Biology subjects

Geng, P. X.

Publications and source records attributed to Geng, P. X..

3 recordsLinked to original sources

bioRxiv2025

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotics targets and metabolic engineering. Unlike single genes or gene pairs, three-gene combinations involve more intricate interactions, making experimental screening time-consuming. Computational methods, particularly Genome-scale metabolic Model (GEM)-based Flux Balance Analysis (FBA), requires constructing new GEMs from experimental data, limiting its use for new species. Moreover, using FBA for three-gene knockout screening could take several years. Therefore, a faster and GEMs-independent approach is needed to facilitate genome-wide three-gene knockout screening. Here, we introduce Tripleknock, for predicting the lethal effects of three-gene knockouts. Tripleknock was trained using whole-genome data from Escherichia coli K-12 MG1655, and three-gene knockout simulations using FBA. The model uses a threshold of 90% reduction in cell growth to define lethal effect as the prediction output. Compared to FBA, Tripleknock achieves predictions approximately 20 times faster, reaching an average cross-species F1 score of 0.77 on six pathogenic species within the Enterobacteriaceae family. For closely related species such as pathogenic E. coli and Shigella, Tripleknock reaches F1 scores exceeding 0.83. To our knowledge, Tripleknock is the first end-to-end model for predicting lethal effects of three-gene knockout in bacteria. Data availabilityTripleknock is publicly available at: https://github.com/Peneapple/Tripleknock

bioinformatics↗

bioRxiv2025

Developing Foundation Models for Predicting Viral Animal Host Range in Intelligent Surveillance

Emerging human infectious viruses originating from animals continue to pose a persistent threat to global public health. Understanding the host range of animal viruses is crucial for identifying potential spillover pathways and mitigating the risk of future pandemics. Here, we present VirHRanger, a prediction method that integrates foundation models trained on viral genome and protein sequences, alongside genomic and protein compositional traits, viral phylogeny, and protein-protein interactions. To systematically predict the animal host range, VirHRanger incorporates host taxonomy-aware neural networks trained on a comprehensive collection of animal-virus associations spanning mammals, birds, and arthropods. Within a dataset of 4,006 virus species spanning 99 viral families, our model achieved robust performance with a micro-averaged AUROC of 0.938 across all host categories, demonstrating its effectiveness in capturing generalizable host signals from viral genetic data. On a dataset of 315 novel viruses, which are associated with key reservoir animal hosts and insect vectors, VirHRanger notably outperformed the homology-based method, exhibiting a strong generalizability to novel viruses. Furthermore, VirHRanger identified host range variations among closely related viruses within the Coronaviridae family and successfully predicted the ability of SARS-CoV-2 to infect humans and other animal hosts. These findings highlight the potential of VirHRanger to transform sequencing data into timely insights for disease control during the early stages of zoonotic outbreaks.

bioinformatics↗

bioRxiv2024

Characterizing the Binding Mechanism of Sparsentan to the Type-2 Angiotensin II Receptor Using AutoDock Vina

The angiotensin II type-2 receptor (AT2R) is known to have a significant impact on cardiovascular physiology, and the elucidation of its drug interactions is crucial for advancing therapeutic interventions. Sparsentan is an antagonist of angiotensin II, which has received FDA approval due to its demonstrated efficacy in ameliorating proteinuria. While sparsentan exhibits a preferential affinity for angiotensin II type-1 receptor (AT1R) and may primarily target it, DrugBank identifies it as a drug that targets AT2R. Present databases lack detailed structural data on the interaction mechanism between sparsentan and AT2R, leaving no definitive evidence to confirm or refute an interaction with AT2R. This study aimed to explore the potential for an interaction between sparsentan and AT2R. Molecular docking simulations were conducted using AutoDock Vina to predict the binding conformation. The docking parameters were meticulously optimized to ensure the accuracy and reliability of the simulation results. Subsequently, the most stable sparsentan-AT2R complex was determined. We identified four primary types of interactions at the binding site containing hydrogen bonds, hydrophobic interactions, {pi}-cation interactions, and {pi}-stacking, that are likely to contribute to the affinity of sparsentan for AT2R. Our study indicates that sparsentan may potentially interact with AT2R, our results may serve as a valuable reference for elucidating the mechanism of sparsentan action and for guiding the design of AT2R-targeted drugs.

biophysics↗