Recep Adiyaman
bioinformatics

Issue #27: AlphaFold can help African researchers to do cutting-edge structural biology.

January 21, 2026 Daily Intelligence
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AlphaFold can help African researchers to do cutting-edge structural biology.

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Exploring the mechanism of Kemofang in treating idiopathic membranous nephropathy based on LC-MS/MS combined with network pharmacology, molecular docking, and molecular dynamics simulation.

Idiopathic membranous nephropathy (IMN), an autoimmune glomerular disease, arises from in situ immune complex deposition in the glomerular subepithelial spaces, triggering complement activation and podocyte injury. Although the Kemo Formula shows therapeutic potential for IMN, its mechanisms remain unclear. This study employed LC-MS/MS, network pharmacology, molecular docking, and dynamic simulations to elucidate the mechanism of action. LC-MS/MS and the TCMSP database identified 83 bioactive components from 267 chemicals detected in the Kemo Formula. Using PubChem, Swiss Target Prediction, and GeneCards, 827 drug targets and 2581 IMN-related targets were screened, yielding 336 overlapping targets linked to 81 components. Network analysis prioritized 15 key components ( baicalein and quercetin) and 36 core targets (TP53, IL6, and AKT1). Functional enrichment revealed involvement in hormone response, MAPK cascade regulation, and kinase binding with pathways including lipid metabolism, PI3K/Akt, and MAPK signaling. Molecular docking indicated strong binding affinities between the active components and targets, while dynamic simulations predicted the stability of the galangin-AKT1 complex. The Kemo Formula likely mitigates IMN by multi-target modulation, ameliorating lipid dysregulation, suppressing podocyte apoptosis, and attenuating immune-inflammatory and oxidative stress via PI3K/Akt and MAPK pathways. This integrative approach highlights its multicomponent, multitarget therapeutic strategy against IMN, providing a foundation for further mechanistic and clinical exploration.

Discovery of ActRIIB antagonistic peptides from in vitro-digested chicken breast meat via an integrated Peptidomics and molecular docking approach.

Sarcopenia and obesity are major global health challenges. This study investigated peptides from chicken breast meat via in vitro digestion as potent ActRIIB antagonists to promote myogenesis. The intestinal-phase digest collected at 120 min showed the highest degree of hydrolysis (65.99 % ± 4.00 %) and enhanced C2C12 proliferation (128.15 % ± 9.90 %). Peptidomics identified peptides mainly from myofibrillar proteins and metabolic enzymes. Molecular docking revealed key hydrogen-bonding residues, including Glu 95 , Pro 117 , Glu 94 , Thr 93 , Asn 96 (Chain A), Ser 97 (Chain L), and Ser 59 (Chain H). Surface plasmon resonance showed that KEKLHVYKHIEK, EIKKEEKKEER, and DLENDKQQLDEK exhibited strong ActRIIB-binding affinity (K D : 0.514, 0.813, 1.91 μM). These peptides enhanced cell proliferation, inhibited myostatin signaling by reducing Smad2/3 phosphorylation, and upregulated MyoD expression. Molecular dynamics simulations (100 ns) indicated that DLENDKQQLDEK-ActRIIB and EIKKEEKKEER-ActRIIB complexes maintained a stable average number of 6 and 10 hydrogen bonds, respectively. Chicken breast-derived peptides thus represent promising functional food ingredients for combating muscle-wasting disorders.

Rational discovery of testosterone-enhancing peptide (AGNYGLPT) from sea cucumber: targeting T-type calcium channels through docking, molecular dynamics simulations, and cellular validation.

Calcium ions (Ca 2+ ) are a crucial signaling factor in testosterone synthesis. This study employed computer-guided peptide screening and mechanism exploration to elucidate how sea cucumber peptide (SCP) promotes testosterone synthesis via Cacna1g (T-type calcium channel). Ca 2+ treatment alone significantly upregulated the expression of Cacna1g, protein kinase C (PKC), protein kinase A (PKA), and testosterone synthase genes (StAR, Hsd17b3, Cyp17a1); these effects were further enhanced by SCP co-treatment. Molecular docking combined with correlation analysis pinpointed the peptide’s isoelectric point as a critical determinant governing its binding affinity to Cacna1g. SCP was subsequently fractionated into three components via ion-exchange chromatography, among which the fraction 1 (F1) elevated intracellular Ca 2+ levels and enhanced testosterone synthesis. Moreover, molecular docking results for the F1 sequences also showed a positive linear relationship between binding affinity and isoelectric point. Peptide AGNYGLPT in F1 stabilizes Cacna1g (with the strongest binding ability) via hydrogen bonding, and participates in ion channel formation (Charge = -2, Radius = 2.8 Å). In vitro, AGNYGLPT increased intracellular Ca 2+ levels and enhanced testosterone synthesis, but this effect was abolished by inhibition of T-type calcium channels. This study provided mechanistic insights into peptide-channel interactions and offered new ideas for computer-assisted screening of active peptides.

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Decipher the molecular network of PFOA in inflammatory bowel disease through integrating machine learning, molecular docking strategies, and validation in vitro.

Objective Perfluorooctanoic Acid (PFOA), widely recognized as an enduring environmental pollutant, is associated with immune system disruption and potential cancer-causing effects. Epidemiological findings show that serum power is significantly associated with inflammatory bowel disease (IBD). However, the specific mechanisms driving these effects remain poorly understood. Methods Possible targets associated with PFOA were obtained from various sources. Transcriptomic data from IBD patients were sourced through GEO. To delve into the binding interactions of PFOA with its target proteins, we employed machine learning techniques, network toxicology approaches, as well as molecular docking strategies. Results This study reveals that PFOA has the potential to promote the development of IBD by affecting certain genes and signaling pathways. ABCB1, HSD17B11, PDK2, LCN2, SETD7, and MMP1 were identified as six pivotal genes via machine learning, and molecular docking verified that PFOA has a strong binding affinity with important goals. In validation, we found that the expression of LCN2 is elevated by PFOA in NCM460 and HT29 cells, with higher concentrations leading to a corresponding increase in expression levels. Conclusion This study demonstrates that PFOA has the potential to promote development of IBD. The results may offer important insights into the advancement of understanding IBD mechanisms associated with PFOA.

Deciphering the pharmacological mechanisms of salidroside in cervical cancer by combining network pharmacology, molecular docking, and in vitro studies.

Cervical cancer is one of the major and serious risks to women. Salidroside, a natural compound, shows promise in treating cervical cancer. However, its specific molecular mechanisms remain unclear and require further investigation. This study aimed to elucidate the pharmacological activity of salidroside and its underlying molecular mechanisms in cervical cancer, employing network pharmacology, molecular docking, and experimental approaches. Genes associated with cervical cancer were gathered from The Cancer Genome Atlas Program (TCGA), Gene Expression Omnibus (GEO) databases, and network pharmacology. Furthermore, we integrated the drug targets with the disease targets pertinent to cervical cancer, subsequently conducting analyses utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) to explain the pharmacological pathways through which salidroside operates in the milieu of cervical cancer. Survival analysis was performed to screen the core therapeutic targets of salidroside. Salidroside constituents and hub genes binding affinity were assessed by molecular docking studies. In vitro experiments, including Cell Counting Kit-8 (CCK-8) assays, flow cytometry, and western blotting, were performed to further validate the computational findings. Study findings revealed that salidroside inhibited the cervical cancer cell progression, reduced viability, and induced apoptosis.Ten target genes related to salidroside’s anti-cancer effects have been identified. Survival analysis revealed that MMP1 and MMP3 exhibited the highest binding capability among all the target genes. Molecular docking indicated that the salidroside’s active entities showed a strong binding tendency with the MMP1 and MMP3 genes. Western blot analysis revealed that it significantly reduced the expression of MMP-1 and MMP-3. In Vitro studies suggested that suppressing MMP1 and MMP3 genes might be responsible for salidroside’s anticancer effects.

Exploring the Mechanism of 2,4-Dichlorophenoxyacetic Acid in Causing Neurodegenerative Diseases Based on Network Toxicology and Molecular Docking.

This study employed an integrated network toxicology and molecular docking approach to explore the molecular mechanisms by which the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D) may contribute to neurodegenerative diseases (NDDs). We identified 89 common targets through the intersection of potential 2,4-D-related targets and NDD-associated genes. Among these, 12 core targets-including NFKB1, PPARG, SERPINE1, NOS3, and NFE2L2-were highlighted via protein-protein interaction network analysis. Functional enrichment revealed that these targets are involved in key pathways such as inflammatory response, oxidative stress, metabolic dysregulation, and synaptic dysfunction. Molecular docking further confirmed strong binding affinities between 2,4-D and all core targets (binding energy ≤ -5.1 kcal·mol -1 ). These findings systematically reveal, for the first time, a multi-target and multi-pathway mechanism through which 2,4-D may induce neuronal injury, providing a theoretical basis for assessing environmental risk in neurodegeneration.

Molecular docking and MD simulations predicted quercetin as a potent human interleukin-1 beta (hIL1β) inhibitor for improved endodontic disease management.

IL1β, a pro-inflammatory cytokine, is a key mediator in the inflammatory processes linked to endodontic disorders. Studies have shown that IL1β production is elevated in symptomatic periapical lesions, highlighting its involvement in inflammation and lesion development. Elevated levels of IL1β correlate with larger lesion sizes and increased inflammation in periapical tissues. Given its role in inflammation, IL1β represents a potential therapeutic target for endodontic diseases, including the use of IL1β inhibitors. The present study used molecular docking and MD simulations to identify small molecule inhibitors of hIL1β. A small library of 329 plant-derived natural compounds was screened against hIL1β, the top five hits were selected based on their binding affinity and score and docked with hIL1β. Molecular docking results showed that Quercetin has the highest binding affinity (-10.3 kcal/mol) and exibits favorable interactions with hIL1β compared to the other four hits. Based on these observations, Quercetin was further subjected to MD simulations with hIL1β. MD trajectories were used to determine the interaction and stability of the hIL1β-Quercetin complex using various parameters, such as RMSD, RMSF, Rg, SASA, and hydrogen bond count of the apo hIL1β and Quercetin-hIL1β complex. Consistent RMSD, RMSF, Rg, and SASA values indicated the stability of the complex. Furthermore, hydrogen bond count emphasized the Quercetin’s role as a non-disruptive binder. Moreover, secondary structure analysis, PCA, and calculations of Gibbs free energy revealed minimal structural changes and highlighted the stable conformational state of hIL1β upon Quercetin binding, suggesting a stabilizing effect of Quercetin. These observations suggest Quercetin’s potential in the development of new treatments for endodontic diseases, which may lead to improved clinical outcomes and reduced recurrence rates.

Identification of diagnostic genes and drug prediction of allergic asthma by integrated bioinformatics analysis, machine learning, and molecular docking.

Background Allergic asthma is a heterogeneous inflammatory airway disease with limited biomarkers and unclear immune mechanisms, complicating diagnosis and treatment. Objectives This study aimed to identify key genes in allergic asthma patients, assess their role in immune regulation, and screen for potential therapeutic agents. Methods RNA was extracted from blood samples of 36 allergic asthma patients and 19 healthy controls for high-throughput sequencing. Differentially Expressed Genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four key genes were identified by intersecting DEGs with key modular genes from Weighted Gene Co-expression Network Analysis (WGCNA), top-ranked genes from Random Forest (RF), and significant genes from Extreme Gradient Boosting (XGBoost). Core genes were determined via Protein-protein Interaction (PPI) network analysis and further evaluated using immune infiltration and molecular docking. Results A total of 333 DEGs were identified, mainly involved in oxygen transport and pathology. TMIGD2, OBSCN, FCGBP, and FBLN2 were screened as key genes, with OBSCN and FBLN2 designated as core genes. Immune infiltration analysis revealed associations between core genes and various immune cells, and molecular docking showed strong binding affinities of midecamycin and paricalcitol to core genes. Conclusions This study highlights the roles of OBSCN and FBLN2 in immune regulation in allergic asthma, suggesting midecamycin and paricalcitol as potential therapeutic agents.

Non-standard proteins in the lens of AlphaFold3 - a case study of amyloids

While three-dimensional structures of globular and transmembrane forms are available for many amyloid proteins, structures of their amyloid forms are scarce in the PDB. Amyloids pose major challenges for both experimental structure determination and computational modelling. We evaluated the amyloid-modelling performance of the current top modelling software, AlphaFold 3 (AF3), using three datasets. Dataset 1 contains 153 proteins and peptides that are known to form fibrils, but their 3D structures have not been experimentally determined. Dataset 2 contains 56 non-aggregating/non-amyloid peptides. Dataset 3 contains seven proteins for which the three-dimensional fibrillar structure is known. Fibrillar structures were predicted for 34% of dataset 1, but unfortunately also for 54% of dataset 2. Fibrillar structures were successfully predicted for five out of seven proteins from dataset 3. Comparing AF3 with different methods, it outperformed Boltz, and predicted the structures of CsgA and -synuclein more correctly than RibbonFold, whereas the latter predicted A{beta}-42 better. The performance of AF3 in prediction of amyloid structures for our datasets seems hindered by low abundance of amyloid structures in the PDB and high prevalence of structure data for their non-fibrillar forms. AF3 tends to assign a higher quality score to globular oligomeric models than to fibrillar ones. A correct amyloid structure prediction is more likely to be obtained for shorter fragments. The amyloid modelling quality of AF3 seems underwhelming, but it can still provide hypotheses about amyloid structures in some cases. Our work also suggests the steps needed to achieve a better performance in the near future. Statement for a broader audienceAmyloid proteins can form stable, insoluble fibrils that are often related to a neurodegenerative disease. Knowledge of the three-dimensional structure of these fibrils is important, e.g. for a drug design. We evaluate the performance of AlphaFold 3 on the prediction of amyloid structures and observe that it struggles with these cases. The problems seem to arise mainly from the nature of the AlphaFold 3 training dataset and polymorphic nature of many amyloids. Although the results are underwhelming, AlphaFold 3 can sometimes provide valuable insights into amyloid protein structures, something that only a few years ago still seemed a very hard to reach goal.

AI-powered literature mining reveals the therapeutic significance of GLP-1 receptor: Simulation of natural agonist candidates based on molecular dynamics.

Glucagon-like peptide-1 (GLP-1), a pivotal incretin hormone modulating glycemic homeostasis, has emerged as a clinically validated target for the treatment of type 2 diabetes and obesity. In this study, we present a comprehensive AI-integrated drug discovery pipeline that leverages BioBERT-based biomedical text mining to delineate the therapeutic landscape of GLP-1 receptor agonism systematically. Subsequent high-throughput virtual screening (HTVS) of a curated natural product library identified structurally diverse candidate ligands. A machine-learning-guided ADMET profiling algorithm was employed to prioritize compounds with optimal pharmacokinetic and safety characteristics. Top-ranked molecules were subjected to extensive molecular dynamics (MD) simulations using the GROMACS platform, enabling quantitative evaluation of structural stability, dynamic behavior, and receptor-ligand interaction persistence. Molecular docking analyses demonstrated robust binding affinities (ΔG: -11.3 to -8.7 kcal/mol), while MM-PBSA free energy estimations (ΔG 0.67), stable interaction trajectories, and enthalpically favorable profiles. This integrative, AI-augmented computational framework demonstrates substantial potential to accelerate the rational design and preclinical advancement of GLP-1-targeted therapeutics.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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