Biology subjects

Dunphy, L.

Publications and source records attributed to Dunphy, L..

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

Image-based Morphological Profiling Reveals Signatures of Radiation Exposure

Exposure to ionizing radiation has the potential to induce significant health risks including radiation sickness and death. Here, the utility of image-based morphological profiling (IBMP) assays was investigated as a method to visualize signatures of radiation over time. Human-derived fibroblasts were used as the model system, and were exposed to varying doses of radiation. Cell Painting protocols were then applied to generate images for profiling. Quantitative analysis of images taken from fibroblasts exposed to 1 Gray of ionizing radiation revealed considerable morphological changes by 24 hours post-exposure, with some morphological signs of exposure emerging as early as 4 hours post-exposure. This work demonstrates proof-of-concept for the use of cell painting and IBMP to visualize signatures of radiation, paving the way for its use as a tool for assessing repeated exposures, screening of potential treatments, and establishing relevant timepoints for downstream orthogonal assays.

cell biology↗

bioRxiv2024

DeepPaint: A deep-learning package for Cell Painting Image Classification

Recent developments in the high-content imaging (HCI) space have allowed for the production of large Cell Painting datasets. These datasets are typically derived from cells exposed to a set of biological perturbants including proteins, small molecules, or even pathogens. While the method of Cell Painting has shown utility for drug discovery and hazard evaluation purposes, traditional analyses pipelines applied Cell Painting datasets typically require the segmentation of single cells from thousands to millions of images, a process that is time-consuming and subject to noise and experimental variability. Here we present DeepPaint, a Python package that uses a deep learning framework to perform image analysis of cell painting images including treatment classification and latent space analysis, circumventing the need for image segmentation. DeepPaint is easily tunable to different HCI setups and datasets and can be applied to classify broad types of biological perturbations. Here we demonstrate that DeepPaint can generate highly accurate neural networks for binary and multiclass classification of cell painting images. The DeepPaint package and example notebooks are freely available at https://github.com/jhuapl-bio/DeepPaint.

bioinformatics↗