Pub Date : 2026-06-01Epub Date: 2026-02-10DOI: 10.1016/j.compbiolchem.2026.108940
Mohammed Alfaifi, Hossam Kamli
Triple-negative breast cancer (TNBC) lacks actionable targets and rapidly develops resistance; therefore, we used an integrative in silico approach to functionally characterise C11orf42 and assess its therapeutic relevance. Sequence and structural analyses revealed that C11orf42 is a ∼36.4 kDa soluble cytosolic protein composed of a conserved TED domain (residues 14–213; mean pLDDT ≈ 89) and a flexible intrinsically disordered C-terminal region (residues ∼232–333). Intrinsic disorder supports conformational flexibility; however, specific functional roles (e.g., protein–protein interaction scaffolding or signalling) remain hypothesis-generating and require orthogonal validation. Protein–protein interaction network analysis identified a highly enriched network (71 nodes, 432 edges; p < 1.0 × 10⁻¹⁶), implicating C11orf42 in vesicular trafficking, receptor recycling, and oncogenic signalling pathways relevant to TNBC. Structure-based druggability analysis revealed four ligandable pockets, and molecular docking identified four phytochemicals—chamaejasmin, Genetin J, isomultiflorenol, and podocarpusflavone B—with favorable binding affinities (≈ –8.9 to –9.6 kcal/mol). In 100-ns MD simulations, the full-length protein showed RMSD ∼10–12 Å due to C-terminal disorder, while the TED core (residues ∼14–213) remained stable at 2–3 Å. In-silico profiling indicates Chamaejasmin is a beyond-Ro5, high-affinity, long-residence C11orf42 inhibitor (t½ = 118.07 h; logP = 2.87) with acceptable safety but poor solubility (logS = −6.36), limited oral bioavailability (39.98 %), and multiple drug-likeness violations, making formulation/scaffold optimisation the main barrier. Importantly, functional genomics analysis of DepMap CRISPR-Cas9 screening data shows that C11orf42 is not a pan-essential viability gene but displays a context-restricted dependency profile, consistent with a regulatory or modulatory role rather than a core survival function. Collectively, these results prioritise C11orf42 as a computationally inferred, conditionally relevant regulatory candidate for further experimental evaluation in TNBC and provide a hypothesis-generating structural, network, and functional framework for future validation.
{"title":"In silico characterisation of C11orf42 as a potential therapeutic target in triple-negative breast cancer","authors":"Mohammed Alfaifi, Hossam Kamli","doi":"10.1016/j.compbiolchem.2026.108940","DOIUrl":"10.1016/j.compbiolchem.2026.108940","url":null,"abstract":"<div><div>Triple-negative breast cancer (TNBC) lacks actionable targets and rapidly develops resistance; therefore, we used an integrative in silico approach to functionally characterise C11orf42 and assess its therapeutic relevance. Sequence and structural analyses revealed that C11orf42 is a ∼36.4 kDa soluble cytosolic protein composed of a conserved TED domain (residues 14–213; mean pLDDT ≈ 89) and a flexible intrinsically disordered C-terminal region (residues ∼232–333). Intrinsic disorder supports conformational flexibility; however, specific functional roles (e.g., protein–protein interaction scaffolding or signalling) remain hypothesis-generating and require orthogonal validation. Protein–protein interaction network analysis identified a highly enriched network (71 nodes, 432 edges; p < 1.0 × 10⁻¹⁶), implicating C11orf42 in vesicular trafficking, receptor recycling, and oncogenic signalling pathways relevant to TNBC. Structure-based druggability analysis revealed four ligandable pockets, and molecular docking identified four phytochemicals—chamaejasmin, Genetin J, isomultiflorenol, and podocarpusflavone B—with favorable binding affinities (≈ –8.9 to –9.6 kcal/mol). In 100-ns MD simulations, the full-length protein showed RMSD ∼10–12 Å due to C-terminal disorder, while the TED core (residues ∼14–213) remained stable at 2–3 Å. In-silico profiling indicates Chamaejasmin is a beyond-Ro5, high-affinity, long-residence C11orf42 inhibitor (t½ = 118.07 h; logP = 2.87) with acceptable safety but poor solubility (logS = −6.36), limited oral bioavailability (39.98 %), and multiple drug-likeness violations, making formulation/scaffold optimisation the main barrier. Importantly, functional genomics analysis of DepMap CRISPR-Cas9 screening data shows that C11orf42 is not a pan-essential viability gene but displays a context-restricted dependency profile, consistent with a regulatory or modulatory role rather than a core survival function<strong>.</strong> Collectively, these results prioritise C11orf42 as a computationally inferred, conditionally relevant regulatory candidate for further experimental evaluation in TNBC and provide a hypothesis-generating structural, network, and functional framework for future validation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108940"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2025-12-29DOI: 10.1016/j.compbiolchem.2025.108872
Varsha Rani , Chetan Chauhan , R.S. Sengar
DNA barcoding has revolutionized species identification and biodiversity assessment by employing short, standardized genetic sequences as molecular markers. Since its inception by Hebert in 2003, it has become a cornerstone of taxonomy, ecology, conservation, agriculture, and medicine. This review traces the historical development of DNA barcoding, highlighting the strengths and limitations of widely used markers such as COI in animals, ITS in fungi, rbcL and matK in plants, and alternative loci in algae. The discussion emphasizes how barcoding enables accurate identification of cryptic taxa, supports food and forensic authentication, and strengthens biodiversity monitoring across ecosystems. Advancements in multi-locus strategies, genome-based markers, and DNA metabarcoding have enhanced resolution and scalability, while next-generation sequencing, environmental DNA, and nanotechnology promise to overcome persistent challenges of low variability, amplification barriers, and incomplete reference databases. Despite ongoing limitations, DNA barcoding continues to be an indispensable, cost-effective tool that bridges classical taxonomy with modern genomics. By integrating emerging technologies and fostering global collaboration, it holds immense potential for transforming biodiversity science and ensuring sustainable ecosystem management in the genomic era.
{"title":"DNA barcoding markers: A comprehensive review and taxonomic classification across species","authors":"Varsha Rani , Chetan Chauhan , R.S. Sengar","doi":"10.1016/j.compbiolchem.2025.108872","DOIUrl":"10.1016/j.compbiolchem.2025.108872","url":null,"abstract":"<div><div>DNA barcoding has revolutionized species identification and biodiversity assessment by employing short, standardized genetic sequences as molecular markers. Since its inception by Hebert in 2003, it has become a cornerstone of taxonomy, ecology, conservation, agriculture, and medicine. This review traces the historical development of DNA barcoding, highlighting the strengths and limitations of widely used markers such as COI in animals, ITS in fungi, <em>rbcL and matK</em> in plants, and alternative loci in algae. The discussion emphasizes how barcoding enables accurate identification of cryptic taxa, supports food and forensic authentication, and strengthens biodiversity monitoring across ecosystems. Advancements in multi-locus strategies, genome-based markers, and DNA metabarcoding have enhanced resolution and scalability, while next-generation sequencing, environmental DNA, and nanotechnology promise to overcome persistent challenges of low variability, amplification barriers, and incomplete reference databases. Despite ongoing limitations, DNA barcoding continues to be an indispensable, cost-effective tool that bridges classical taxonomy with modern genomics. By integrating emerging technologies and fostering global collaboration, it holds immense potential for transforming biodiversity science and ensuring sustainable ecosystem management in the genomic era.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108872"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145898002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-08DOI: 10.1016/j.compbiolchem.2026.108895
UK Shajil , Jaleel UCA , S. Sathish , Sandesh EPA , A. Sujith , Baiju G. Nair
This study builds on the premise that phytochemicals, when co-existing across multiple plant species, tend to form structural and functional clusters. While individual compounds may exhibit distinct pharmacological properties in isolation, their behaviour within molecular clusters often diverges, potentially leading to emergent synergistic effects. Leveraging this insight, we systematically analysed 490 phytoconstituents derived from ten medicinal plants belonging to the Dasamoola group. Through chemoinformatic clustering using K-Means and dimensionality reduction via t-SNE, these molecules were organised into 49 structurally coherent clusters. Pharmacological relevance was assessed by mapping clusters to 87 ICD-11-classified disease conditions, thereby integrating clustering, ICD-11 mapping, and knowledge-graph visualization into a unified workflow that can serve as a template for analysing other complex polyherbal formulations. Heat map analyses revealed significant correlations between molecular clusters and disease phenotypes, indicating potential poly pharmacological mechanisms. To further elucidate these relationships, predicted molecular targets were integrated with disease ontologies using a Neo4j-based knowledge graph framework. This network-based approach enabled the visualization of molecule–target–disease associations, suggesting mechanistic insights that extend beyond conventional reductionist perspectives in an exploratory manner. Overall, our findings suggest potential molecular–target–disease associations that link structurally related phytochemicals to defined disease categories through shared biological targets. These associations indicate plausible network-level relationships and offer new avenues for, understanding the systems-level pharmacology of traditional medicinal formulations, generating testable hypothesis that warrant further experimental validation.
{"title":"Knowledge graph integration of clustered medicinal plants, molecules, diseases, and targets","authors":"UK Shajil , Jaleel UCA , S. Sathish , Sandesh EPA , A. Sujith , Baiju G. Nair","doi":"10.1016/j.compbiolchem.2026.108895","DOIUrl":"10.1016/j.compbiolchem.2026.108895","url":null,"abstract":"<div><div>This study builds on the premise that phytochemicals, when co-existing across multiple plant species, tend to form structural and functional clusters. While individual compounds may exhibit distinct pharmacological properties in isolation, their behaviour within molecular clusters often diverges, potentially leading to emergent synergistic effects. Leveraging this insight, we systematically analysed 490 phytoconstituents derived from ten medicinal plants belonging to the Dasamoola group. Through chemoinformatic clustering using K-Means and dimensionality reduction via t-SNE, these molecules were organised into 49 structurally coherent clusters. Pharmacological relevance was assessed by mapping clusters to 87 ICD-11-classified disease conditions, thereby integrating clustering, ICD-11 mapping, and knowledge-graph visualization into a unified workflow that can serve as a template for analysing other complex polyherbal formulations. Heat map analyses revealed significant correlations between molecular clusters and disease phenotypes, indicating potential poly pharmacological mechanisms. To further elucidate these relationships, predicted molecular targets were integrated with disease ontologies using a Neo4j-based knowledge graph framework. This network-based approach enabled the visualization of molecule–target–disease associations, suggesting mechanistic insights that extend beyond conventional reductionist perspectives in an exploratory manner. Overall, our findings suggest potential molecular–target–disease associations that link structurally related phytochemicals to defined disease categories through shared biological targets. These associations indicate plausible network-level relationships and offer new avenues for, understanding the systems-level pharmacology of traditional medicinal formulations, generating testable hypothesis that warrant further experimental validation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108895"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recombinant West Nile virus (WNV) envelope (Env) protein WN80E is one of the few WNV antigens that has been evaluated in humans and has demonstrated robust immunogenicity across animal models and in a Phase I clinical trial. Here, we performed a retrospective in silico analysis of WN80E and its variant containing a N-terminal methionine (WNM80E) to assess how accurately contemporary computational vaccinology tools can capture and differentiate their structural and immunological profiles. Physicochemical and sequence-based predictions classified both constructs as antigenic, non-allergenic, and containing stable disulfide patterns, although WNM80E exhibited markedly improved predicted in vitro and in vivo half-lives. Tertiary structure modeling and subsequent refinement produced high-confidence homodimeric structures for both antigens, with favorable stereochemical metrics and preserved quaternary organization. Conformational B cell epitope mapping identified hinge-proximal and domain II antigenic patches with high solvent accessibility and minimal glycan shielding. Molecular docking with the broadly neutralizing monoclonal antibody CR4354 yielded energetically favorable complexes for both constructs, with slightly enhanced interface complementarity for WNM80E. Immune simulations predicted strong and durable humoral and cellular responses for both antigens, dominated by a Th1 signature, sustained memory formation, and repeated antigen clearance following booster doses. These findings demonstrate that results from in silico vaccinology tools further support continued evaluation of the clinically tested WN80E antigen in clinical trials and identify WNM80E as a structurally and immunologically comparable variant with modestly improved predicted stability and immunogenicity. This work highlights the utility of integrated computational pipelines for antigen evaluation.
{"title":"In silico post-hoc analysis of a clinically tested recombinant West Nile virus envelope protein vaccine","authors":"Jesús Reiné , Rosaria Tinnirello , Alberto Cagigi , Chiuan Yee Leow , Chiuan Herng Leow , Gioacchin Iannolo , Bruno Douradinha","doi":"10.1016/j.compbiolchem.2026.108890","DOIUrl":"10.1016/j.compbiolchem.2026.108890","url":null,"abstract":"<div><div>The recombinant West Nile virus (WNV) envelope (Env) protein WN80E is one of the few WNV antigens that has been evaluated in humans and has demonstrated robust immunogenicity across animal models and in a Phase I clinical trial. Here, we performed a retrospective in silico analysis of WN80E and its variant containing a N-terminal methionine (WNM80E) to assess how accurately contemporary computational vaccinology tools can capture and differentiate their structural and immunological profiles. Physicochemical and sequence-based predictions classified both constructs as antigenic, non-allergenic, and containing stable disulfide patterns, although WNM80E exhibited markedly improved predicted in vitro and in vivo half-lives. Tertiary structure modeling and subsequent refinement produced high-confidence homodimeric structures for both antigens, with favorable stereochemical metrics and preserved quaternary organization. Conformational B cell epitope mapping identified hinge-proximal and domain II antigenic patches with high solvent accessibility and minimal glycan shielding. Molecular docking with the broadly neutralizing monoclonal antibody CR4354 yielded energetically favorable complexes for both constructs, with slightly enhanced interface complementarity for WNM80E. Immune simulations predicted strong and durable humoral and cellular responses for both antigens, dominated by a Th1 signature, sustained memory formation, and repeated antigen clearance following booster doses. These findings demonstrate that results from in silico vaccinology tools further support continued evaluation of the clinically tested WN80E antigen in clinical trials and identify WNM80E as a structurally and immunologically comparable variant with modestly improved predicted stability and immunogenicity. This work highlights the utility of integrated computational pipelines for antigen evaluation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108890"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-26DOI: 10.1016/j.compbiolchem.2026.108927
Pengfei Wang , Pengpeng Dong , Zikai Geng , Yizhuo Gong , Wenze Cui , Wenhuan Song , Yiyang Liu , Wenhao Yu , Lianzhi Ren , Jiantao Lv , Mingkun Yu
Background
Colorectal cancer(CRC) makes difficulties to human beings. KCNQ1 is a possible tumor suppressor among potassium channels, but it is not yet known if there is any tumor suppression and whether it is epigenetically regulated in CRC. Dahuang Zhechong Pills (DHZCP) is a traditional Chinese medicine with anti-tumor effects, but its mechanisms, especially the KCNQ1 and cuproptosis pathways, still need to be elucidated. The motivation of this study is to draw up a high-resolution mechanistic map of Dahuang Zhechong Pills (DHZCP) by combining genetic causality with function validation, so as to raise both the academic value and clinical value of traditional Chinese medicine (TCM) in the management of CRC.
Methods
The integrative causal-validation framework was carried out. This study used the SMR based on the summary data to measure the causal effect of methylation on KCNQ1 expression, network pharmacology and molecular docking were used to predict the DHZCP targets, and in vitro studies were conducted with HCT-116 CRC cells. In vitro study was carried out in KCNQ1 overexpression (KCNQ1-OE) and DHZCP treated cells to study the effect of cell proliferation, apoptosis, migration, invasion, oxidative stress, and intracellular copper and expression of cuproptosis related protein (FDX1, DLAT, LIAS). Set up of clinical potency by systematically meta-analytical researches.
Results
SMR showed that KCNQ1 methylation negative regulate KCNQ1. KCNQ1 overexpression inhibited HCT-116 cell proliferation, migration, invasion and induced apoptosis, Oxidative stress. DHZCP-containing serum replicating and increasing these ones. Both decreased FDX1, DLAT and LIAS, increased ROS, MDA, 4-HNE and intracellular copper. DHZCP components bound directly to KCNQ1 in silico and multi-target actions against CRC were implied by network pharmacology. Meta-analysis of the clinical benefits of DHZCP in cancer therapy.
Conclusion
KCNQ1 is a tumor suppressor of CRC that is DNA methylated. DHZCP in combination with KCNQ1 overexpression exhibits anti-CRC effects through the regulation of cuproptosis-related pathways, cuproptosis is promoted, oxidative stress is enhanced, and copper accumulates, thus supporting the clinical application prospects of DHZCP in CRC.
{"title":"Integrated analysis and functional validation reveal KCNQ1 tumor suppressor targeting by dahuang Zhechong Pills via cuproptosis modulation in colorectal cancer","authors":"Pengfei Wang , Pengpeng Dong , Zikai Geng , Yizhuo Gong , Wenze Cui , Wenhuan Song , Yiyang Liu , Wenhao Yu , Lianzhi Ren , Jiantao Lv , Mingkun Yu","doi":"10.1016/j.compbiolchem.2026.108927","DOIUrl":"10.1016/j.compbiolchem.2026.108927","url":null,"abstract":"<div><h3>Background</h3><div>Colorectal cancer(CRC) makes difficulties to human beings. KCNQ1 is a possible tumor suppressor among potassium channels, but it is not yet known if there is any tumor suppression and whether it is epigenetically regulated in CRC. Dahuang Zhechong Pills (DHZCP) is a traditional Chinese medicine with anti-tumor effects, but its mechanisms, especially the KCNQ1 and cuproptosis pathways, still need to be elucidated. The motivation of this study is to draw up a high-resolution mechanistic map of Dahuang Zhechong Pills (DHZCP) by combining genetic causality with function validation, so as to raise both the academic value and clinical value of traditional Chinese medicine (TCM) in the management of CRC.</div></div><div><h3>Methods</h3><div>The integrative causal-validation framework was carried out. This study used the SMR based on the summary data to measure the causal effect of methylation on KCNQ1 expression, network pharmacology and molecular docking were used to predict the DHZCP targets, and in vitro studies were conducted with HCT-116 CRC cells. In vitro study was carried out in KCNQ1 overexpression (KCNQ1-OE) and DHZCP treated cells to study the effect of cell proliferation, apoptosis, migration, invasion, oxidative stress, and intracellular copper and expression of cuproptosis related protein (FDX1, DLAT, LIAS). Set up of clinical potency by systematically meta-analytical researches.</div></div><div><h3>Results</h3><div>SMR showed that KCNQ1 methylation negative regulate KCNQ1. KCNQ1 overexpression inhibited HCT-116 cell proliferation, migration, invasion and induced apoptosis, Oxidative stress. DHZCP-containing serum replicating and increasing these ones. Both decreased FDX1, DLAT and LIAS, increased ROS, MDA, 4-HNE and intracellular copper. DHZCP components bound directly to KCNQ1 in silico and multi-target actions against CRC were implied by network pharmacology. Meta-analysis of the clinical benefits of DHZCP in cancer therapy.</div></div><div><h3>Conclusion</h3><div>KCNQ1 is a tumor suppressor of CRC that is DNA methylated. DHZCP in combination with KCNQ1 overexpression exhibits anti-CRC effects through the regulation of cuproptosis-related pathways, cuproptosis is promoted, oxidative stress is enhanced, and copper accumulates, thus supporting the clinical application prospects of DHZCP in CRC.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108927"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div>This study employed an integrative computational and systems biology framework to define a diagnostic gene signature for hepatocellular carcinoma (HCC) and to explore its potential translational relevance in a hypothesis-generating manner. Differential expression analysis of transcriptomic data from 230 samples identified 2748 significantly differentially expressed genes (DEGs), including 2283 upregulated and 465 downregulated genes, with FGF4 (log2FC = 10.08) and REG1B (log2FC = 10.02) among the top hits. Four machine learning classifiers were trained using this signature and demonstrated consistently high predictive performance, with XGBoost emerging as the top-performing model (accuracy = 0.97, F1-score = 0.96, ROC-AUC = 0.981). Logistic Regression (L1) and Random Forest achieved comparable performance (ROC-AUC = 0.980 and 0.979, respectively), while SVM-linear also showed high robustness (ROC-AUC = 0.978). All models showed good calibration, with low Brier scores (<0.04) and precision consistently exceeding 0.90 across most recall thresholds, indicating strong but not perfect classification performance. SHAP-based explainability analysis was used to rank and prioritise the most influential predictors, refining the biomarker panel to 81 genes that collectively accounted for approximately 50 % of the model’s explanatory contribution, and highlighting key downregulated predictors in HCC, including GDF2, COLEC10, BMP10, LRAT, and DNASE1L3. Protein–protein interaction and functional enrichment analyses revealed five major molecular clusters and provided systems-level insights into dysregulated biological processes associated with HCC. Drug–gene interaction mining mapped 78 target proteins to clinically relevant compounds, including tolrestat, alcuronium, metyrosine, and 4-phenylbutyric acid. Molecular docking suggested favorable binding propensities for several complexes, including alcuronium–3UON (–8.5 kcal/mol), tolrestat–1ZUA (–8.3 kcal/mol), metyrosine–2XSN (–6.7 kcal/mol), and 4-phenylbutyric acid–2NZ2 (–5.9 kcal/mol). A 100 ns molecular dynamics simulation of the tolrestat–AKR1B10 (1ZUA) complex indicated structural stability, with protein backbone RMSD stabilising at 1.5–3.0 Å, ligand RMSD at 0.6–1.4 Å, and persistent interactions involving Trp22, His110, Glu111, and Phe122. Physicochemical and pharmacokinetic profiling further prioritised tolrestat as a computationally favourable candidate (MW = 357.35, LogP = 3.64, TPSA = 81.86 Ų), exhibiting acceptable drug-likeness, high predicted gastrointestinal absorption, and low synthetic complexity (SA = 2.34), in contrast to alcuronium (MW = 666.89, SA = 7.86), which showed multiple rule violations. Collectively, this in silico study proposes a robust diagnostic gene signature for HCC and identifies tolrestat as a promising repurposing candidate that warrants experimental validation, demonstrating the utility of integrating machine learning, network biology, and molecular simulation
{"title":"Biomarker discovery and drug repurposing in hepatocellular carcinoma through transcriptomics, machine learning, network pharmacology, and molecular dynamics","authors":"Mohammed Alfaifi , Hossam Kamli , Najeeb Ullah Khan , Ahsanullah Unar","doi":"10.1016/j.compbiolchem.2026.108937","DOIUrl":"10.1016/j.compbiolchem.2026.108937","url":null,"abstract":"<div><div>This study employed an integrative computational and systems biology framework to define a diagnostic gene signature for hepatocellular carcinoma (HCC) and to explore its potential translational relevance in a hypothesis-generating manner. Differential expression analysis of transcriptomic data from 230 samples identified 2748 significantly differentially expressed genes (DEGs), including 2283 upregulated and 465 downregulated genes, with FGF4 (log2FC = 10.08) and REG1B (log2FC = 10.02) among the top hits. Four machine learning classifiers were trained using this signature and demonstrated consistently high predictive performance, with XGBoost emerging as the top-performing model (accuracy = 0.97, F1-score = 0.96, ROC-AUC = 0.981). Logistic Regression (L1) and Random Forest achieved comparable performance (ROC-AUC = 0.980 and 0.979, respectively), while SVM-linear also showed high robustness (ROC-AUC = 0.978). All models showed good calibration, with low Brier scores (<0.04) and precision consistently exceeding 0.90 across most recall thresholds, indicating strong but not perfect classification performance. SHAP-based explainability analysis was used to rank and prioritise the most influential predictors, refining the biomarker panel to 81 genes that collectively accounted for approximately 50 % of the model’s explanatory contribution, and highlighting key downregulated predictors in HCC, including GDF2, COLEC10, BMP10, LRAT, and DNASE1L3. Protein–protein interaction and functional enrichment analyses revealed five major molecular clusters and provided systems-level insights into dysregulated biological processes associated with HCC. Drug–gene interaction mining mapped 78 target proteins to clinically relevant compounds, including tolrestat, alcuronium, metyrosine, and 4-phenylbutyric acid. Molecular docking suggested favorable binding propensities for several complexes, including alcuronium–3UON (–8.5 kcal/mol), tolrestat–1ZUA (–8.3 kcal/mol), metyrosine–2XSN (–6.7 kcal/mol), and 4-phenylbutyric acid–2NZ2 (–5.9 kcal/mol). A 100 ns molecular dynamics simulation of the tolrestat–AKR1B10 (1ZUA) complex indicated structural stability, with protein backbone RMSD stabilising at 1.5–3.0 Å, ligand RMSD at 0.6–1.4 Å, and persistent interactions involving Trp22, His110, Glu111, and Phe122. Physicochemical and pharmacokinetic profiling further prioritised tolrestat as a computationally favourable candidate (MW = 357.35, LogP = 3.64, TPSA = 81.86 Ų), exhibiting acceptable drug-likeness, high predicted gastrointestinal absorption, and low synthetic complexity (SA = 2.34), in contrast to alcuronium (MW = 666.89, SA = 7.86), which showed multiple rule violations. Collectively, this in silico study proposes a robust diagnostic gene signature for HCC and identifies tolrestat as a promising repurposing candidate that warrants experimental validation, demonstrating the utility of integrating machine learning, network biology, and molecular simulation","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108937"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146168556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-20DOI: 10.1016/j.compbiolchem.2026.108918
Assad Rasheed , Syed Hamad Shirazi , Pordil Khan , Ali M. Aseere , Atef masmoudi
Cervical nuclei segmentation is critical for the early detection and accurate diagnosis of cervical cancer. However, this task is challenging due to the presence of clumped nuclei and variations in texture, shape, and contrast. To address these challenges, we proposed a novel synergic conditional generative adversarial network (SCGAN) for cervical nuclei segmentation. The SCGAN integrates densely connected blocks that progressively extract hierarchical features, a Unified Attention Module (UAM) for selective feature refinement and the Scale-Adaptive Feature Integration and upsampling (SAFIU) module for multi-scale feature integration and upsampling, and a synergic discriminator to enhance adversarial learning. The SAFIU module constructs a multi-scale feature pyramid by progressively upsampling across feature levels, effectively retaining fine spatial details critical for segmenting small nuclei. The Scale-Adaptive Fusion (SAF) block further facilitates feature learning by merging high-level features with low-level spatial cues from the encoder, and then forwarding the fused representation to the corresponding decoder stage. On the adversarial side, the synergic discriminator, consisting of ResNet-50 and EfficientNet-B2, is designed for collaborative learning and accelerates convergence with the help of a synergic block. The integration of an Uncertainty-Aware Attention (UAA) mechanism in the synergic block helps the discriminators concentrate on ambiguous or overlapping regions, thereby providing more informative feedback to the generator. Experiments on multiple cervical nuclei datasets demonstrated that the proposed SCGAN outperformed existing methods in terms of sensitivity, specificity, Dice coefficient, and F1-score. By effectively integrating multi-scale features and leveraging adversarial training, our SCGAN achieves more accurate and more consistent cervical nuclei segmentation, paving the way for improved computer-aided diagnosis systems.
{"title":"cervical nuclei segmentation through synergic conditional generative adversarial network in cervical smear images","authors":"Assad Rasheed , Syed Hamad Shirazi , Pordil Khan , Ali M. Aseere , Atef masmoudi","doi":"10.1016/j.compbiolchem.2026.108918","DOIUrl":"10.1016/j.compbiolchem.2026.108918","url":null,"abstract":"<div><div>Cervical nuclei segmentation is critical for the early detection and accurate diagnosis of cervical cancer. However, this task is challenging due to the presence of clumped nuclei and variations in texture, shape, and contrast. To address these challenges, we proposed a novel synergic conditional generative adversarial network (SCGAN) for cervical nuclei segmentation. The SCGAN integrates densely connected blocks that progressively extract hierarchical features, a Unified Attention Module (UAM) for selective feature refinement and the Scale-Adaptive Feature Integration and upsampling (SAFIU) module for multi-scale feature integration and upsampling, and a synergic discriminator to enhance adversarial learning. The SAFIU module constructs a multi-scale feature pyramid by progressively upsampling across feature levels, effectively retaining fine spatial details critical for segmenting small nuclei. The Scale-Adaptive Fusion (SAF) block further facilitates feature learning by merging high-level features with low-level spatial cues from the encoder, and then forwarding the fused representation to the corresponding decoder stage. On the adversarial side, the synergic discriminator, consisting of ResNet-50 and EfficientNet-B2, is designed for collaborative learning and accelerates convergence with the help of a synergic block. The integration of an Uncertainty-Aware Attention (UAA) mechanism in the synergic block helps the discriminators concentrate on ambiguous or overlapping regions, thereby providing more informative feedback to the generator. Experiments on multiple cervical nuclei datasets demonstrated that the proposed SCGAN outperformed existing methods in terms of sensitivity, specificity, Dice coefficient, and F1-score. By effectively integrating multi-scale features and leveraging adversarial training, our SCGAN achieves more accurate and more consistent cervical nuclei segmentation, paving the way for improved computer-aided diagnosis systems.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108918"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-30DOI: 10.1016/j.compbiolchem.2026.108917
Jay Raval , Kamalesh V.N. , Dr. Raj Kumar Patra
Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the identification of arrhythmias; yet, most of the techniques have been utilized sources such as Electrocardiogram (ECG). The ECG-based manual evaluation by the medical analysts is inaccurate. Some experiments have been concentrated on the accuracy and the speed of the learning method by utilizing Artificial Intelligence (AI), and pattern detection in the classification model. However, there are two primary limitations in the conventional mechanisms; the models demand large training time and demand feature selection on a manual basis. Hence, an intellectual arrhythmia classification model using deep learning is introduced to identify the irregular heartbeat. In the beginning, the required signals are accumulated from standard sources. Further, three different kinds of features are extracted for an efficient automatic classification process of arrhythmia. At first, the deep features are extracted by applying the Conditional Autoencoder, and these features are considered as feature set 1. Further, wave features and spectral features are retrieved from the input signal and these features are considered as feature set 2. Subsequently, the signals are converted into spectrogram images and the Graph Convolutional Neural Network (GCNN) technique is employed to retrieve the feature set 3 from those images. Further, the ensemble feature fusion process takes place to combine all three sets of features. Ensemble features are provided as input for the Optimal Dense Recurrent neural network with Attention Mechanism (ODR-AM) for classifying the arrhythmia. The classifier’s performance is boosted by optimizing the parameters using the Augmented Random value of Giant Armadillo Optimization (ARGAO). This model is useful to know about the specific type of arrhythmia. Finally, the simulation findings of the presented model are analyzed with other conventional models.
{"title":"A meta-heuristic aided arrhythmia classification model using advanced deep learning technique with multiple feature extraction mechanisms","authors":"Jay Raval , Kamalesh V.N. , Dr. Raj Kumar Patra","doi":"10.1016/j.compbiolchem.2026.108917","DOIUrl":"10.1016/j.compbiolchem.2026.108917","url":null,"abstract":"<div><div>Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the identification of arrhythmias; yet, most of the techniques have been utilized sources such as Electrocardiogram (ECG). The ECG-based manual evaluation by the medical analysts is inaccurate. Some experiments have been concentrated on the accuracy and the speed of the learning method by utilizing Artificial Intelligence (AI), and pattern detection in the classification model. However, there are two primary limitations in the conventional mechanisms; the models demand large training time and demand feature selection on a manual basis. Hence, an intellectual arrhythmia classification model using deep learning is introduced to identify the irregular heartbeat. In the beginning, the required signals are accumulated from standard sources. Further, three different kinds of features are extracted for an efficient automatic classification process of arrhythmia. At first, the deep features are extracted by applying the Conditional Autoencoder, and these features are considered as feature set 1. Further, wave features and spectral features are retrieved from the input signal and these features are considered as feature set 2. Subsequently, the signals are converted into spectrogram images and the Graph Convolutional Neural Network (GCNN) technique is employed to retrieve the feature set 3 from those images. Further, the ensemble feature fusion process takes place to combine all three sets of features. Ensemble features are provided as input for the Optimal Dense Recurrent neural network with Attention Mechanism (ODR-AM) for classifying the arrhythmia. The classifier’s performance is boosted by optimizing the parameters using the Augmented Random value of Giant Armadillo Optimization (ARGAO). This model is useful to know about the specific type of arrhythmia. Finally, the simulation findings of the presented model are analyzed with other conventional models.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108917"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-08DOI: 10.1016/j.compbiolchem.2026.108894
Antara Tandi, Dijendra Nath Roy
The persistence of Pseudomonas aeruginosa biofilm often renders antibiotic treatments ineffective, necessitating alternative approaches, such as biofilm inhibition by another drug molecule. In this study, andrographiside, a labdane diterpenoid glucoside, a secondary metabolite found in Andrographis paniculata, demonstrated potent antibiofilm activity. After an optimization study, andrographiside (0.1 mM) alone or combined with azithromycin (Sub-MIC 6 µg/mL) effectively inhibited Pseudomonas aeruginosa PAO1 biofilm formation. The Confocal Laser Scanning Microscope study further confirmed this biofilm inhibition by observing a reduction in biofilm height from 132 µm to 42 µm in the drug-treated samples. Not only that, but swarming/swimming/twitching motility was also significantly reduced due to treatment with andrographiside, which indicates less pathogenicity in the infection cycle. Moreover, on account of the mechanism, andrographiside binds Qurum Sensing Proteins (LasR and RhlI), Pseudomonas quinolone signal regulator (PqsR) and Pellicle B of PEL Operon −42.011, 59.071, −29.296, −33.485 Kcal/mol, respectively. A gene expression study revealed that PelA and PelB expression were enhanced 9- and 12-fold, respectively, as a survival strategy. These pathways are mutually inclusive for biofilm development in Pseudomonas aeruginosa PAO1, so molecular binding and simulation, along with altered gene expression, resulted in biofilm inhibition in the presence of andrographiside. Following this, the ADMET study of andrographiside confirmed the druggability of the molecule in both animal and human bodies.
铜绿假单胞菌生物膜的持久性经常使抗生素治疗无效，需要替代方法，如用另一种药物分子抑制生物膜。在这项研究中，穿心莲苷，一种双萜糖苷，在穿心莲中发现的次级代谢产物，显示出有效的抗膜活性。经优化研究，穿心龙苷（0.1 mM）单独或联合阿奇霉素（亚mic 6 µg/mL）可有效抑制铜绿假单胞菌PAO1生物膜的形成。共聚焦激光扫描显微镜研究进一步证实了这种生物膜抑制作用，观察到药物处理样品的生物膜高度从132 µm降低到42 µm。不仅如此，穿心莲内酯还显著降低了蜂群/游泳/抽搐的运动能力，表明在感染周期中致病性较低。此外，根据其作用机制，androandroide分别结合quum Sensing Proteins （LasR and RhlI）、Pseudomonas quinolone signal regulator （PqsR）和PEL Operon的PqsR，分别为-42.011、59.071、-29.296、-33.485 Kcal/mol。一项基因表达研究显示，作为一种生存策略，PelA和PelB的表达分别提高了9倍和12倍。这些途径在铜绿假单胞菌PAO1的生物膜发育中是相互包容的，因此分子结合和模拟，以及基因表达的改变，导致了穿心莲苷存在下的生物膜抑制。在此之后，对穿心莲苷的ADMET研究证实了该分子在动物和人体中的药物作用。
{"title":"Andrographiside acts as a novel biofilm inhibitor of Pseudomonas aeruginosa PAO1 by modulating quorum-sensing proteins (LasR and RhlI), Pseudomonas quinolone signal regulator (PqsR) and Pellicle B of PEL Operon: An in silico and in vitro approach","authors":"Antara Tandi, Dijendra Nath Roy","doi":"10.1016/j.compbiolchem.2026.108894","DOIUrl":"10.1016/j.compbiolchem.2026.108894","url":null,"abstract":"<div><div>The persistence of <em>Pseudomonas aeruginosa</em> biofilm often renders antibiotic treatments ineffective, necessitating alternative approaches, such as biofilm inhibition by another drug molecule. In this study, andrographiside, a labdane diterpenoid glucoside, a secondary metabolite found in <em>Andrographis paniculata</em>, demonstrated potent antibiofilm activity. After an optimization study, andrographiside (0.1 mM) alone or combined with azithromycin (Sub-MIC 6 µg/mL) effectively inhibited <em>Pseudomonas aeruginosa</em> PAO1 biofilm formation. The Confocal Laser Scanning Microscope study further confirmed this biofilm inhibition by observing a reduction in biofilm height from 132 µm to 42 µm in the drug-treated samples. Not only that, but swarming/swimming/twitching motility was also significantly reduced due to treatment with andrographiside, which indicates less pathogenicity in the infection cycle. Moreover, on account of the mechanism, andrographiside binds Qurum Sensing Proteins (LasR and RhlI), Pseudomonas quinolone signal regulator (PqsR) and Pellicle B of PEL Operon −42.011, 59.071, −29.296, −33.485 Kcal/mol, respectively. A gene expression study revealed that <em>PelA</em> and <em>PelB</em> expression were enhanced 9- and 12-fold, respectively, as a survival strategy. These pathways are mutually inclusive for biofilm development in <em>Pseudomonas aeruginosa</em> PAO1, so molecular binding and simulation, along with altered gene expression, resulted in biofilm inhibition in the presence of andrographiside. Following this, the ADMET study of andrographiside confirmed the druggability of the molecule in both animal and human bodies.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108894"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145968021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-25DOI: 10.1016/j.compbiolchem.2026.108924
Bishwajit Sarma , Hemen Dutta , Ujjal Das
We present a fractional model for cell cycle progression using the Caputo fractional-order derivative. An in-depth analysis of the existence, uniqueness, non-negativity, and boundedness of the solution has been carried out. Finally, the model has been applied to two cell lines: MCF-7 (Breast cancer) and A549 (Lung cancer). By combining the suggested mathematical model with experimental findings, Markov Chain Monte Carlo (MCMC) sampling was used to estimate model parameters within a Bayesian framework. The MCF-7 and A549 cell lines show almost the same values for the transition from the phase to the phase, with rates of and , respectively, according to the calculated transition rates. On the other hand, MCF-7 cells undergo a faster transition from the phase to the phase, with a rate of as opposed to in A549 cells. To assess how well MCMC investigated the posterior and generated reliable parameter values, trace plots have been used as a diagnostic tool.
{"title":"Fractional-order modelling of cancer cell population dynamics affected by radiation","authors":"Bishwajit Sarma , Hemen Dutta , Ujjal Das","doi":"10.1016/j.compbiolchem.2026.108924","DOIUrl":"10.1016/j.compbiolchem.2026.108924","url":null,"abstract":"<div><div>We present a fractional model for cell cycle progression using the Caputo fractional-order derivative. An in-depth analysis of the existence, uniqueness, non-negativity, and boundedness of the solution has been carried out. Finally, the model has been applied to two cell lines: MCF-7 (Breast cancer) and A549 (Lung cancer). By combining the suggested mathematical model with experimental findings, Markov Chain Monte Carlo (MCMC) sampling was used to estimate model parameters within a Bayesian framework. The MCF-7 and A549 cell lines show almost the same values for the transition from the <span><math><mrow><msub><mrow><mi>G</mi></mrow><mrow><mn>2</mn></mrow></msub><mi>M</mi></mrow></math></span> phase to the <span><math><msub><mrow><mi>G</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> phase, with rates of <span><math><mrow><mn>0.2</mn><mspace></mspace><msup><mrow><mi>h</mi></mrow><mrow><mo>−</mo><mn>0.9</mn></mrow></msup></mrow></math></span> and <span><math><mrow><mn>0.189</mn><mspace></mspace><msup><mrow><mi>h</mi></mrow><mrow><mo>−</mo><mn>0.9</mn></mrow></msup></mrow></math></span>, respectively, according to the calculated transition rates. On the other hand, MCF-7 cells undergo a faster transition from the <span><math><msub><mrow><mi>G</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> phase to the <span><math><mi>S</mi></math></span> phase, with a rate of <span><math><mrow><mn>0.159</mn><mspace></mspace><msup><mrow><mi>h</mi></mrow><mrow><mo>−</mo><mn>0.9</mn></mrow></msup></mrow></math></span> as opposed to <span><math><mrow><mn>0.095</mn><mspace></mspace><msup><mrow><mi>h</mi></mrow><mrow><mo>−</mo><mn>0.9</mn></mrow></msup></mrow></math></span> in A549 cells. To assess how well MCMC investigated the posterior and generated reliable parameter values, trace plots have been used as a diagnostic tool.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"122 ","pages":"Article 108924"},"PeriodicalIF":3.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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