Computational Biology and Chemistry最新文献

{"title":"Identification of novel ligands against GSK-3β: Molecular Docking, ADMET filtering, MD Simulations, MM-GBSA studies, and DFT analysis","authors":"Nachiket Joshi,&nbsp;RajaSekhar Reddy Alavala","doi":"10.1016/j.compbiolchem.2025.108478","DOIUrl":"10.1016/j.compbiolchem.2025.108478","url":null,"abstract":"<div><div>Glycogen synthase kinase-3β (GSK-3β) is a key enzyme involved in tau hyperphosphorylation, a key pathological mechanism leading to tau aggregation and thus, Alzheimer’s disease. It also has a significant role in neuroinflammation, induction of BACE1, and amyloid-β aggregation. Such crucial pathological events are involved intricately with the Alzheimer’s disease pathophysiology which makes this enzyme an attractive drug target. Designing a competitive ATP-site inhibitor of GSK-3β is the approach adopted here in an attempt to target tau pathology. Tetrazole is selected as a scaffold taking into account the previously reported inhibitors bearing nitrogen heterocycles which are at the heart of drug design strategies. A library of disubstituted tetrazole analogs was designed and molecular docking was performed. The top 30 molecules were then passed through several ADMET filters with a special preference given to BBB permeability. The hit molecules obtained after this were analyzed for their amino acid interactions. The docked complexes were used to perform MD simulations for 100<!--> <!-->ns and later MM-GBSA studies were performed with staurosporine and co-crystallized ligand as reference molecules. Furthermore, various chemical reactivity parameters and the HOMO-LUMO energy gap were analyzed with DFT studies using B3LYP functional. The best molecule obtained was <strong>TD30</strong>, revealing a docking score of -167.036, an average RMSD of 2.15<!--> <!-->Å for 100<!--> <!-->ns simulation time, average ΔG_Binding energy of -86.55 ± 4.45<!--> <!-->kcal/mol, and an HOMO-LUMO energy gap of 4.044<!--> <!-->eV; all corroborating the potential of <strong>TD30</strong> for future research and enzyme inhibition assays.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108478"},"PeriodicalIF":2.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881510","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}

{"title":"SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data","authors":"Farong Liu ,&nbsp;Sheng Ren ,&nbsp;Jie Li ,&nbsp;Haoyang Lv ,&nbsp;Fenghui Jiang ,&nbsp;Bin Yu","doi":"10.1016/j.compbiolchem.2025.108482","DOIUrl":"10.1016/j.compbiolchem.2025.108482","url":null,"abstract":"<div><div>In recent years, spatial transcriptomics (ST) has emerged as an innovative technology that enables the simultaneous acquisition of gene expression information and its spatial distribution at the single-cell or regional level, providing deeper insights into cellular interactions and tissue organization, this technology provides a more holistic view of tissue organization and intercellular dynamics. However, existing methods still face certain limitations in data representation capabilities, making it challenging to fully capture complex spatial dependencies and global features. To address this, this paper proposes an innovative spatial multi-scale graph convolutional network (SGTB) based on large language models, integrating graph convolutional networks (GCN), Transformer, and BERT language models to optimize the representation of spatial transcriptomics data. The Graph Convolutional Network (GCN) employs a multi-layer architecture to extract features from gene expression matrices. Through iterative aggregation of neighborhood information, it captures spatial dependencies among cells and gene co-expression patterns, thereby constructing hierarchical cell embeddings. Subsequently, the model integrates an attention mechanism to assign weights to critical features and leverages Transformer layers to model global relationships, refining the ability of learned representations to reflect variations in spatial patterns. Finally, the model incorporates the BERT language model, mapping cell embeddings into textual inputs to exploit its deep semantic representation capabilities for high-dimensional feature extraction. These features are then fused with the embeddings generated by the Transformer, further optimizing feature learning for spatial transcriptomics data. This approach holds significant application value in improving the accuracy of tasks such as cell type classification and gene regulatory network construction, providing a novel computational framework for deep mining of spatial multi-scale biological data.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108482"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881514","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}

{"title":"ProAttUnet: Advancing protein secondary structure prediction with deep learning via U-Net dual-pathway feature fusion and ESM2 pretrained protein language model","authors":"Long Cheng ,&nbsp;Weizhong Lu ,&nbsp;Yiyi Xia ,&nbsp;Yiming Lu ,&nbsp;Jiyun Shen ,&nbsp;Zhiqiang Hui ,&nbsp;Yixin Xu ,&nbsp;Hongjie Wu ,&nbsp;Jing Chen ,&nbsp;Qiming Fu ,&nbsp;You Lu","doi":"10.1016/j.compbiolchem.2025.108429","DOIUrl":"10.1016/j.compbiolchem.2025.108429","url":null,"abstract":"<div><div>Protein secondary structure prediction remains a pivotal concern within the domain of bioinformatics. In this innovative research, we introduce a novel methodology to further enhance a protein prediction model grounded in single sequences. Our key contribution lies in integrating the state-of-the-art (SOTA) model ESM2, which hails from the field of universal protein language models. By leveraging ESM2, we are able to acquire residual embeddings and contact maps for the protein sequences under study. Regarding the model architecture, we employ a unique dual-way U-Net framework for effective feature fusion. This framework is complemented by the integration of a cross-attention mechanism, enabling the model to capture more comprehensive context information. Furthermore, In accordance with the distinctive characteristics of protein sequences, we incorporate a so-called GCU_SE module into both the encoder and the decoder components of the model. These innovative enhancements enable the ProAttUnet model to outperform the benchmark model SPOT-1D-Single by 1.6%, 3.5%, 1.0%, 4.6%, and 7.2% for ss3, and by 5.5%, 7.8%, 4.1%, 8.1%, and 10.1% for ss8 across five test sets (SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ and TEST2018, respectively). This significant improvement vividly demonstrates the effectiveness and novelty of our proposed model.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108429"},"PeriodicalIF":2.6,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877338","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}

{"title":"Convolutional Neural Network approach to classify mitochondrial morphologies","authors":"Soumaya Zaghbani ,&nbsp;Lukas Faber ,&nbsp;Ana J. Garcia-Saez","doi":"10.1016/j.compbiolchem.2025.108477","DOIUrl":"10.1016/j.compbiolchem.2025.108477","url":null,"abstract":"<div><div>The morphology of the mitochondrial network is a major indicator of cellular health and function, with changes often linked to various physiological and pathological conditions. As a result, efficient methods to quickly assess mitochondrial shape in cellular populations from microscopy images in a quantitative manner are of high interest for the health and life sciences. Here, we present MitoClass, a deep learning-based software designed for automated mitochondrial classification. MitoClass employs a classification algorithm that categorizes mitochondrial network shapes into three classes: fragmented, intermediate, and elongated. By leveraging super-resolution images, we curated a comprehensive dataset for training, including both high- and low-resolution representations of mitochondrial networks. Using a Convolutional Neural Network (CNN) architecture, our model effectively distinguishes between different mitochondrial morphologies. Through rigorous training and validation, MitoClass provides a fast, accurate, and user-friendly solution for researchers and clinicians to assess the organization of the mitochondrial network as a proxy for studying organelle health and dynamics.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108477"},"PeriodicalIF":2.6,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864175","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}

{"title":"The drug discovery candidate for targeting PARP1 with Onosma. Dichroantha compounds in triple-negative breast cancer: A virtual screening and molecular dynamic simulation","authors":"Mohamed J. Saadh ,&nbsp;Hanan Hassan Ahmed ,&nbsp;Radhwan Abdul Kareem ,&nbsp;Muktesh Chandra ,&nbsp;Lokesh Verma ,&nbsp;G.V. Siva Prasad ,&nbsp;Anurag Mishra ,&nbsp;Waam Mohammed Taher ,&nbsp;Mariem Alwan ,&nbsp;Mahmood Jasem Jawad ,&nbsp;Atheer Khdyair Hamad","doi":"10.1016/j.compbiolchem.2025.108471","DOIUrl":"10.1016/j.compbiolchem.2025.108471","url":null,"abstract":"<div><div>Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the overexpression of poly-ADP ribose polymerase 1 (PARP1), a key enzyme in DNA repair. Targeting PARP1 with inhibitors presents a promising therapeutic strategy, particularly given the limited treatment options for TNBC. This study employed in silico methodologies to evaluate the pharmacokinetic and inhibitory potential of FDA-approved drugs and compounds derived from <em>Onosma. dichroantha</em> root extracts against PARP1. Virtual screening and molecular docking identified Midazolam, Olaparib, Beta-sitosterol, and 1-Hexyl-4-nitrobenzene as top candidates, exhibiting strong binding affinities of −10.6 kcal/mol, −9.9 kcal/mol, −6.83 kcal/mol, and −5.53 kcal/mol respectively. Molecular dynamics simulations (MDS) over 100 nanoseconds revealed that Beta-sitosterol formed the most stable complex with PARP1, demonstrating minimal structural deviations and robust hydrogen bonding. The Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) analysis further confirmed Beta-Sitosterol and Olaparib superior binding free energy (ΔG_bind= −175.43 kcal/mol and –180.8 kcal/mol respectively), highlighting its potential as a potent PARP1 inhibitor. ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling indicated that Beta-Sitosterol adheres to Lipinski's Rule of Five, with high intestinal absorption (95.88 %) and blood-brain barrier permeability (0.824), despite low water solubility. Protein-protein interaction analysis identified key PARP1-associated proteins, including CASP3, CASP7, and XRCC1, suggesting broader therapeutic implications. These findings underscore the potential of Beta-Sitosterol as a novel PARP1 inhibitor for TNBC treatment, combining computational validation with favorable pharmacokinetic properties. The study also highlights the utility of drug repurposing and plant-derived compounds in developing targeted therapies for TNBC, paving the way for further preclinical and clinical investigations.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108471"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848088","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}

{"title":"PathwayMind: An innovative tool and database for pathway perturbation analysis, uncovering critical pathways for drugs and target protein sets","authors":"Om Prakash Sharma","doi":"10.1016/j.compbiolchem.2025.108466","DOIUrl":"10.1016/j.compbiolchem.2025.108466","url":null,"abstract":"<div><div>Pathway enrichment analysis is a valuable tool for researchers aiming to understand the mechanisms underlying any specific drug or disease associated gene lists derived from biological assays or large-scale genome (omics) experiments. By employing this method, researchers can pinpoint biological pathways that exhibit a higher level of enrichment for a given set of genes than would be anticipated by random chance. These biological pathways play a crucial role in understanding the disease pathophysiology, therapeutic strategy, polypharmacological effects, synergistic mechanisms, and target engagement. However many of the available tools do not fulfill this requirement as most of the available tools consider a flat hierarchy of protein involvement in the pathway and do not consider topological information or importance of molecules in the pathway. Here we propose a novel method to enrich the molecular pathways and prioritize them based on their importance and crucial role in the biological function using the graph and evidence-based approach and customized datasets called PathwayMind. It includes 2648 pathways, 4539 biological events, 2465847 protein-protein interactions and 124717 gene-to-pathway relationships and the role of 3510 unique initial genes in 11,992 molecular pathways. The current manuscript comprises three major steps: The first step is about the data extraction and datasets creation for pathway enrichment, and the second steps comprises pathway perturbation analysis to identify most perturbed biological pathways and the third steps includes validation of this approach along with standalone tools and visualization algorithms which disclose the molecular involvement and improve the interpretability of the results. The end-to-end pathway analysis can be performed in a few minutes to provide complete insights of your target or drug of interest. The current PathwayMind tool and its datasets could be very useful for the molecular scientists and system biologists who are interested to understand the therapeutic effects of their drugs or understanding the involvement of biological pathways for specific gene sets which does not require any prior bioinformatics training.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108466"},"PeriodicalIF":2.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838978","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}

{"title":"Prediction of newly synthesized heparin mimic’s effects as heparanase inhibitor in cancer treatments via variational quantum neural networks","authors":"Samet Kocabay ,&nbsp;Erdi Acar ,&nbsp;Samet Memiş ,&nbsp;Irmak İçen Taşkın ,&nbsp;Meryem Rüveyda Sever ,&nbsp;Ramazan Şener","doi":"10.1016/j.compbiolchem.2025.108476","DOIUrl":"10.1016/j.compbiolchem.2025.108476","url":null,"abstract":"<div><div>Cancer remains a leading global cause of death, primarily driven by the uncontrolled proliferation of abnormal cells. Malignant tumors, such as carcinomas, originate from unchecked epithelial cell growth and produce growth factors like FGF and VEGF, which promote angiogenesis and tumor progression through heparanase-mediated degradation of heparan-sulfate proteoglycans. Chitosan and its derivatives have shown promise in inhibiting tumor growth and metastasis. This study aims to investigate newly synthesized sulfated chitosan oligomers as heparin mimics to inhibit heparanase, evaluating their cytotoxic effects on SH-SY5Y, HCT116, A549, and MDA-MB-231 cancer cell lines. Moreover, it seeks to leverage a variational quantum neural network (VQNN) to predict and validate cytotoxicity outcomes, integrating quantum computing methods into evaluating novel anticancer compounds. The VQNN algorithm was applied to analyze the anticancer effects of sulfated chitosan oligomers. Cytotoxicity data from wet lab experiments validated the model’s predictive performance. The VQNN model demonstrated strong predictive capabilities in evaluating anticancer compounds. Specifically, it achieved a mean absolute error (MAE) of 6.5844, indicating a similar trend to the experimental results. Additionally, the model obtained an R² value of 0.6020, reflecting a moderate level of correlation between predicted and observed outcomes. The results underscore the potential of integrating quantum-based machine learning models into cancer research. The VQNN effectively predicted experimental outcomes, showcasing its utility in assessing novel anticancer compounds. This approach could speed up drug discovery by streamlining the identification and optimization of therapeutic candidates. Furthermore, the findings support the ongoing development of quantum computing techniques for tackling complex biological challenges, contributing to innovative cancer treatment strategies that target tumor growth and angiogenesis.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108476"},"PeriodicalIF":2.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859616","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}

{"title":"im7G-DCT: A two-branch strategy model based on improved DenseNet and transformer for m7G site prediction","authors":"Rufeng Lei ,&nbsp;Jian Jia ,&nbsp;Lulu Qin","doi":"10.1016/j.compbiolchem.2025.108473","DOIUrl":"10.1016/j.compbiolchem.2025.108473","url":null,"abstract":"<div><div>N-7 methylguanosine (m7G) is an important RNA modification that plays a key role in regulating gene expression and cellular physiological functions. Medical research has shown that m7G is closely associated with the development of a variety of diseases, including cancer, neurodegenerative diseases and viral infections. Therefore, accurate identification of m7G sites in mRNA is important for clinical applications and development of therapeutic strategies. With the rapid development of computational methods, deep learning prediction models are widely used in the field of m7G site. We developed a model called im7G-DCT based on the improved Densely Connected Convolutional Network and Transformer, which employs a two-branching strategy to extract local and global features from the original feature code in parallel. We found that this innovative strategy can mine the potential feature information of m7G locus sequences in a deeper way, which enhances the richness of features and improves the accuracy of prediction. As an innovative deep learning method, the im7G-DCT model shows important research value in the field of bioinformatics and has broad application prospects. In the results of independent test experiments, the im7G-DCT model achieved 81.68 %, 90.52 %, 86.10 %, and 72.48 % in sensitivity, specificity, accuracy, and Matthews' correlation coefficient, respectively. The im7G-DCT model performs well in all evaluation metrics and it significantly outperforms other existing prediction models.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108473"},"PeriodicalIF":2.6,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834828","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}

{"title":"Kolmogorov-Arnold networks for predicting drug-gene associations of HDAC1 inhibitors in periodontitis","authors":"Pradeep Kumar Yadalam ,&nbsp;Swarnambiga Ayyachamy ,&nbsp;Francisco T. Barbosa ,&nbsp;Prabhu Manickam Natarajan","doi":"10.1016/j.compbiolchem.2025.108451","DOIUrl":"10.1016/j.compbiolchem.2025.108451","url":null,"abstract":"<div><div>Periodontal disease, or periodontitis, is a chronic inflammatory condition affecting the tissues supporting teeth, with epigenetic mechanisms such as DNA methylation, histone modifications, and RNA molecules playing a crucial role in its progression. Histone deacetylase (HDAC) inhibitors have shown potential in treating inflammatory diseases by modulating gene expression to suppress inflammation and promote tissue regeneration. Machine learning models, particularly Kolmogorov-Arnold Networks (KANs), provide an advanced solution for predicting drug-gene associations, offering superior accuracy, efficiency, interpretability, and scalability compared to traditional Multi-Layer Perceptrons (MLPs). This study explores the application of KANs in predicting drug-gene associations of HDAC1 inhibitors for periodontitis. A dataset comprising 533 compounds and genes was analyzed using Cytoscape for network-based topological and functional insights, followed by predictive modeling using KANs. The resulting network contained 326 nodes and 3734 edges, with an average of 23.411 neighbors, a diameter 5, and a characteristic path length of 2.383. The predictive model achieved an impressive accuracy of 96.49 %, as indicated by the F1 score, reflecting balanced performance in classification. The study highlights the potential of KANs in drug discovery for periodontal disease by efficiently predicting drug-gene associations, enabling better understanding and research for experimental validation and clinical applications.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108451"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830354","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}

{"title":"Structure-based profiling of putative therapeutics against monkeypox virus VP39 using pharmacophore modelling and molecular dynamics simulation studies","authors":"Gideon Ampoma Gyebi,&nbsp;Saheed Sabiu","doi":"10.1016/j.compbiolchem.2025.108458","DOIUrl":"10.1016/j.compbiolchem.2025.108458","url":null,"abstract":"<div><div>The growing global health threat of the monkeypox virus (MPXV) underscores the critical need for effective antiviral agents, since there are currently no therapeutics. The MPXV VP39, a methyltransferase, is essential for viral replication, hence a potential target for anti-MPXV drug candidates. Herein, a structure-based pharmacophore modelling and molecular docking approach was employed to screen natural compounds (NCs: 581,426) from the COCONUT database for potential inhibitors of MPXV VP39. After ranking of the docking scores, an ensemble-based docking of the top-ranked 20 NCs against multiple conformations obtained from ttcluster analysis of the molecular dynamics simulation trajectory of unbound MPXV VP39 further identified five leads with favourable interaction profiles, drug-likeness, ADMET properties, and synthetic features when compared to the reference standard (sinefungin). Further analysis of the thermodynamic stability of the resulting complexes of the leads over a 100-ns MD simulation period revealed varying degrees of thermodynamic stability while maintaining the structural integrity of MPXV VP39. Furthermore, the thermodynamic binding free energy calculation, while corroborating the docking analysis, identified CNP0297833 (-39.07 kcal/mol), CNP0371756 (-25.76 kcal/mol), and CNP0402319 (-19.26 kcal/mol) as the most promising candidates, with better modulatory effect against MPXV VP39 relative to sinefungin (-3.68 kcal/mol). These leads were stabilised with hydrophobic (Phe115, Val139, and Val116) and electrostatic (Glu46 and Asp138) interactions in different conformational clusters. In addition to the observed consistent interaction patterns, favourable binding energies, pharmacokinetics, ADMET, thermodynamic stability, and molecular orbital energies of these leads, the potential for optimisation for enhanced binding features for the active site of MPXV VP39 was elucidated. Further <em>in vitro</em> investigation to validate these findings is suggested to establish the putative leads as therapeutics targeting the replication phase of MPXV.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"118 ","pages":"Article 108458"},"PeriodicalIF":2.6,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838975","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}