self-avoiding random walks and includes hydrodynamic interactions between coarse-grained protein subunits, modeled using the generalized Rotne-Prager-Yamakawa approximation. To estimate the hydrodynamic radius, we rely on the minimum dissipation approximation recently introduced by Cichocki et al. Using a large set of experimentally measured hydrodynamic radii of IDPs over a wide range of chain lengths and domain contributions, we demonstrate that our predictions are more accurate than the Kirkwood approximation and phenomenological approaches. Our technique may prove valuable in predicting the hydrodynamic properties of both fully unstructured and multidomain disordered proteins. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/578612v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1e2943aorg.highwire.dtl.DTLVardef@7193c8org.highwire.dtl.DTLVardef@9d5bceorg.highwire.dtl.DTLVardef@f64f72_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Topological evolution of sprouting vascular networks: from day-by-day analysis to general growth rules.

Engineering tissues with an embedded vasculature of well-controlled topology remains one of the basic problems in biofabrication. Still, little is known about the evolution of topological characteristics of vascular networks over time. Here, we perform a high-throughput day-by-day analysis of tens of microvasculatures that sprout from endothelial-cell coated micrometric beads embedded in an external fibrin gel. We use the bead-assays to systematically analyze (i) macroscopic observables such as the overall length and area of the sprouts, (ii) microscopic observables such as the lengths of segments or the branching angles and their distributions, as well as (iii) general measures of network complexity such as the average number of bifurcations per branch. We develop a custom angiogenic image analysis toolkit and track the evolution of the networks for at least 14 days of culture under various conditions, e.g., in the presence of fibroblasts or with added endothelial growth factor (VEGF). We find that the evolution always consists of three stages: (i) an inactive stage in which cells remain bound to the beads, (ii) a sprouting stage in which the sprouts rapidly elongate and bifurcate, and (iii) the maturation stage in which the growth slows down. We show that higher concentrations of VEGF lead to an earlier onset of sprouting and to a higher number of primary branches, yet without significantly affecting the speed of growth of the individual sprouts. We find that the mean branching angle is weakly dependent on VEGF and typically in the range of 60-75 degrees suggesting that, by comparison with the available Laplacian growth models, the sprouts tend to follow local VEGF gradients. Finally, we observe an exponential distribution of segment lengths, which we interpret as a signature of stochastic branching at a constant bifurcation rate (per unit branch length). Our results, due to high statistical relevance, may serve as a benchmark for predictive models and reveal how the external means of control, such as VEGF concentration, could be used to control the morphology of the vascular networks. We provide guidelines for the fabrication of optimized microvasculatures with potential applications in drug testing or regenerative medicine.

bioengineering↗

bioRxiv2023

DNA supercoiling-induced shapes alter minicircle hydrodynamic properties

DNA in cells is organized in negatively supercoiled loops. The resulting torsional and bending strain allows DNA to adopt a surprisingly wide variety of 3-D shapes. This interplay between negative supercoiling, looping, and shape influences how DNA is stored, replicated, transcribed, repaired, and likely every other aspect of DNA activity. To understand the consequences of negative supercoiling and curvature on the hydrodynamic properties of DNA, we submitted 336 bp and 672 bp DNA minicircles to analytical ultracentrifugation (AUC). We found that the diffusion coefficient, sedimentation coefficient, and the DNA hydrodynamic radius strongly depended on circularity, loop length, and degree of negative supercoiling. Because AUC cannot ascertain shape beyond degree of non-globularity, we applied linear elasticity theory to predict DNA shapes, and combined these with hydrodynamic calculations to interpret the AUC data, with reasonable agreement between theory and experiment. These complementary approaches, together with earlier electron cryotomography data, provide a framework for understanding and predicting the effects of supercoiling on the shape and hydrodynamic properties of DNA.

biophysics↗

bioRxiv2022

The molecular fingerprint of stress resilience

Stress resilience is the ability of neuronal networks to maintain their function despite the stress exposure. Using a mouse model we here investigate stress resilience phenomenon. To assess the resilient and anhedonic behavioral phenotypes developed after the induction of chronic unpredictable stress, we quantitatively characterized the structural and functional plasticity of excitatory synapses in the hippocampus using a combination of proteomic, electrophysiological, and imaging methods. Our results indicate that stress resilience is an active and multifactorial process manifested by structural, functional, and molecular changes in synapses. We reveal that chronic stress influences palmitoylation of synaptic proteins, whose profiles differ between resilient and anhedonic animals. The changes in palmitoylation are predominantly related with the glutamate receptor signaling thus affects synaptic transmission and associated structures of dendritic spines. We show that stress resilience is associated with structural compensatory plasticity of the postsynaptic parts of synapses in CA1 subfield of the hippocampus. One Sentence SummaryCompensatory remodeling of dendritic spines at the structural and molecular levels underlies stress resilience.

neuroscience↗

bioRxiv2022

HydrAMP: a deep generative model for antimicrobial peptide discovery

Antimicrobial peptides emerge as compounds that can alleviate the global health hazard of antimicrobial resistance, prompting a need for novel computational approaches to peptide generation. Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. The model disentangles the learnt representation of a peptide from its antimicrobial conditions and leverages parameter-controlled creativity. HydrAMP is the first model that is directly optimized for diverse tasks, including unconstrained and analogue generation and outperforms other approaches in these tasks. An additional preselection procedure based on ranking of generated peptides and molecular dynamics simulations increases experimental validation rate. Wet-lab experiments on five bacterial strains confirm high activity of nine peptides generated as analogues of clinically relevant prototypes, as well as six analogues of an inactive peptide. HydrAMP enables generation of diverse and potent peptides, making a step towards resolving the antimicrobial resistance crisis.

bioinformatics↗