2025 Hyper Recent •CC0 1.0 Universal

This work is dedicated to the public domain. No rights reserved.

Access Preprint From Server
January 22nd, 2025
Version: 1
Saveetha University
bioinformatics
biorxiv

Unlocking Antiviral Potentials of Traditional Plants: A Multi-Method Computational Study against Human Metapneumovirus (HMPV)

Dubey, A.Open in Google Scholar•Kumar, M.Open in Google Scholar•Tufail, A.Open in Google Scholar•Dwivedi, V. D.Open in Google Scholar

Human metapneumovirus (HMPV) remains a critical challenge in respiratory healthcare, particularly due to the lack of targeted antiviral therapies and vaccines. This study employs an integrative computational framework to identify and evaluate the antiviral potential of nature-based compounds derived from traditional medicinal plants. A suite of advanced methodologies, including virtual screening, molecular docking, molecular dynamics (MD) simulations, density functional theory (DFT) calculations, pharmacophore modeling, and ADMET profiling, was utilized to comprehensively analyze candidate compounds. Among the tested compounds, Glycyrrhizin exhibited exceptional properties, with a binding energy of -65.4 kcal/mol, eight stabilizing hydrogen bonds, and remarkable dynamic stability (RMSD 1.3 Angstrom). Similarly, Withaferin A demonstrated a binding energy of -63.7 kcal/mol and high pharmacokinetic potential. Quantum-level analyses revealed favorable electronic properties, while ADMET profiling confirmed the compounds safety and drug-like characteristics. These findings underscore the potential of traditional phytochemicals to serve as lead candidates in antiviral drug development. This research bridges the gap between traditional medicine and modern computational techniques, paving the way for innovative and efficient therapeutic strategies against HMPV.

Similar Papers

biorxiv
Wed Jul 02 2025
hoodscanR: profiling single-cell neighborhoods in spatial transcriptomics data
Understanding complex cellular niches and neighborhoods have provided new insights into tissue biology. Thus, accurate neighborhood identification is crucial, yet existing methodologies often struggle to detect informative neighborhoods and generate cell-specific neighborhood profiles. To address these limitations, we developed hoodscanR, a Bioconductor package designed for neighborhood identifica...
Liu, N.
•
Martin, J.
•
Bhuva, D. D.
•
Chen, J.
...•
Davis, M. J.
biorxiv
Wed Jul 02 2025
Direct RNA sequencing (RNA004) allows for improved transcriptome assessment and near real-time tracking of methylation for medical applications
Direct RNA sequencing (DRS) is a nanopore-based technique for analyzing RNA in its native form, promising breakthroughs in diagnostics and biomarker development. Coupled to RNA002 sequencing chemistry, its clinical implementation has been challenging due to low throughput, low accuracy, and lack of large-scale RNA-modification models. In this study, we evaluate the improvements achieved by pairing...
Hewel, C.
•
Wierczeiko, A.
•
Miedema, J.
•
Hofmann, F.
...•
Gerber, S.
biorxiv
Wed Jul 02 2025
MOTL: enhancing multi-omics matrix factorization with transfer learning
Joint matrix factorization is popular for extracting lower dimensional representations of multi-omics data but loses effectiveness with limited samples. Addressing this limitation, we introduce MOTL (Multi-Omics Transfer Learning), a framework that enhances MOFA (Multi-Omics Factor Analysis) by inferring latent factors for small multi-omics target datasets with respect to those inferred from a lar...
Hirst, D.
•
Terezol, M.
•
Cantini, L.
•
Villoutreix, P.
...•
Baudot, A.
biorxiv
Wed Jul 02 2025
MORPH Predicts the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities
Modeling cellular responses to genetic perturbations is a significant challenge in computational biology. Measuring all gene perturbations and their combinations across cell types and conditions is experimentally challenging, highlighting the need for predictive models that generalize across data types to support this task. Here we present MORPH, a MOdular framework for predicting Responses to Per...
He, C.
•
Zhang, J.
•
Dahleh, M. A.
•
Uhler, C.
biorxiv
Wed Jul 02 2025
Inferring metabolite states from spatial transcriptomes using multiple graph neural network
Metabolism serves as the pivotal interface connecting genotype and phenotype in various contexts, such as cancer reprogramming and immune metabolic reprogramming. Compared to the transcriptome, the development of the single-cell metabolome faces significant challenges. While various methods exist for predicting metabolite levels from transcriptome, their efficacy remains limited. We developed an e...
Jiaxu, L.
•
Daosheng, A.
•
Sun, W.
biorxiv
Wed Jul 02 2025
A systematic assessment of phylogenomic approaches for microbial species tree reconstruction
A key challenge in microbial phylogenomics is that microbial gene families are often affected by extensive horizontal gene transfer (HGT). As a result, most existing methods for microbial phylogenomics can only make use of a small subset of the gene families present in the microbial genomes under consideration, potentially biasing their results and affecting their accuracy. To address this challen...
Weiner, S.
•
Feng, Y.
•
Gogarten, J. P.
•
Bansal, M. S.
biorxiv
Wed Jul 02 2025
Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings
Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, high-dimensional single-cell data. Neighbor embedding (NE) algorithms, such as t-SNE and UMAP, are widely used to embed hi...
Ma, R.
•
Li, X.
•
Hu, J.
•
Yu, B.
biorxiv
Wed Jul 02 2025
Confidence: A Web App for Cross-Platform Differential Gene Expression Analysis, Gene Scoring, and Enrichment Analysis
RNA sequencing (RNA-seq) is used to quantify transcript levels through measurement of nucleotide sequences. To evaluate statistically significant changes in gene expression, transcript counts between samples are compared using differential expression analysis methods. However, three of the most pressing challenges in transcriptomics analyses are: 1) analytical packages produce a distinct number of...
Shastry, A.
•
Ott, B.
•
Paterson, A.
•
Simpson, M.
...•
Hindmarch, C. C. T.
biorxiv
Wed Jul 02 2025
nf-core/viralmetagenome: A Novel Pipeline for Untargeted Viral Genome Reconstruction
Motivation: Eukaryotic viruses present significant challenges for genome reconstruction and variant analysis due to their extensive diversity and potential genome segmentation. While de novo assembly followed by reference database matching and scaffolding is a commonly used approach, the manual execution of this workflow is extremely time-consuming, particularly due to the extensive reference cura...
Klaps, J.
•
Lemey, P.
•
nf-core community,
•
Kafetzopoulou, L. E.
biorxiv
Wed Jul 02 2025
Machine learning-based structural classification of lytic polysaccharide monooxygenases
Lytic polysaccharide monooxygenase (LPMO) is a copper-dependent redox enzyme and according to CAZy is classified either as cellulolytic or chitinolytic. According to CAZy, there are eight families of LPMO namely AA9, AA10, AA11, AA13, AA14, AA15, AA16, and AA17, where AA stands for Auxiliary Activity. Previously, using the sequence information machine learning-based functional annotation was succe...
Manikandan, A.
•
Yennamalli, R. M.