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        <title>Frontiers in Bioinformatics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics</link>
        <description>RSS Feed for Frontiers in Bioinformatics | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-08-07T22:23:32.96+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1887419</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1887419</link>
        <title><![CDATA[AI-enhanced virtual screening identifies a potent small-molecule modulator of ClC-3 for cervical cancer drug discovery]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chao Liu</author><author>Chongxing Ji</author>
        <description><![CDATA[ClC-3 chloride channels play essential roles in cervical cancer progression by regulating lysosomal acidification, cell volume homeostasis, and chemoresistance. However, no highly selective small-molecule modulators of ClC-3 have been reported to date. Motivated by the urgent clinical need to reverse ClC-3-mediated chemoresistance and the challenge of processing massive chemical libraries under limited computational hardware resources, we propose a novel AI-driven Drug Discovery (AIDD) pipeline. Here, we present an integrated virtual drug discovery framework that transitions from traditional Computer-Aided Drug Design (CADD) by combining large-scale molecular docking, deep-learning-based rescoring, pharmacokinetic filtering, and atomistic molecular dynamics (MD) simulations. The primary advantage of this proposed scheme lies in the integration of GNINA 3D-convolutional neural network (CNN) rescoring, which significantly reduces the false-positive rates inherent in empirical scoring functions for membrane proteins. A library of ∼180,000 ZINC15 compounds was initially screened using AutoDock Vina, followed by GNINA convolutional neural network rescoring to refine predicted binding affinity and pose confidence. ADMET profiling further narrowed the candidates, providing computational proof of drug-likeness and toxicity criteria rather than experimental validation. Ultimately, only ZINC000001556308 (Lig8) satisfied all in silico criteria. To validate binding stability, we performed 100-ns all-atom MD simulations of the ClC-3–Lig8 complex embedded in a lipid bilayer. Lig8 induced reduced RMSD fluctuations, lower RMSF values across key transmembrane helices, and a more compact radius of gyration, indicating enhanced structural stabilization of ClC-3. MM/PBSA calculations confirmed favorable binding energetics dominated by van der Waals interactions, while per-residue decomposition identified PHE527, GLY283, and GLY584 as major contributors to ligand recognition. These results reveal a previously uncharacterized binding pocket within the ClC-3 transmembrane domain and highlight Lig8 as a promising lead compound for targeting ClC-3-mediated oncogenic signaling. Overall, this study establishes the first comprehensive computational framework for ClC-3 modulator discovery and provides a validated chemical scaffold for future therapeutic development against cervical cancer.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1911554</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1911554</link>
        <title><![CDATA[Integrated analysis of enzymes, mRNAs, and miRNAs provides insight into the regulatory potential of extracellular vesicles in recipient cell glucose metabolism]]></title>
        <pubdate>2026-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Namita N. Kashyap</author><author>Sharath Mohan Bhat</author><author>Padmanabha Udupa E. G.</author><author>Kavitha S. Shettigar</author><author>Vinutha R. Bhat</author><author>Dinesh Upadhya</author>
        <description><![CDATA[IntroductionExtracellular vesicles (EVs) are increasingly recognized as active coordinators of metabolic processes rather than mere messengers. By carrying unique subsets of enzymes, metabolites, lipids, and nucleic acids, EVs can directly deliver functional metabolic machinery or dynamically alter intracellular metabolic fluxes in recipient cells. However, their role in regulating specific biochemical pathways remains largely unknown.MethodologyIn the current in silico analysis, we explored the dominant metabolic role of EV cargo using publicly available multi-omics data. For this, the top 500 mRNAs and proteins, along with miRNAs reported at least 10 times in humans across independent studies, as catalogued in the EVpedia database are considered and curated into a comprehensive dataset.ResultsEnrichment analysis of these mRNAs and proteins revealed that carbohydrate metabolic pathways, including glycolysis, the pentose phosphate pathway and the TCA cycle, were over-represented in EVs. Further, to investigate whether EVs carry miRNAs that regulate these pathways, we analyzed the miRNA targets. Enrichment analysis of EV miRNA targets mapped glycolytic regulatory genes, including HK1, HK2, PFKP and PKM. Interestingly, we also found miRNAs targeting genes encoding glucose transporters (SLC2A1, SLC2A3, SLC2A4, and SLC2A14) reported in EVs. Genomic annotation of these miRNAs revealed them to form clusters, including the miR-17-92 cluster, a well-known regulator of glycolysis.DiscussionWhile the study has limitations—mainly due to the biological heterogeneity of EVs and the difficulty of standardizing cargo—the results potentially suggest that, by delivering enzymes, their mRNAs, regulatory miRNAs or combinations thereof, EVs could potentially mediate recipient cell glucose metabolism.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1832826</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1832826</link>
        <title><![CDATA[Spatial transcriptomics reveal heterogeneous cell‒cell interactions among brain regions in cuprizone model consistent with multiple sclerosis lesions]]></title>
        <pubdate>2026-08-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hui-Hsin Tsai</author><author>Sarbottam Piya</author><author>Jing Wang</author><author>Jing Zhu</author><author>Wenxing Hu</author><author>Andrew R. Gehrke</author><author>Shaolong Cao</author><author>Amanda J. Guise</author><author>Su Jing Chan</author><author>Mark Sheehan</author><author>Jenhwa Chu</author><author>Zhengyu Ouyang</author><author>Matthew Ryals</author><author>Michelle Lee</author><author>Wanli Wang</author><author>Edward Zhao</author><author>Patrick Cullen</author><author>Ravi Challa</author><author>Eric Marshall</author><author>Wanyong Zeng</author><author>Yea Jin Kaeser-Woo</author><author>Chris Ehrenfels</author><author>Luke Jandreski</author><author>Helen McLaughlin</author><author>Thomas M. Carlile</author><author>Jake Gagnon</author><author>Taylor L. Reynolds</author><author>Mingyao Li</author><author>Kejie Li</author><author>Baohong Zhang</author>
        <description><![CDATA[The cuprizone (CPZ) model is widely used for modeling demyelination in multiple sclerosis (MS) and for testing potential remyelination therapies. To better understand the underlying pathology of the CPZ model and evaluate its translatability, we integrated single-cell and spatial transcriptomics (ST) to investigate spatial cellular and molecular interactions during de- and remyelination in multiple brain regions. ST revealed global demyelination and neuroinflammation in the brain beyond the corpus callosum (CC), with region-specific differences. We identified oligodendroglia and microglia as two major cell types with significant transcriptomic changes in the model. CPZ-associated subclusters of oligodendroglia (marker genes Arap2, Dock10, Tenm4, Pex5l and Dock1) and microglia (marker genes ApoE, Axl, Cd9 and Lpl) were mapped to the CC by ST. During remyelination, while mature oligodendrocytes (MOL) nearly reversed their phenotype back to the control state, microglia remained associated with the demyelination phenotype. Ligand‒receptor (LR) pairing analyses predicted growth factor and phagocytic pathway enrichment during demyelination, which is consistent with changes in MS lesions, and microglia were predicted to be the major sender cells. LR pairing also predicted a high likelihood of interaction between oligodendroglia and microglia, and a novel interaction between MOL and oligodendrocyte precursor cells (OPC), underscoring their roles during de- and remyelination. Finally, astrocytes in the CPZ model had the greatest preservation of disease-associated modules in MS lesions, while MOL, OPC, and microglia showed moderate to low preservation, which overall suggests that the CPZ model has moderate translatability to chronically active MS lesions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1894439</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1894439</link>
        <title><![CDATA[Decoding the hypoxic injury landscape and hypoxic risk model construction in diabetic kidney disease: a multi-omics study]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Li Jiang</author><author>Chien Chieh</author><author>Haojun Zhang</author><author>Tingting Zhao</author><author>Xiai Wu</author>
        <description><![CDATA[ObjectivesTo identify the core hypoxic injury pattern of DKD, construct a DKD risk model based on hypoxic injury-related (HIR) score, and explore the potential therapeutic targets of DKD.MethodsDKD-related microarray-based transcriptomic analyses, single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics were retrieved from the Gene Expression Omnibus (GEO) database. Seven HIR gene sets were obtained from various public databases. Core hypoxic genes were identified using different machine-learning algorithm. LASSO and nomogram were applied to construct a HIR risk score for cellular hypoxic damage. The detailed expression of hub gene would be showed in the single cell and kidney region. The prognostic value of the HIR score was externally validated using plasma proteomics from the United Kingdom Biobank.ResultsFive core hypoxic injury pathways in DKD were identified: Hypoxia, Autophagy, Ferroptosis, Endoplasmic Reticulum (ER) Stress, and Apoptosis. The HIR risk score was constructed based on three hub genes: CASP3, DUSP1, and ZFP36. The HIR score demonstrated high diagnostic efficiency for DKD patients. Higher HIR scores were associated with significantly infiltrated immune cells and poorer kidney function. In United Kingdom Biobank validation, the HIR score significantly improved the prediction of kidney outcomes, renal death, and secondary endpoints beyond demographic and metabolic variables (AUC increments 0.04–0.06), and correlated negatively with eGFR and positively with lipoprotein(a). The calculated tissue-level HIR scores also showed a highly significant and robust increase in the renal microenvironment of BTBR ob/ob mice.ConclusionThese results provided a predictive model for clinical evaluation in patients with DKD and also a new insight into the role of HIR genes in the pathogenesis of DKD.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1827877</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1827877</link>
        <title><![CDATA[SiaRNA: a siamese neural network with bidirectional cross-attention for pairwise siRNA-mRNA efficacy prediction]]></title>
        <pubdate>2026-08-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vaishnavi Sapireddy</author><author>Rajkumar Nathi</author><author>Venkata Harshit Meruva</author><author>Varun Raju Nannapuraju</author><author>Bhargava Chary Basangari</author><author>Vani Kondaparthi</author>
        <description><![CDATA[IntroductionSmall interfering RNA (siRNA) therapeutics have extraordinary potential for targeted gene silencing. They mediate post-transcriptional gene regulation by binding to complementary messenger RNA (mRNA) sequences and degrading them, thereby preventing the production of unwanted proteins. Recent machine learning and deep learning frameworks for predicting siRNA efficacy have only achieved moderate success as these models solely rely either on handcrafted features or on sequential relations and therefore cannot capture the full complexity of siRNA-mRNA interactions.MethodsIn the above context, we propose SiaRNA, which uses a Siamese Neural Network for feature-derived representations and a bidirectional cross-attention mechanism for sequence-level relationships. It uniquely identifies mRNAs and their corresponding siRNAs as paired entities, allowing unified and context-aware modeling. Unlike previous models, which discard 2-nucleotide (2-nt) overhangs at the 3′ end while using 21-nt efficacy labels, SiaRNA both trains and tests on 21-nt sequences to ensure biologically consistent predictions.ResultsOur model sets a new performance benchmark, outperforming previous state-of-the-art models. SiaRNA is trained on the HUVK dataset, achieving an accuracy of 0.881, and its generalization is confirmed by testing on the independent Simone dataset.DiscussionThe results prove SiaRNA’s potential as a reliable and biologically accurate framework to guide siRNA design and improve therapeutic outcomes. On performing a case study using Patisiran siRNA and its target transthyretin (TTR) mRNA, an efficacy value of 0.7134 was observed indicating that our model can successfully identify therapeutically effective targets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1883130</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1883130</link>
        <title><![CDATA[MultiCausGRN: directed prior-guided graph attention model for multi-omics gene regulatory network inference]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Noor Jamal Alkhateeb</author><author>Mamoun Awad</author>
        <description><![CDATA[IntroductionExisting methods for gene regulatory network (GRN) inference rely primarily on gene expression data alone or on lower-resolution bulk sequencing data. Despite recent advances in integrating chromatin accessibility and RNA sequencing, inferring GRNs from paired single-cell multi-omics data remains challenging due to noise, sparsity, and complex nonlinear regulatory relationships.MethodsWe present MultiCausGRN, a graph attention network (GAT)-based framework for GRN inference from paired scRNA-seq and scATAC-seq data. The model incorporates directed prior-guided graph attention learning to capture biologically grounded regulatory directionality by integrating curated directed regulatory edges into graph representation learning. MultiCausGRN performs supervised transcription factor–target link prediction using integrated multi-omics features within a two-layer graph attention architecture.ResultsOn the human PBMC multi-omics dataset, prior knowledge integration improved predictive stability and achieved a mean test AUPRC of 0.743 ± 0.049 and a mean AUROC of 0.682 ± 0.026 across five independent random seeds.DiscussionThese results demonstrate that directed prior-guided graph learning can improve the robustness and biological interpretability of GRN inference in data-limited settings. MultiCausGRN is publicly available at: https://github.com/nrr-90/MultiCausGRN.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1882476</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1882476</link>
        <title><![CDATA[LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug–target interaction prediction under multi-scenario cold-start settings]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuai Guo</author><author>Weichi Liu</author><author>Jie Zou</author><author>Tao Ban</author><author>Gaifang Dong</author>
        <description><![CDATA[Computational drug–target interaction (DTI) prediction provides a scalable alternative to costly and time-consuming experimental screening, but its reliability is limited by the scarcity of experimentally verified negative interactions. In public DTI databases, most unobserved drug–target pairs are unlabeled rather than true non-interactions. Randomly treating these unlabeled pairs as negatives can introduce label noise and reduce model reliability, particularly in cold-start scenarios involving unseen drugs or targets. To address this issue, we propose LNMGAT, a LapRLS-guided reliable pseudo-negative mining framework coupled with dual graph attention encoders. Instead of relying on experimentally confirmed negative labels or randomly sampled negatives, LNMGAT first applies Laplacian regularized least squares to drug and target similarity graphs to identify low-confidence unlabeled pairs as reliable pseudo-negatives. Drug and target representations are then learned separately on similarity-based k-nearest-neighbor graphs using graph attention networks, and their embeddings are concatenated for MLP-based interaction prediction. Across Yamanishi, Davis, KIBA, and BindingDB benchmarks, LNMGAT achieved the best AUPR in 10 of 16 evaluation settings and ranked within the top two in 14 of 16 settings. In the 12 cold-start settings, LNMGAT obtained the best AUPR in 9 cases, with absolute AUPR gains over the strongest baseline of up to 0.016 in pair cold-start prediction. External evaluation on DrugBank positive interactions and SwissDock-based molecular docking further provided database-level and in silico support for the plausibility of high-ranked predictions. Nevertheless, the biological validation in this study remains computational and database-based; no wet-lab binding assay was performed. LNMGAT therefore provides a competitive and interpretable framework for DTI prediction under negative-label uncertainty, while further experimental validation is required for its top-ranked candidates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1821527</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1821527</link>
        <title><![CDATA[Molecular docking and dynamics simulation studies uncover the host–pathogen protein–protein interactions in soybean (Glycine max (L.) Merr.) and Groundnut bud necrosis virus: first report]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rayees Ahmad Bhat</author><author>Ayyagari Ramlal</author><author>Amooru Harika</author><author>Ashutosh Sharma</author><author>Ambika Rajendran</author><author>Iten M. Fawzy</author><author>Sreeramanan Subramaniam</author><author>Dhandapani Raju</author><author>S. K. Lal</author><author>Sonu Krishankumar</author><author>Shyam S. Kurup</author>
        <description><![CDATA[BackgroundSoybean is a widely consumed oilseed crop with economic importance. It is enriched with numerous bioactive compounds that have health-promoting properties. Groundnut bud necrosis virus (GBNV) affects many agriculturally important crops, such as soybean, resulting in crop losses. GBNV is a single-stranded RNA virus from the genus Orthotospovirus and family Bunyaviridae. It is transmitted by insect vectors (thrips) and is further aggravated by secondary transmission from infected plants to others in the same field. There are no commercially available drugs against GBNV; thus, there is a need to explore phytomolecules that control the infection. This study utilizes soybean proteins (CAM, CDK1, Cul1, GSK3, HSP70, LOX, PCNA, and TCP) and GBNV proteins (CP, EGP, MP, NSP, NS, and RDRP) for the analysis of their inhibitory interactions. Physicochemical properties for both protein groups were examined. The molecular basis for the selective predictive association of these protein interactions was evaluated using in silico molecular docking approaches, dynamics simulations analysis, and post-simulation computational analyses.ResultsThe results indicate that among these 50 protein–protein interactions, heat shock protein 70 (HSP70) exhibited potential inhibitory action against the RNA-dependent RNA polymerase (RDRP) with −12.12 kcal/mol binding affinity (z-score: 0.0). RDRP is an essential enzyme required for the transcription and replication of the viral genome. HSPs function in both abiotic and biotic stresses. Although further studies are required, these preliminary findings will be useful for developing new therapeutic agents against the virus, thus paving the way for researchers to find better alternatives.ConclusionThis is the first study to combine prediction, structural validation, and interface analysis of the interaction between soybean and GBNV proteins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1902380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1902380</link>
        <title><![CDATA[A linked independent component analysis framework for characterizing site-effect patterns in multi-site structural and functional MRI]]></title>
        <pubdate>2026-07-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Huashuai Xu</author><author>Yuge Xing</author><author>Weiya Guo</author>
        <description><![CDATA[IntroductionLarge-scale multi-site magnetic resonance imaging (MRI) improves population coverage and statistical power, but scanner- and protocol-related variability can obscure biological effects. Most harmonization methods aim to reduce site-related variance for downstream analysis, whereas less attention has been paid to where site effects are spatially expressed, whether they are reproducible across site compositions, and which acquisition parameters contribute to them.MethodsWe developed a modality-wise Linked Independent Component Analysis (LICA) framework to identify and interpret site-effect patterns in structural and resting-state functional MRI. Grey matter (GM) volume, amplitude of low-frequency fluctuation (ALFF), and regional homogeneity (ReHo) maps were analyzed separately. For each imaging measure, LICA decomposed voxel-wise maps into spatial components and subject-level loadings. Components were classified according to their associations with site labels and biological covariates, their spatial reproducibility was assessed using stepwise site-inclusion analyses, and their technical attribution was evaluated using cross-validated models based on site labels and recorded acquisition parameters. The framework was applied to ABIDE II GM maps from 913 participants across 18 sites and ALFF and ReHo maps from 795 participants across 16 sites.ResultsLICA identified site-related components across all three imaging measures. Site effects were not limited to uniform global shifts, but formed modality-specific spatial patterns. GM volume showed a dominant and highly stable whole-brain site-effect pattern, together with site-specific and regional components. In contrast, ALFF and ReHo showed more heterogeneous functional patterns, including global, focal, and scattered configurations. Site labels explained the largest proportion of loading variance, whereas recorded acquisition parameters showed modality-dependent contributions: TR and TE were more prominent for structural site effects, while FA, voxel size, TR, and scanner model contributed more strongly to functional site effects.DiscussionThe proposed framework provides a component-level diagnostic approach for multi-site MRI analysis. By mapping, stabilizing, and technically interpreting site-effect patterns, it complements conventional harmonization methods and may improve the transparency and reproducibility of multi-site structural and functional MRI studies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1846380</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1846380</link>
        <title><![CDATA[Computational design of immunogenic peptide–ligand conjugates for targeted therapy against Nipah virus infection]]></title>
        <pubdate>2026-07-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sudip Prasad Jena</author><author>Prateek Nayak</author><author>M. A. Adithyan</author><author>Manoviraj Morey</author><author>R. Vihaas Tharsini</author><author>Abilash Valsala Gopalakrishnan</author><author>Kuntal Pal</author><author>Sabina Evan Prince</author>
        <description><![CDATA[IntroductionNipah virus (NiV) poses a major risk to global public health due to its high infectivity and associated mortality rates. Currently, no licensed vaccines or antiviral medications are available for NiV infection, leaving clinical management limited to supportive care. The viral receptor glycoprotein responsible for binding NiV to host cell receptors (ephrin-B2/B3) represents an ideal therapeutic target. This study proposes a novel peptide-ligand conjugate (PLC) immunotherapeutic approach that exploits pre-existing immune responses in NiV-endemic populations to selectively target and eliminate infected cells.MethodsWe employed biomolecular modeling (in silico) to establish binding affinities and perform docking studies using a compound library obtained from the MolProphet database. A non-cleavable oxime linker was selected to enhance physical stability and ensure robust conjugation between ligand and peptide components. The peptide was engineered to contain immunogenic minimal epitope regions derived from measles, mumps, and rubella vaccines, selected based on their high immunization rates and long-lived memory responses in individuals residing in NiV-endemic areas.ResultsThe PLC design demonstrated selective binding capacity to a transmembrane protein present on NiV-infected cells. The oxime linker provided enhanced stability, and the peptide epitope design successfully incorporated regions associated with established long-term immunity.DiscussionThis PLC system represents a promising framework for antiviral therapeutic development by harnessing pre-existing immune recognition to promote selective clearance of NiV-infected cells. The findings highlight critical structural components and functional roles of PLCs in therapeutic development, including drug target screening and rational design strategies for enhancing targeting specificity and molecular stability. Future work should focus on experimental validation of the computational predictions and in vitro/in vivo efficacy studies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1851023</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1851023</link>
        <title><![CDATA[Instability of LLM text embeddings for unsupervised dimension reduction of tabular data]]></title>
        <pubdate>2026-07-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jun Li</author><author>Yixuan Gou</author><author>Shawn Su</author><author>Mujun Xu</author><author>Brendan Chen</author>
        <description><![CDATA[Large language model (LLM) text embeddings have recently been used for supervised learning on tabular data by serializing each observation into text and then converting the text into a dense fixed-length vector. This strategy is appealing for biomedical tabular data, which are often mixed-type and contain missing values, because it produces a complete numeric representation even when some original entries are missing. However, its suitability for unsupervised tasks remains unclear. Here we evaluate the use of LLM-derived text embeddings for dimension reduction of tabular data, focusing on biological and clinical datasets. We compare an LLM embedding-based approach with a direct tabular approach that computes dissimilarities directly from the original variables. Because unsupervised dimension reduction has no ground-truth low-dimensional target, we assess performance through stability under perturbation. Across multiple datasets and analysis settings, the LLM embedding-based approach is consistently less stable than the direct tabular approach. In particular, small amounts of additional missingness and random permutation of feature order can substantially alter the resulting low-dimensional representation. These results suggest that the straightforward use of LLM text embeddings is not reliable for unsupervised dimension reduction of tabular data.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1875897</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1875897</link>
        <title><![CDATA[Functional restoration of conformational (R175H) and contact (R273H) mutant p53 by Bacillus-derived Hydroxymycotrienin A in non-small cell lung cancer: a computational approach]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adeline Celina Rufus</author><author>Sidharth Kumar N</author><author>Magesh Ramasamy</author><author>Elavarashi Elangovan</author>
        <description><![CDATA[IntroductionThe TP53 mutations in Non-Small Cell Lung Cancer (NSCLC) remain a formidable clinical challenge. Current strategies, using the covalent binder APR-246, are limited by off-target toxicity and resistance.MethodsThis study screens 1,580 Bacillus-derived metabolites to identify novel non-covalent pharmacological chaperones for native, conformational (R175H), and contact (R273H) p53 mutants. Virtual screening, ADMET profiling, molecular docking, extended 500 ns Molecular Dynamics (MD) simulations and Principal Component Analysis, against experimental anti-cancer drug APR-246, and top hit compounds.ResultsHydroxymycotrienin A emerged as the potential candidate, demonstrating thermodynamic superiority with binding affinities of −6.63 kcal/mol (R175H) and −6.57 kcal/mol (R273H), demonstrated significant superior non covalent docking affinity compared to APR-246 parent scaffold (approximately −3.5 kcal/mol) in the mutated p53 protein, suggesting a direct pharmacological chaperone activity. Unlike the covalent alkylating mechanism of APR-246, Hydroxymycotrienin A utilizes a non-covalent network to chaperone the mutant p53. MD simulations revealed that Hydroxymycotrienin A acted as a structural stabilizer for the conformational mutant R175H by suppressing atomic fluctuations within the L2 loop and reducing overall structural deviations. In the contact mutant R273H, the ligand stabilized the DNA-binding interface while maintaining favorable conformational dynamics without introducing steric clashes.DiscussionADMET profiling predicts high bioavailability and a non-toxic safety profile, characterizing Hydroxymycotrienin A as a promising, bioavailable ‘privileged scaffold’ that offers a non-covalent therapeutic strategy for NSCLC. By restoring p53 function, this study addresses Sustainable Development Goals Target 3.4 to reduce premature mortality from non-communicable diseases. However, future in vitro and in vivo studies are required to validate the efficacy of these compounds.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1839606</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1839606</link>
        <title><![CDATA[In silico drug discovery pipelines targeting antibiotic resistance: from genomes to leads]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Rohan Gupta</author><author>Pallavi Singh</author><author>Karthikeyan Ravi</author><author>Mosleh Mohammad Abomughaid</author><author>Sorabh Lakhanpal</author><author>Ravi Sharma</author><author>Naveen Kumar</author><author>Niraj Kumar Jha</author>
        <description><![CDATA[Antimicrobial resistance (AMR) is currently one of the leading global health threats. The evolution of drug-resistant bacterial pathogens is rapid, and there is a growing number of bacterial pathogens that have developed resistance to multiple antibiotics, and the rate at which new antibiotics are being developed is lagging far behind these two issues. In addition, historical drug discovery processes rely on conducting traditional in vitro-based studies to determine new antibiotics to use in practice. However, this process is becoming increasingly constricted due to high costs of conducting traditional in vitro research, lengthy timeframes to bring products to market, a high attrition rate of research projects in traditional wet laboratory environments, and the need for more effective and efficient ways of developing new drugs. For this reason, Drug discovery continues to evolve from a wet lab-based approach to an in silico (i.e., computational) based approach, which takes advantage of the advances made in various fields, such as bacterial genomics, structural bioinformatics, machine learning (ML), and systems biology to enable researchers to rationally design, discover, and develop new antibiotics to combat drug-resistant pathogens. This review aims to provide a comprehensive and critical overview of contemporary in silico antibiotic discovery strategies and their potential to accelerate the development of novel, resistance-resilient, and clinically relevant antimicrobial agents. It examines genome-informed approaches ranging from genomic data generation, resistome analysis, and computational target identification to structure-based drug design, ligand-based and fragment-based discovery, drug repurposing, and the expanding applications of ML and artificial intelligence (AI) in activity prediction, de novo antibiotic design, and resistance evolution modeling. The review also highlights the importance of in silico ADMET prediction in lead optimization and discusses representative case studies demonstrating successful translation of computational predictions into experimental validation. Overall, the integration of digital-first, data-driven, and genome-guided discovery pipelines with experimental validation offers a powerful framework to overcome current challenges in antibiotic development and represents a promising strategy for addressing the global threat of AMR. Lastly, the review was conducted using a structured literature search across major biomedical and computational databases with emphasis on experimentally validated case studies and translational relevance.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770712</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1770712</link>
        <title><![CDATA[Integrative phosphoproteomics reveals kinase-mediated regulation of OCIAD1 and its roles in mitochondrial quality control]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amal Fahma</author><author>Suhail Subair</author><author>Fathimathul Lubaba</author><author>Athira Perunelly Gopalakrishnan</author><author>Prathik Basthikoppa Shivamurthy</author><author>Rajesh Raju</author>
        <description><![CDATA[BackgroundThe ovarian cancer immunoreactive antigen domain-containing protein 1 (OCIAD1) is a mitochondrial protein implicated in mitochondrial morphology, energy metabolism, and differentiation. Although understudied, recent studies position it as a critical player in carcinogenesis and neurodegenerative disorders, making it a potentially druggable node in cellular signaling networks. However, the phosphoregulatory networks and the upstream kinases governing OCIAD1 remain unknown.MethodsA large-scale literature mining and analysis of 177 phosphoproteomic datasets with differential expression of OCIAD1 was carried out to map its phosphoregulatory network. The predominant phosphosites were determined based on localization probability, detection frequency, and differential regulation. Multipronged computational approaches were employed to gather novel candidate kinases that may target OCIAD1 phosphosites. Co-differential phosphorylation analysis was conducted with other proteins, including interactors and candidate upstream kinases, to infer functional and regulatory associations.ResultsThe sites S108 and S123 emerged as predominant, together accounting for 70% of OCIAD1 phosphorylation. Co-differential phosphorylation analysis revealed associations with proteins involved in the cell cycle, DNA repair, autophagy, mitophagy, endocytosis, and apoptosis. Novel candidate kinases for OCIAD1 phosphosites were identified; notably, SRMS and YES1 emerged as potential upstream regulators of Y199. Furthermore, the phosphosites in the candidate kinases of sites, including PLK1 (T210), CDK13 (S383, S397), PRKD2 (S200), CIT (S1343), and RPS6KA3 (T577), showed strong positive co-differential regulation with OCIAD1 predominant sites, supporting their potential involvement as upstream kinases.ConclusionThis study presents the first systematic map of the OCIAD1 phosphoregulatory network and provides candidate upstream kinases that may contribute to its phosphorylation, which warrant further experimental validation. The strong co-differential regulation of proteins involved in autophagy, mitophagy, endocytosis, and neurodegenerative pathways, as well as of kinases that orchestrate these processes, suggests that OCIAD1 phosphoregulatory network maybe involved in mitochondrial quality control and mitochondria-associated neurodegeneration, establishing a foundation for therapeutic investigations targeting OCIAD1 signaling.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1858891</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1858891</link>
        <title><![CDATA[Analysing open-source protein folding models for nanobody binding prediction]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yannick Vogt</author><author>Rebekka Roßberg</author><author>Jan Habermann</author><author>Pia Kluge</author><author>Wendan Xu</author><author>Mehdi Naouar</author><author>Stefan Hirschberg</author><author>Cornelius Miething</author><author>Joschka Bödecker</author><author>Maria Kalweit</author><author>Evelyn Ullrich</author><author>Gabriel Kalweit</author>
        <description><![CDATA[IntroductionAntibody-based therapeutics are a rapidly expanding class of treatments, with over 200 approved candidates and thousands in clinical trials. Computational pre-filtering using protein structure prediction models has the potential to reduce the cost of wet-lab screening, yet the relationship between model confidence measures and functional binding properties remains incompletely understood. Here, we evaluate whether confidence measures produced by contemporary open-source protein structure prediction models are suitable for in silico screening of nanobody–antigen interactions.MethodsWe benchmark Boltz-2, Chai-1, IntFold, and AlphaFold3 using two complementary tasks: (i) ranking true nanobody–antigen binding complexes above non-binding bait pairs across 17 antigens, and (ii) detecting out-of-distribution sequences generated by alanine substitution of all complementarity-determining region residues. We further assess confidence measure sensitivity through progressive alanine mutagenesis on 13 nanobody–antigen complexes spanning the range of CDR3 lengths in our dataset and evaluate generalizability using data from a camelid immunization campaign against CD33.ResultsBoltz-2-derived confidence measures achieved the highest median performance for identifying true binders. Local confidence measures, including pLDDT and interface- or CDR-focused metrics, were most effective at detecting out-of-distribution sequences and exhibited the greatest sensitivity to mutations. No single confidence measure performed best across both tasks, and all evaluated protein structure prediction models showed limited generalization to previously unseen antigens.DiscussionOur results suggest that robust in silico nanobody candidate selection should combine complementary global and local confidence measures rather than relying on a single metric. These findings provide practical guidance for integrating open-source protein structure prediction models into AI-driven nanobody discovery pipelines while highlighting the need for improved generalization across antigens.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1858518</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1858518</link>
        <title><![CDATA[Pan-cancer multi-omics analysis of pharmacogenomic alterations]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Aisha AlMulla</author><author>Simerpreet Kaur</author><author>Prachi Balyan</author><author>Sana Al-Saafin</author><author>Manoj Kumar Balyan</author><author>Dinesh Velayutham</author><author>Puthen Veettil Jithesh</author>
        <description><![CDATA[BackgroundPharmacogenomic (PGx) variation is an important determinant of inter-individual variability in drug response. While germline pharmacogenomics has been extensively studied, somatic alterations affecting these pharmacogenes in tumors remain less systematically characterised.MethodsWe performed a pan-cancer multi-omics analysis integrating somatic mutation, copy number alteration, and transcriptomic data across 765 patients with primary tumors spanning seven The Cancer Genome Atlas (TCGA) cancer types. Exploratory survival analyses were restricted to 733 patients with complete clinical and overall survival information. Using a curated panel of 40 pharmacogenes, we characterized gene-level alterations, summarised recurrent events across tumor types, and aggregated these signals into pharmacological modules representing key pharmacological processes.ResultsPharmacogene alterations were generally infrequent at the individual gene level but showed recurrent, non-uniform patterns across cancers. A uniform-gene null model showed that descriptive combined scores were more concentrated than expected under a simplified uniform-gene background, with a global Gini index of 0.306 compared with a null mean of 0.248, and the top 10 gene-cancer pairs accounting for 10.1% of the total score compared with a null mean of 7.8%. Broad non-synonymous mutation, copy number amplification, and copy number deletion frequencies averaged 3.8%, 4.7%, and 0.3%, respectively, while the mean frequency of any integrated alteration was 8.6%. A descriptive prioritization score, defined as broad non-synonymous mutation frequency plus copy number amplification frequency, identified recurrent high-ranking alterations in transporter- and drug-handling genes, including ABCB1, ABCC1, and ABCC2. Sensitivity analyses supported cautious interpretation, including partial ranking robustness across stricter truncating/LoF-like mutation definitions, small-cohort exclusions, and cBioPortal GISTIC amplification sensitivity analyses. Systematic CNA-expression testing across 280 gene-cancer pairs identified 64 significant positive associations after FDR correction, with representative examples including GGH, ERCC1, and SLC29A1. Module-level pharmacogenomic alteration was not significantly associated with survival in exploratory treatment-agnostic survival models.ConclusionOur findings provide an exploratory TCGA-based map of somatic alterations of pharmacogenes across cancers and identify recurrent alteration patterns that warrant validation in treatment-annotated and external datasets.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1877345</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1877345</link>
        <title><![CDATA[Diagnostic accuracy and repeatability of ChatGPT using textual and radiographic data in reversible pulpitis: a retrospective diagnostic study]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>María Llorente de Pedro</author><author>Yolanda Freire</author><author>Natalia Moneo</author><author>Cristina Andreu-Vázquez</author><author>Roberto Estévez</author><author>Víctor Díaz-Flores García</author><author>Ana Suárez</author>
        <description><![CDATA[ObjectivesLarge language models have recently evolved into multimodal systems capable of interpreting images alongside text. The aim of this study was to assess the diagnostic accuracy and repeatability of ChatGPT-5 for reversible pulpitis, based on structured clinical information and periapical radiographs (PRs).MethodsThirty expert-validated cases of reversible pulpitis were retrospectively selected. For each case, clinical data and PRs were compiled to create a reference dataset, with expert consensus serving as the reference standard. A structured prompt incorporating clinical information and PR was designed and included six predefined questions: four addressing diagnostic reasoning, one assessing whether PR information was used to establish the diagnosis, and one requiring radiographic findings description. Each case was entered thirty times into ChatGPT-5, generating a total of 900 answers. Answers were assessed by two independent experts, with disagreements resolved by a third. Radiographic descriptions were graded using a three-point Likert scale. Diagnostic accuracy and repeatability were analysed using binomial and agreement methods.ResultsDiagnostic questions related to tooth identification, pulpal diagnosis, periapical diagnosis and treatment indication showed accuracies above 96%. Overall accuracy for radiographic description was low (4.67%). Coronal radiographic interpretation showed poor performance (5.15%), whereas accuracy for pulpal and periapical structures exceeded 96%. Repeatability was almost perfect for diagnostic questions and pulpal/periapical interpretation, but moderate for coronal descriptions.DiscussionChatGPT-5 demonstrated high accuracy and repeatability in text-based diagnostic reasoning for reversible pulpitis. However, limitations in radiographic interpretation, particularly of coronal structures, indicate that its use should be limited to a supervised decision-support role.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1844071</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1844071</link>
        <title><![CDATA[Clinically interpretable deep learning for breast cancer missense variant pathogenicity prediction]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rahaf M. Ahmad</author><author>Noura AlDhaheri</author><author>Mohd Saberi Mohamad</author><author>Bassam R. Ali</author>
        <description><![CDATA[BackgroundMissense variants in breast cancer remain diagnostically challenging due to their functional diversity and complex genomic contexts. Conventional laboratory assays for evaluating pathogenicity are labor-intensive, costly, and often impractical for large-scale screening, creating a pressing need for accurate, scalable, and clinically interpretable computational approaches.MethodsIn this study, we present a novel deep learning framework for predicting the pathogenicity of breast cancer missense variants, integrating comprehensive preprocessing, advanced imputation, rigorous model benchmarking, and explainability. Genetic variants were curated from multiple genomic databases, annotated using the Ensembl Variant Effect Predictor (VEP), and processed with Variational Autoencoders (VAE) for missing-value imputation. Seven deep learning models, MLP, CNN, DNN, RNN, LSTM, GRU, and Transformer, were trained and evaluated across 11 performance metrics. To quantify performance stability, each model was trained across five random seeds; mean AUC ± SD across seeds is reported as the primary performance estimate, with the best-seed run used only for LIME and PMI interpretability analyses. Recursive feature elimination, permutation importance (PMI), and Local Interpretable Model-Agnostic Explanations (LIME) were employed to enhance transparency. Statistical analyses, including Z-tests, ANOVA, and calibration assessments, validated performance consistency and inter-model differences.ResultsGRU achieved the highest internal AUC (0.9956 [95% CI 0.9936–0.9972]; mean across five seeds 0.9941 ± 0.0011), with precision 0.9967 and calibration ECE 0.0095. Externally, LSTM led with AUC 0.9457, exceeding all eleven standalone predictors benchmarked on the same set. Models showed strong alignment with conservation signals such as phyloP470way and Eigen-PC scores. Notably, the pipeline provides performance metrics with 95% confidence intervals and incorporates case-level LIME visualizations for true positive, true negative, false positive, and false negative predictions, bolstering interpretability and clinical relevance.ConclusionThis work delivers one of the most comprehensive evaluations of deep learning in breast cancer variant classification to date. By combining high-performance sequential models with interpretable AI tools, the proposed framework provides a reproducible, transparent benchmark for variant pathogenicity prediction and a foundation for future research use and translation in cancer genomics.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1876523</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1876523</link>
        <title><![CDATA[Radiation-induced network rewiring in cancer: systems-level insights into environmental radioactivity and precision oncology]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Marckasagayam Priyadharshini</author><author>Saravanan Sekaran</author>
        <description><![CDATA[The impact of ionising radiation (IR) from environmental and therapeutic sources is complex and extends beyond the damage to DNA. These perturbations are conveyed by regulatory networks, such as transcription factors, microRNAs, long non-coding RNA, and signaling cascades, which in the end affect the susceptibility, progression, and treatment response to cancer. The traditional reductionist approaches, which are very useful, are not enough to explain emergent properties of these dynamic systems. The system’s biology paradigm has a great potential of providing the explanation of the rewiring of cellular networks by radiation and identify novel targets to be targeted in precision oncology. The review summarizes the current literature on radiation-induced alterations of network patterns and on the remodelling of the molecular landscape of cancer cells due to exposure to environmental radioactivity and therapeutic radiation exposure. We create awareness of methodological progress in the development and analysis of radiation-responsive regulatory networks, identify key hub molecules and pathways, and discuss the translation potential of using radiation-responsive regulatory networks in individual radiotherapy. This review is different from previous reviews that have concentrated on single mechanisms in single-omics viewpoints or focused on a single radiation type as environmental or therapeutic, since it brings together network topology analysis, multi-omics data and clinical translatability in the context of both types of radiation exposure. We explore how weaknesses in radiation-sensitive components of a tumor can be used to target radiation-resistant ones while at the same time, radiation-induced damage to normal tissues can be reduced by network-aware approaches.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1870490</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1870490</link>
        <title><![CDATA[Network pharmacology and molecular modelling analysis of Arctium lappa phytochemicals in valproic acid–induced hepatotoxicity and pancreatitis]]></title>
        <pubdate>2026-07-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mukul Shyam</author><author>Sabina Evan Prince</author>
        <description><![CDATA[BackgroundValproic acid (VPA) is an antiepileptic drug commonly employed to treat epilepsy. Despite this, the clinical application of VPA is often hindered by adverse reactions. This study aims to investigate the therapeutic potential of phytochemicals obtained from Arctium lappa to mitigate VPA-induced toxicity and to determine the mechanisms involved.MethodsNetwork pharmacology and molecular modelling techniques were employed. HR-LCMS-QTOF was conducted to identify the phytoconstituents. ADMET and toxicity screening were then performed. Ligand-based prediction and toxicity gene analysis were employed to determine the potential targets. Protein-protein interaction analysis and function enrichment analysis were conducted to understand the potential mechanisms. Molecular docking and molecular dynamics studies were conducted to determine the protein-ligand interaction potential.ResultsA total of 111 metabolites were identified, out of which 22 compounds passed the ADMET and toxicity screening. Using ligand-based prediction and toxicity analysis, 18 potential target genes were identified. These include MAPK8, NLRP3, TNF-α, and MCL-1. These are involved in various functions, including inflammation, apoptosis, and metabolic regulation. Molecular docking analysis revealed high protein-ligand interaction potential. This was evident by the high binding affinity of the phytoconstituents to the potential targets. Among all the compounds, 4-Hydroxy-3-methoxy-2,10-bisaboladien-9-one showed the highest potential. Molecular dynamics analysis revealed high protein-ligand complex stability, especially with MCL-1 and NLRP3. Function enrichment and Metascape analysis revealed the involvement of the potential targets in oxidative stress, inflammation, and xenobiotic metabolism.ConclusionThe results suggest that phytoconstituents from A. lappa have the potential to modulate key molecules involved in VPA-induced toxicity.]]></description>
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