Biology subjects

Berman, D. S.

Publications and source records attributed to Berman, D. S..

3 recordsLinked to original sources

bioRxiv2026

Fung-AI: An AI/ML-driven pipeline for antifungal peptide discovery

Emerging fungal pathogens represent a concerning threat to both global health and food security. In this study, we aimed to address our rising vulnerability to fungal pathogens through the development of the Fung-AI pipeline: an AI/ML-driven approach for antifungal discovery. A generative adversarial network (GAN) was trained to generate novel candidate antifungal peptide sequences. Next, in silico antifungal and hemolytic classifiers were built to further prioritize AI-generated peptides for experimental validation. From a pool of [~]10,000 candidates, thirteen peptides were selected for testing over two-stages of experimentation. Five peptides were found to display mild antifungal activity against the wheat pathogen, Fusarium graminearum, with minimal inhibitory concentrations (MICs) ranging from 250 {micro}g/mL to 500 {micro}g/mL. Four of the five peptides also showed activity against the human pathogen, Candida albicans (MIC: 500 {micro}g/mL). Two of our AI-generated antifungal peptides additionally demonstrated low cytotoxicity in HepG2 human liver carcinoma cells (LC50 > 704.2 {micro}g/mL) indicating that they may be useful as scaffolds for future optimization for therapeutic applications. None of our peptides were found to considerably inhibit the emerging pathogen C. auris, suggesting the need for pathogen-specific down-selection of candidate peptides. Overall, we present a proof-of-principle, generative-AI-based approach for the rapid design of de novo antifungal peptides.

synthetic biology↗

bioRxiv2025

An Improved Systematic Method for Constructing ecGEMs using a Protein-Chemical Transformer

Enzyme-constrained genome-scale metabolic models (ecGEMs) have improved Flux Balance Analysis (FBA) by incorporating enzyme turnover numbers (kcats). Since in-vivo kcat data is costly to obtain and therefore scarce, we present a novel multi-modal transformer-based approach with cross-attention to predict kcat values for Escherichia coli using enzyme amino acid sequences and SMILES annotations of reaction substrates. For heteromeric enzymes, we evaluate multiple subunit kcat aggregation strategies. We benchmark ecGEMs constructed with these strategies against current state-of-the-art models using experimental growth rates, 13C fluxes, and enzyme abundances, and prior to any calibration outperform or match existing methods. We also devise a new calibration method using flux control coefficients (derivatives of log flux with respect to log kcat), which we show to be identical to enzyme cost at the FBA optimum. Using these coefficients, we identify 8 key kcat values to recalibrate using experimental data, subsequently achieving superior performance to the current state-of-the-art with 81% fewer calibrations.

systems biology↗

bioRxiv2024

Alterations in genes associated with cytosolic RNA sensing in whole blood are associated with coronary microvascular disease in SLE

ObjectiveTo investigate whether gene signatures discriminate systemic lupus erythematosus (SLE) patients with coronary microvascular dysfunction (CMD) from those without and whether any signaling pathway is linked to the underlying pathobiology of SLE CMD. MethodsThis study collected whole blood RNA samples from female subjects aged 37 to 57, comprising 11 SLE patients (4 SLE-CMD, 7 SLE-non-CMD) and 10 HC. Total RNA was then used for library preparation and sequencing. Differential gene expression analysis was performed to identify gene signatures associated with CMD in SLE patients using DEseq2 v1.42.0. Gene Set Enrichment Analysis were performed by ClusterProfiler v4.10.0 and pathfindR v2.3.1. ResultsRNA-seq analysis revealed 143 differentially expressed (DE) genes between the SLE and HC groups. GO analysis indicated associations with virus defense and interferon signaling in SLE. 14 DE genes were identified from comparison between SLE-CMD and SLE-non-CMD with adjusted parameters (padj < 0.1). Notably, SLE-CMD exhibited elevated levels of genes associated with RNA sensing, while downregulated genes in SLE-non-CMD were associated with blood coagulation and cell-cell junction. Further investigation highlighted differences in IFN signaling and ADP-ribosylation pathways between SLE-CMD and SLE-non-CMD, suggesting distinct molecular mechanisms underlying vascular changes in CMD and reduced left ventricular function in non-CMD. ConclusionOur study identified a unique gene signature in SLE-CMD compared to the HC group, highlighting the significant involvement of type 1 interferon, RIG-I family proteins, and chronic inflammation in the progression of SLE-CMD. The intricate relationship between SLE-CMD and these factors underscores their probable role in initiating and advancing SLE-CMD.

bioinformatics↗