Comput Biol Med. 2025 Dec 7;200:111372. doi: 10.1016/j.compbiomed.2025.111372. Online ahead of print.

ABSTRACT

The emergence of monkeypox virus (MPXV) as a global health threat has necessitated the rapid identification of novel antiviral therapeutics. Currently, no FDA-approved drugs are specifically designed against the disease. We used an in-house deep learning pharmacophore model for screening a library of 1974 FDA-approved drugs targeting the active site of MPXV DNA polymerase. Three drugs exhibited the strongest binding affinities, outperforming the control drug, Cidofovir diphosphate, and forming stable interactions with key active site residues. Among them, Paromomycin emerged as the most favourable drug, demonstrating stable, persistent, and adaptable interactions in molecular dynamics simulation. In parallel, we developed a novel automated peptide-generating AI pipeline that integrates active-site residues with knowledge-guided amino acid selection to generate and evaluate synthetic peptides. Cysteine-Phenylalanine-Cysteine (CFC), together with a panel of candidates, emerged through rational balancing of physicochemical properties and drug-likeness for accelerated therapeutic discovery. Synthetic peptides were evaluated to further understand the binding efficacies with DNA polymerase. CFC peptide demonstrated strong binding affinity (-8.08 kcal/mol) through stable interactions with key catalytic residues ASP549, ARG634 and LYS661, while MMGBSA analysis confirmed favourable binding energy (-33.02 kcal/mol). Consistent results in MD simulations indicate functional binding without destabilisation. Although ADMET predictions for CFC revealed limitations in permeability and oral bioavailability, its favourable binding profile and reduced predicted toxicity support its potential as a novel antiviral lead.

PMID:41360016 | DOI:10.1016/j.compbiomed.2025.111372

Int J Mol Sci. 2025 Nov 13;26(22):11005. doi: 10.3390/ijms262211005.

ABSTRACT

Type 2 diabetes mellitus (T2DM) is a prevalent and complex metabolic disorder characterized by insulin resistance, progressive β-cell dysfunction, and severe systemic complications. Advances in single-cell multi-omics-transcriptomics, chromatin accessibility profiling, and integrative analyses-have offered unprecedented insights into the cellular heterogeneity and regulatory networks of pancreatic islets. We highlight recent discoveries in islet cell heterogeneity and β-cell pathophysiology, with a particular focus on dysfunction and dedifferentiation. We further underscore the computational frameworks that enable these discoveries, spanning data preprocessing, multi-omics integration, and machine learning-driven analyses, which collectively enable the dissection of disease-relevant cell subpopulations and the reconstruction of developmental and regulatory trajectories. We also examine how impaired signaling within islets and chronic adipose inflammation contribute to T2DM pathogenesis. Finally, we discuss key challenges in clinical translation-including limited population diversity in single-cell atlases and the interpretability of computational models-and propose future directions toward precision diagnostics and therapeutic innovation in T2DM.

PMID:41303487 | PMC:PMC12652634 | DOI:10.3390/ijms262211005

Nat Commun. 2025 Nov 26. doi: 10.1038/s41467-025-66644-5. Online ahead of print.

ABSTRACT

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.

PMID:41298467 | DOI:10.1038/s41467-025-66644-5