Frontiers in bioinformatics最新文献

{"title":"Alternative splicing remodeling and splicing factor regulatory networks define immune-associated molecular heterogeneity in dilated cardiomyopathy.","authors":"Jia Li, Yiyu He, Xianxian Zhao, Zhifu Guo, Jinchao Song, Xiaowei Song","doi":"10.3389/fbinf.2026.1934275","DOIUrl":"10.3389/fbinf.2026.1934275","url":null,"abstract":"<p><p>Dilated cardiomyopathy (DCM), a progressive cardiac disorder marked by ventricular dilatation and systolic dysfunction, involves a complex interplay between genetic and immune dysregulation. However, how alternative splicing remodeling contributes to its molecular heterogeneity remains poorly understood. Here, we performed an integrated transcriptomic analysis to characterize DCM-associated alternative splicing landscapes and their regulation by splicing factor networks. We identified widespread differential alternative splicing (DAS) events in DCM, with affected genes significantly enriched in cardiac contraction, sarcomere organization, calcium handling, extracellular matrix remodeling, and protein metabolism. Representative splicing events in key cardiac genes, including <i>TTN</i>, <i>RYR2</i>, <i>LDB3</i>, and <i>FLNA</i>, exhibited distinct junction usage and percent spliced in (PSI) alterations between DCM and control samples, suggesting functionally relevant transcript isoform remodeling. By integrating splicing factor expression profiles with DAS event PSI values, we constructed a characteristic SF-AS regulatory network and identified five key splicing factors-<i>MBNL1</i>, <i>NOVA1</i>, <i>RBM25</i>, <i>RBM5</i>, and <i>SF1</i>-as candidate tissue-level biomarkers of DCM-associated splicing remodeling. Molecular subtype analysis further revealed distinct DCM subgroups characterized by divergent immune infiltration patterns, pathway activities, and hub gene signatures. Machine learning-based identification and external validation supported the diagnostic potential of this five-splicing-factor signature. Collectively, this study deciphers coordinated splicing factor-associated alternative splicing remodeling in DCM, providing deeper insights into the immune-associated molecular heterogeneity and potential regulatory mechanisms underlying disease progression.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1934275"},"PeriodicalIF":3.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational insights into synergistic mechanisms of PD-901 and 5-azacitidine targeting PTPN11 (SHP2) E76K mutation in juvenile myelomonocytic leukemia.","authors":"Abeer M Al-Subaie, Balu Kamaraj","doi":"10.3389/fbinf.2026.1887328","DOIUrl":"10.3389/fbinf.2026.1887328","url":null,"abstract":"<p><strong>Introduction: </strong>Juvenile Myelomonocytic Leukemia (JMML) is an aggressive pediatric hematological cancer categorized by aberrant activation of the RAS/MAPK pathway. It is commonly caused by gain-of-function mutations, such as E76K in the PTPN11 protein. While 5-azacitidine (5-Aza) and the MEK inhibitor PD0325901 (PD-901) are not known direct inhibitors of SHP2/PTPN11, the reported activity of 5-Aza combined with MEK inhibition in PTPN11-mutated JMML offered a biological rationale for examining their potential structural interactions with SHP2.</p><p><strong>Methods: </strong>This study used a computational approach that included molecular docking, molecular dynamics (MD) simulations, and binding free-energy calculations using the MM-PBSA method to exploratively investigate interactions between 5-azacitidine (5-Aza) and the MEK inhibitor PD0325901 (PD-901) with wild-type and E76K-mutant PTPN11 proteins.</p><p><strong>Results: </strong>In the molecular docking study, 5-Aza was observed to bind within the active site, whereas PD-901 bound preferentially to the allosteric site. The dual-ligand simulations were used to investigate whether occupancy of these distant regions was associated with changes in PTPN11's structural and dynamic characteristics. MD simulations showed that the structures of all complexes remained stable throughout, and that the combined structure exhibited lower deviations, less residue flexibility, compactness, and more intermolecular hydrogen-bond interactions. Principal component analysis also revealed differences in the dominant conformational motions of the ligand-bound systems. MM-PBSA calculations provided comparative estimates of binding energetics and revealed promising interaction energies for all complexes, particularly in the E76K mutant system.</p><p><strong>Discussion: </strong>Overall, these computational findings suggest that the association of 5-Aza and PD-901 with distinct regions of PTPN11 may influence its structural and dynamic properties. These findings should therefore be considered exploratory and do not establish direct SHP2 binding or inhibition by either compound. Further biochemical, biophysical, and cellular studies are needed to validate these predicted interactions and determine their potential biological or therapeutic relevance in JMML.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1887328"},"PeriodicalIF":3.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13586710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying mounting behaviour in boars using deep learning-based instance segmentation and binary classification - a pilot study.","authors":"Harry Aricibasi, Jessica Bode, Renée Bergeron, Dan Tulpan","doi":"10.3389/fbinf.2026.1869902","DOIUrl":"10.3389/fbinf.2026.1869902","url":null,"abstract":"<p><strong>Introduction: </strong>Mounting behaviour in group-housed pigs poses significant welfare and productivity challenges, often resulting in injuries and stress. To address this issue, we developed a novel computer vision-based approach for the automatic detection of mounting behaviour in pigs.</p><p><strong>Methods: </strong>The pilot study was conducted on eight boars, aged 4-5 months and weighing between 60 and 90 kg, housed in two groups of four. Continuous video footage was collected over a 1-week period using an overhead camera, resulting in 766 2-s video clips. The proposed hybrid approach integrates Mask Region-based Convolutional Neural Network (Mask R-CNN) instance segmentation with a binary classifier. To evaluate the model's performance under varying data conditions, the dataset was divided into training, validation, and testing sets using three data grouping scenarios: by frame, by clip, and by day.</p><p><strong>Results: </strong>Among the five evaluated binary classification algorithms, Support Vector Machine (SVM) was selected based on its superior performance. The hybrid system achieved a balanced accuracy and F1-score of 95% with the clip-based split, rising to 99% with the frame-based split and falling to 74% with the day-based split.</p><p><strong>Discussion: </strong>Under pilot conditions, the system offers a feasible proof-of-concept for continuous monitoring of mounting behaviour in group-housed pigs, operating at a processing rate of five frames per second. This capability offers strong potential for supporting early intervention and proactive welfare management, subject to future multi-site validation. Moving forward, future research will focus on integrating individual animal tracking and conducting larger-scale studies to further explore the system's scalability and enhance its performance.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1869902"},"PeriodicalIF":3.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13587698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conformational dynamics of fuzzy interfaces in disordered protein complexes: mapping key residues and binding modes beyond NMR models.","authors":"María Laura Fernández, María Clara Bastien, Franco G Tavolaro, Cristina Marino-Buslje","doi":"10.3389/fbinf.2026.1907936","DOIUrl":"10.3389/fbinf.2026.1907936","url":null,"abstract":"<p><p>Understanding the dynamic behavior of fuzzy complexes formed by intrinsically disordered proteins (IDPs) is challenging due to their conformational flexibility. Nuclear Magnetic Resonance (NMR) structural bundles satisfy experimental constraints and inherently provide an overview of alternative conformations in solution. However, they assign equal weight to every sampled model, obscuring the temporal prevalence and dynamics of inter-residue contacts. In this study, we performed dual 1-microsecond (µs) Molecular Dynamics (MD) simulations for eight biologically relevant fuzzy complexes, utilizing the most distant conformers from deposited NMR models, as starting configurations. Through structural clustering and contact-persistence analysis, we classified the complexes into two distinct dynamic categories. The first group comprises complexes whose trajectories remain consistent with experimental data. Notably, large-scale motions such as relative chain rotations observed during simulations are already reflected as distinct orientations within the starting NMR conformers. Conversely, the second group comprises complexes that exhibit interaction modes absent in the structural NMR ensembles. These include alternative stable interfaces arising from chain reorientation, as well as highly dynamic interfaces characterized by rapidly exchanging contacts lacking any persistent stabilizing core. Furthermore, the microsecond trajectories captured intermittent dissociation and re-association events where the proteins sampled transient contacts through alternative regions. Overall, leveraging NMR models as a baseline for MD clustering and contact weighting, provides a systematic bioinformatics analysis to solve the atomistic details of flexible interfaces. Beyond categorizing diverse binding behaviors, this approach systematically refines the interaction surfaces across all studied cases, uncovers dynamic features that escape detection in ensemble-averaged NMR structures. Furthermore, it maps key residues to guide further experimental approaches, such as mutational studies or therapeutic targeting, among other applications.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1907936"},"PeriodicalIF":3.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All-atoms MD simulations study of newly designed ciprofloxacin derivatives as potential bacterial DHPS and DNA gyrase inhibitors.","authors":"Chita Ranjan Sahoo, Madhusmita Rout, Sudhir Kumar Paidesetty, Budheswar Dehury, Debdutta Bhattacharya, Sanghamitra Pati","doi":"10.3389/fbinf.2026.1876643","DOIUrl":"10.3389/fbinf.2026.1876643","url":null,"abstract":"<p><p>The widespread use of antibiotics, particularly ciprofloxacin, has accelerated the emergence of antibiotic-resistant bacteria, posing a major public health challenge. Limited investment and declining pharmaceutical interest have slowed the discovery of new antibiotics, contributing to the increasing prevalence of multidrug-resistant (MDR) infections, especially those caused by ESKAPE pathogens. Uropathogenic <i>Escherichia coli</i> (UPEC), a leading cause of urinary tract infections (UTIs), develops multidrug resistance through mechanisms including drug target modification and reduced antibiotic susceptibility. In the present study, a series of ciprofloxacin-sulfonamide hybrid molecules (CIS1-CIS15) was designed, in which each pharmacophore retains its established mechanism of action. Ciprofloxacin inhibits DNA gyrase and topoisomerase IV, whereas sulfonamides inhibit dihydropteroate synthase (DHPS) by competing with para-aminobenzoic acid (PABA) in the bacterial folate biosynthetic pathway. Accordingly, the designed hybrids were computationally evaluated against both enzymatic targets to investigate their potential dual-target antibacterial activity. Molecular docking and molecular dynamics (MD) simulations were performed to assess the binding affinity and stability of the ciprofloxacin-bearing sulfonamide derivatives (CIS1-CIS15) against DNA gyrase and <i>Staphylococcus aureus</i> DHPS. Principal component analysis (PCA) and free-energy landscape (FEL) analysis were used to characterize the conformational flexibility and low-energy states of the protein-ligand complexes, while MD simulations validated the stability of the highest-scoring docked complexes. Among the designed derivatives, CIS4 (ciprofloxacin-sulfamethoxazole), CIS5 (ciprofloxacin-sulfaguanidine), and CIS12 (ciprofloxacin-sulfamerazine) exhibited the strongest binding affinity toward both targets while maintaining conformational stability throughout the simulations. Structural analysis of the pre- and post-MD trajectories identified Arg52, Lys39, Arg88, Lys94, Lys120, Glu201, and Lys203 as key residues involved in ligand recognition. Overall, CIS4, CIS5, and CIS12 emerged as the most promising dual-target inhibitor of bacterial DHPS and DNA gyrase. These findings provide valuable structural insights for the rational design of novel fluoroquinolone-based antibacterial agents and demonstrate the utility of computational approaches in identifying potential therapeutics against drug-resistant bacterial pathogens.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1876643"},"PeriodicalIF":3.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software engineering for reproducible pipeline development in bioinformatics.","authors":"Daniel Pérez-Rodríguez, Alba Nogueira-Rodríguez, Jorge Vieira, Cristina P Vieira, Daniel Glez-Peña, Hugo López-Fernández","doi":"10.3389/fbinf.2026.1824590","DOIUrl":"10.3389/fbinf.2026.1824590","url":null,"abstract":"<p><p>Reproducibility in bioinformatics remains challenging despite the availability of workflow management systems and mature computational infrastructures. This work presents a software-engineering perspective for developing reproducible bioinformatics pipelines, with emphasis on pipeline-specific code. We reinterpret the SOLID principles in this context at two levels: workflow management systems (e.g., Nextflow, Snakemake, and Compi) and pipeline implementation. Our approach promotes pipeline designs based on well-defined task interfaces, explicit input/output specifications, and a clear separation between compute tasks and glue/adaptation tasks, in order to improve flexibility, reuse, and maintainability. The paper provides practical guidance for robust and reproducible pipeline development, including systematic validation checks (environment, inputs, and runtime), standardized project organization, and comprehensive testing strategies using both real and synthetic data within continuous integration workflows. It also discusses how modular ecosystems (such as nf-core modules and Snakemake wrappers) support these principles in community-driven environments. Finally, we relate these recommendations to FAIR-oriented research software guidelines (FAIR4RS and FAIRsoft), showing how core engineering practices strengthen robustness, portability, and long-term sustainability, thereby supporting reproducibility in bioinformatics pipelines.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1824590"},"PeriodicalIF":3.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial enumeration of extreme rays in metabolic networks using bit pattern trees.","authors":"Wannes Mores, Satyajeet Sheetal Bhonsale, Filip Logist, Jan F M Van Impe","doi":"10.3389/fbinf.2026.1892684","DOIUrl":"10.3389/fbinf.2026.1892684","url":null,"abstract":"<p><p>Extreme ray analysis of metabolic networks, even though very powerful, is currently limited to smaller metabolic networks. Some approaches to generating partial sets of extreme rays exist, but the computational efficiency of the so-called double-description method is yet to be exploited. Previous work highlighted the possibility of sampling within its iterations, enabling partial enumeration for double-description based methods. However, these approaches severely lack computational efficiency to be a suitable alternative. In this work, the highly efficient bit pattern trees are used within the sampling framework to significantly enhance its output and speed. Combined with the recent revision of the Canonical Basis Approach (CBA), our approach outperforms the other tested methods under the reported benchmark conditions even for a full enumeration study, requiring only half the computation time. In addition, a filter setting allows the memory demand to be scaled down while retaining high efficiency. However, some issues with the combinatorial explosion of candidates still persist and are further investigated. This study therefore puts forward a novel, double description-based alternative to partial enumeration of extreme rays. Further improvements in memory efficiency would allow this promising approach to scale powerful extreme ray-based analyses to genome-scale metabolic networks.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1892684"},"PeriodicalIF":3.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13574982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DBP-CanPred: a machine learning model for predicting cancer-causing mutations in DNA-binding proteins.","authors":"Amit Phogat, Sowmya Ramaswamy Krishnan, Medha Pandey, M Michael Gromiha","doi":"10.3389/fbinf.2026.1920228","DOIUrl":"10.3389/fbinf.2026.1920228","url":null,"abstract":"<p><strong>Introduction: </strong>The fundamental cellular processes, including transcriptional regulation, chromatin organization, and genome maintenance, are regulated by DNA-binding proteins (DBPs). Mutations in DBPs can alter protein-DNA interactions, leading to tumor development. However, identifying such driver mutations remains a major challenge due to limitations of experimental approaches.</p><p><strong>Methods: </strong>We have trained a machine learning model, DBP-CanPred, to identify driver mutations in DBPs. We used the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors.</p><p><strong>Results: </strong>We evaluated DBP-CanPred using a curated test set, achieving an AU-ROC of 0.86 and a balanced accuracy of 0.79. Further analysis based on substitution-type showed consistent performance across different categories, especially higher performance on charged residues. In addition, we applied the model on an independent dataset and identified potential driver mutations with high confidence scores.</p><p><strong>Discussion: </strong>The study contributes to understanding mutation patterns in DNA-binding proteins and supports variant interpretation in cancer research.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1920228"},"PeriodicalIF":3.6,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148956755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the impact of manual curation in BioCyc.","authors":"R Caspi, B Wilson-Mortier, L R Moore, J N Karp, D Beesley, I T Paulsen, P D Karp","doi":"10.3389/fbinf.2026.1887950","DOIUrl":"10.3389/fbinf.2026.1887950","url":null,"abstract":"<p><strong>Introduction: </strong>BioCyc is an extensive collection of databases of genomic and pathway information for microorganisms and model eukaryotes. These organismal databases integrate diverse biological data by combining computationally inferred information, data imported from other databases, and, for selected organisms, literature-based manual curation. This study investigates the magnitude and significance of annotation changes performed during the curation of 10 prokaryotic genomes to better understand the rate of erroneous annotations and the value of BioCyc curation.</p><p><strong>Methods: </strong>We identified curation changes by finding cases where the annotation of the protein at the start of the curation process differed from its annotation at the end of the process.</p><p><strong>Results: </strong>We found that across a sample of curated databases (<i>n</i> = 10), the annotation of 6,753, or 25.6% of the proteins in the pooled protein dataset (n = 26,126) were modified. Assessment of considerable sampling fractions of these proteins found that a median of 62% (mean of 52.9%) represented functionally informative name changes, rather than stylistic annotation changes. These results were then extrapolated to total proteins with name changes with uncertainty quantified via finite population correction, indicating that most Tier 2 Biocyc PGDBs received hundreds of functionally informative name changes during manual curation. On average 363, or13% (±5.4% SD) of the proteins encoded in each genome received functionally informative annotation changes, ranging from 5.3% <i>(Streptococcus pneumoniae</i> D39V) to 22.7% (<i>Staphylococcus aureus</i> NCTC 8325).</p><p><strong>Discussion: </strong>These findings demonstrate a substantial improvement in the accuracy of manually curated BioCyc databases compared with automated annotation pipelines. This result is particularly impactful as the rate of downstream propagation of erroneous annotations across biological databases can significantly compromise scientific discovery.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1887950"},"PeriodicalIF":3.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting IDH-driven Oncometabolism in glioblastoma: a computational approach for the identification of potential inhibitors.","authors":"Nivedhitha Tamilazhagan, Sivakumar Arumugam","doi":"10.3389/fbinf.2026.1884553","DOIUrl":"10.3389/fbinf.2026.1884553","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma (GBM) is a malignant brain tumor frequently driven by mutations in isocitrate dehydrogenase (IDH1 and IDH2) enzymes, which promote neomorphic synthesis of the oncometabolite 2-hydroxyglutarate (2-HG) and subsequent metabolic dysfunction. Although these mutations are associated with different clinical outcomes, therapeutic intervention remains challenging due to tumor heterogeneity, cellular plasticity, restricted blood-brain barrier permeability, drug efflux mechanisms, and effective DNA repair pathways. Therefore, this study aims to identify selective inhibitors of mutant IDH proteins by overcoming these challenges.</p><p><strong>Methods: </strong>10,309 compounds from ChemFaces and MedChemExpress libraries are used for large-scale virtual screening and multi-level ADMET filtering, uncovering 19 lead compounds with favourable drug-likeness. Further, molecular docking followed by 300 ns of molecular dynamics simulations, PCA analysis, and MM-PBSA calculations were performed to explore conformational dynamics.</p><p><strong>Results: </strong>Molecular docking revealed strong binding affinities of PubChem ID 91457 (I1Hit1) as -7.57 kcal/mol and PubChem ID 4946 (I1Hit2) as -6.82 kcal/mol against IDH1, and PubChem ID 71550939 (I2Hit1) as -11.23 kcal/mol and PubChem ID 4946 (I2Hit2) as -7.87 kcal/mol against IDH2, by outperforming the standard (-6.17 kcal/mol). Molecular dynamics simulations, PCA analysis, and MM-PBSA calculations confirmed complex stability and inhibitory potential. Notably, I1Hit1 and I2Hit1 exhibited enhanced binding free energies of -20.67 ± 2.32 kcal/mol against IDH1 and -28.78 ± 2.67 kcal/mol against IDH2 respectively, compared to the standard, as further supported by computational pharmacophore evaluation.</p><p><strong>Conclusion: </strong>Collectively, these findings highlight I1Hit1 and I2Hit1 as novel therapeutic compounds with efficient IDH-target inhibition to address epigenetic modification in GBM, and further experimental validation of these compounds is required to demonstrate potential inhibitors of IDH-driven metabolism in GBM.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"6 ","pages":"1884553"},"PeriodicalIF":3.6,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13553832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148921939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}