Frontiers in bioinformatics最新文献

Background: Lung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer, representing a major contributor to cancer mortality rate marked by a high frequency of mutations and intricate interactions between multiple signalling pathways.

Objective: Here we explore the role of NOTCH1 associated Single nucleotide polymorphisms (SNPs) IDR and PTM in LUAD progression. Although the NOTCH1 expression is downregulated, it has been validated as an important prognostic marker because of its complex biological roles under specific conditions.

Methods: With the aid of In silico tools we predicted and identified the deleterious SNPs. The Molecular Docking and dynamics simulations (MDS) were conducted to characterize these mutations.

Results: A total of 43 deleterious SNPs were found in the sequential SNP analysis with 13 SNPs resulted deleterious and damaging effects. The stabilizing SNPs such as S1464I, A1705V and T1602I are found within the conserved and functional domains of NOTCH1. In addition, 1660-2555 sequence region of the PEST domain was recognized as an Intrinsically Disordered Region (IDR) with a score of above 0.5. Moreover, the presence of the two phosphodegrons (SCF_FBW7_1 at 2129-2136 and SCF_FBW7_2 at 2508-2515) along with the Post Translational Modification (PTM) such as o-linked glycosylation and Phosphothreonine within the IDR region, PEST and conserved domains suggest functional significance in LUAD progression.

Conclusion: In conclusion our research highlights the potential regulatory role of identified SNPs, PTMs, and the functional domains of Notch1, particularly the PEST domain and IDR, in pathophysiology of LUAD particularly through the crosstalk of the EMT signalling.

Introduction: With increased drug resistance and tumor heterogeneity accounting for limited therapeutic strategies, gastric cancer remains one of the major causes of cancer-related mortality around the globe. Targeting the components of the tumor microenvironment (TME) has become a promising therapeutic strategy due to their crucial roles in cancer cell proliferation, progression, and metastasis. One of the limitations of the previously identified therapeutic targets is their limited applicability to a broader patient population.

Methods: This study aims to identify (TME)-related therapeutic targets using an integrated bioinformatics and molecular docking approach that involves a larger number of datasets to cover a broader cohort of gastric cancer patients. It analyzed multiple publicly available transcriptomic datasets using Robust Rank Aggregation (RRA) meta-analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to identify significant hub genes. Furthermore, protein-protein interaction (PPI) network analyses, conducted using multiple methods such as Cytohubba topology analysis and ClusterONE module analysis, refined the potential therapeutic candidates. Functional enrichment analyses were performed to identify vital genes involved in TME interactions and ECM remodeling.

Results: The enriched genes were validated for their significant dysregulation in the Cancer Genome Atlas gastric adenocarcinoma dataset (TCGA-STAD) and three independent GEO datasets to ensure differential expression across distinct cohorts. Genes with consistent dysregulation were used in survival analyses across TCGA and two GEO datasets to prioritize hub genes with prognostic significance. Finally, a targeted literature survey ensured the exclusion of previously targeted genes, and molecular docking analyses conducted using phytocompounds identified potential therapeutic leads with strong affinities for the identified targets.

Discussion: This integrated approach revealed notable, promising targets in the TME and natural compounds for developing potential personalized therapeutic strategies in gastric cancer.

Nipah virus (NiV) is a zoonotic pathogen that causes recurrent outbreaks with considerable implications for public health. Viruses engage host kinases to phosphorylate viral proteins, aiding replication and host disruption. Identifying NiV phosphoproteins and their host kinases is therefore critical for understanding the mechanism of infection and developing therapeutics. We performed kinase-substrate phosphomotif analysis based on prior studies and employed computational tools to identify putative phosphosites in NiV proteins and corresponding host kinases. Redundancy analysis highlighted key kinases capable of phosphorylating multiple NiV proteins and high-potential viral substrates. Integration with human-viral protein-protein interaction data revealed human kinase substrate proteins in human that interact with NiV proteins, while conservation analysis assessed phosphosites across nine NiV proteins in various strains. The functional significance of the identified and predicted viral substrates and their corresponding host kinases was further validated through in silico docking and molecular dynamics simulation (MD). Motif-based kinase-substrate analysis identified 51 human kinases predicted to target 1180 phosphorylation sites across nine NiV proteins, including key human kinases such as Eukaryotic elongation factor 2 kinase [EEF2K], Haploid germ cell-specific nuclear protein kinase [HASPIN], Mitogen-activated protein kinase 9 [MAPK9], Microtubule-associated serine/threonine-protein kinase 2 [MAST2], and Spleen tyrosine kinase [SYK], with the potential to phosphorylate multiple sites across NiV proteins. Using computational prediction tools, we identified several potential phosphorylation sites on NiV proteins, along with their corresponding candidate human kinases. In silico docking revealed interactions between EEF2K and both the NiV Fusion Glycoprotein and NiV Phosphoprotein (P), MAPK9 with the NiV Matrix Protein, and HASPIN with NiV RNA-dependent RNA polymerase. MD simulations of the EEF2K-NiV Fusion Glycoprotein complex confirmed the stability of this interaction. Leucine-rich repeat serine/threonine-protein kinase 2 [LRRK2], HASPIN, MAST2, and EEF2K were the human kinases predicted to phosphorylate experimentally validated sites on NiV nucleocapsid (N), P, and W proteins. Furthermore, through an extensive literature review, we investigated the therapeutic potential of targeting these kinases using known inhibitors and identified compounds that could potentially be repurposed as antiviral agents against NiV infection. Our findings indicate that EEF2K phosphorylates key NiV proteins at conserved phosphosites across variants, underscoring the pathogenic significance of kinases in NiV infection and their potential as therapeutic targets.

Background: The disialoganglioside GD2 is a key cancer therapy target due to its overexpression in several cancers and limited presence in normal tissues. However, experimental assessment is technically challenging and not routinely available. We developed a computational framework that integrates reaction activity derived from transcriptomic data with the glycosphingolipid biosynthesis pathway to predict GD2 expression.

Methods: We computed Reaction Activity Scores from transcriptomic data and weighted the reactions of a glycosphingolipid metabolic network, refining edge weights with topology-based transition probabilities to account for enzyme promiscuity. Cumulative activities of GD2-promoting and -mitigating reactions served as features in a Support Vector Machine (SVM) to model GD2-associated differences between neuroblastoma and normal tissue. SVM decision values were used as a continuous proxy for GD2 expression. We validated the predicted GD2 scores across independent datasets by comparing them with literature-reported values and flow-cytometric confirmation of a model-predicted high-GD2 tumor. Copy-number alteration (CNA) data were integrated to identify candidate genomic biomarkers of GD2-positive samples.

Results: Our SVM-based GD2 score achieved balanced accuracy of 0.80 with a linear kernel, selected due to reduced overfitting risk and interpretability, while matching the accuracy of more complex kernels. The model transferred reliably across six independent RNA-seq datasets and reproduced known GD2 expression patterns, outperforming a two-gene signature in capturing subtype-specific heterogeneity and avoiding overestimation in normal brain tissue. Pan-cancer analyses revealed heterogeneous GD2 expression in several cancer subtypes. Notably, we experimentally confirmed high GD2 expression in clear cell sarcoma of the kidney, consistent with model predictions. CNA analysis implicated B4GALNT1 amplification as a GD2-promoting factor in dedifferentiated liposarcoma. To facilitate adoption of our approach, we developed GD2Viz, an R package with an interactive Shiny application for score computation, visualization, and analysis of user data.

Conclusion: Our computational framework provides a robust, interpretable, biologically grounded predictor of GD2 expression, offering greater consistency and clinical interpretability over existing gene-based signatures. Importantly, with over 20 GD2-directed trials ongoing, our approach may help prioritize tumor entities with high GD2 levels, delineate candidate patient subgroups, and generate testable hypotheses in underexplored cancers, thereby supporting patient stratification and eligibility screening for clinical trials.

[This corrects the article DOI: 10.3389/fbinf.2025.1599098.].

Introduction: The rise of multidrug-resistant Salmonella typhimurium is a severe public health threat that renders conventional antibiotics ineffective. This study employed a computational strategy to identify a novel drug target in S. typhimurium and screen food-based polyphenols as potential inhibitors.

Methods: A subtractive genomics approach was used to identify essential, pathogen-specific proteins. A lead target was prioritized based on its druggability, localization, and network interactions. The target's 3D structure was then modeled for molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations with a polyphenol library.

Results: The screening identified UDP-N-acetylglucosamine transferase (MurG) as a promising and previously unexplored drug target. The polyphenol 6-prenylnaringenin showed a superior binding affinity for MurG compared to the antibiotic ciprofloxacin. Subsequent MD simulations and binding free energy calculations confirmed that the MurG-6-prenylnaringenin complex was significantly more stable.

Conclusion: This study validates MurG as a druggable target in S. typhimurium and identifies 6-prenylnaringenin as a potent inhibitor. With computational metrics superior to ciprofloxacin, 6-prenylnaringenin is a promising lead compound for developing new anti-Salmonella therapeutics. Future experimental validation is required to confirm these in silico findings.

Background: Sickle cell disease (SCD) is an inherited blood disorder characterized by chronic hemolysis, inflammation, and vaso-occlusive crises (VOC), leading to multiple complications and reduced life expectancy in affected individuals. Limited effective treatment options are currently available; however, recent genomic findings from underrepresented populations (Saudi Arabians) have offered new hope for predicting molecularly guided treatments. This study aimed to identify approved drugs suitable for repurposing based on their interactions with SCD-associated genetic variants and to discover novel druggable targets within genetic pathways linked to disease severity by utilizing genome-wide association study (GWAS) data from Saudi SCD patients.

Methods: Bioinformatic pipelines were used to evaluate drug-gene interactions and identify potential therapeutic targets based on GWAS data derived from the Saudi population. Approved drugs were suggested for repurposing according to their interactions with genes known to impact SCD pathophysiology, using the Drug-Gene Interaction Database (DGIdb 5.0). New drug targets were also proposed by assessing the simulated binding pockets of gene products, using 3D protein structures from the Protein Data Bank (PDB) and the AlphaFold database. Molecules with higher druggability scores, as estimated by the DoGSiteScorer database, were predicted to have a higher success rate for new SCD treatment development.

Results: Our analysis identified 78 approved medications with potential for repurposing in SCD; this list was narrowed to 21 candidates based on safety profiles and interactions with key genetic pathways. Among these, simvastatin, allopurinol, omalizumab, canakinumab, and etanercept were suggested as the most promising agents. Furthermore, novel drug targets encoded by olfactory receptor (OR) gene clusters (OR51V1, OR52A1, OR52A5, OR51B5, and OR51S1), TRIM genes, SIDT2, and CADM3 displayed high druggability scores.

Conclusion: This study provides a robust framework for drug repurposing and novel drug discovery in SCD, particularly tailored to the Saudi population. The findings underscore the potential of leveraging genomic data to identify targeted therapies, offering a pathway to more personalized and effective treatments for SCD patients. Future clinical trials are essential to validate these findings and translate them into clinical practice.

Introduction: The polo-like kinase 1 (PLK1), a master key mitotic regulator, is frequently expressed in various types of cancers and associated with poor prognosis. The missense mutations in PLK1 may compromise its structural integrity and functional interactions, contributing to tumorigenesis.

Methods: This study utilized a comprehensive computational pipeline to identify deleterious missense variants across multiple cancers. 207 non-synonymous single nucleotide polymorphisms (nsSNPs) were retrieved from cBioPortal, and 11 high-risk variants were prioritized using functional and structural prediction tools, such as SIFT, PolyPhen-2, I-mutant 2.0, and so on. Prognostic prevalence was evaluated via Kaplan-Meier survival analysis, and functional networks were explored using STRING. The structural dynamics of modeled mutations were analyzed through molecular dynamic simulations over 100 ns.

Results: The kinase domain mutations such as L244F, R293C, and R293H and polo-box domain mutations such as A520T were found to cause deviations in structural stability, flexibility, solvent exposure, and compactness compared to wild-type. Further, PLK1 overexpression correlated with poor overall survival of patient outcomes in many types of cancers, including breast, liver, lung, kidney, and pancreatic cancers. Protein-protein interaction revealed PLK1's involvement in oncogenic pathways.

Discussion: The study highlights the structural and functional implications of oncogenic PLK1 mutations, emphasizing their role in cancer progression. Integrating predictive and dynamic exploration approaches facilitates prioritization of variants with potential clinical relevance.

Conclusion: The nsSNPs in PLK1 may perturb conformational stability and functions of the protein. Further experimental validation and discovery of novel inhibitors might develop mutation-specific interventions in precision oncology.

Structural similarity metrics such as the Tanimoto coefficient (TC) miss many functionally related compounds-indeed, 60% of similarly bioactive ligand pairs in the ChEMBL database show TC < 0.30, revealing a major blind spot that constrains ligand-based discovery. Our motivation is to overcome this blind spot and enable the recovery of structurally different yet functionally equivalent chemotypes that structure-based similarity fails to detect. Here, we introduce the bioactivity similarity index (BSI), a machine learning model that estimates the probability that two molecules bind the same or related protein receptors. Trained under leave-one-protein-out (LOPO) across Pfam-defined protein groups on dissimilar pairs, BSI not only outperforms TC but also surpasses modern molecular embedding baselines (ChemBERTa and contrastive language-molecule pre-training (CLAMP), using cosine similarity) across protein families. We further develop a cross-family model (BSI-Large) that, while slightly below group-specific models, generalizes better and can be fine-tuned with less data, consistently improving over models trained from scratch. In retrospective validation on new ChEMBL v35 data, BSI achieves strong early-retrieval performance (top 2% enrichment factor, EF_2%), with group-specific models delivering the best enrichment, and BSI-Large remaining competitive. In a realistic virtual screening-like scenario against the target gene ADRA2B, the mean rank of the next active, given a known active, improves from 45.2 (TC) to 3.9 (BSI), with 54.9 for ChemBERTa and 28.6 for CLAMP. Altogether, BSI complements, rather than replaces, structure-based similarity and embedding-based comparisons, extending hit finding to remote chemotypes that are structurally dissimilar yet functionally equivalent. The code is available at https://github.com/gschottlender/bioactivity-similarity-index.

Prokaryotic genome evolution is shaped by mutation, gene duplication, and horizontal gene transfer, yet the interaction of these mechanisms, particularly in relation to low complexity regions (LCRs), remains poorly understood. LCRs are known to be mutation-prone and have been proposed to promote genetic innovation. However, the interaction between LCR-mediated and paralogy-mediated genetic innovation is still unclear. To clarify the interplay between these two evolutionary forces, we analyzed the distribution of LCRs in protein-coding genes from three closely related enterobacteria (Escherichia coli, Salmonella enterica, and Klebsiella pneumoniae) at both species and population levels. Using pangenomic and orthology-based approaches, we categorized genes by duplication history and conservation status and assessed LCR frequencies across these groups. We found that LCRs were consistently enriched in core and orthologous genes rather than in accessory or paralogous ones. This pattern was stable across evolutionary timescales and particularly pronounced in genes involved in cell cycle control and defense. These results suggest that, contrary to prior assumptions, LCRs may serve conserved functional roles rather than acting primarily as agents of evolutionary plasticity even at population-level timescales.