Pub Date : 2025-01-30DOI: 10.1016/j.compbiolchem.2025.108362
Fahamidur Rahaman Rafi , Nafeya Rahman Heya , Md Sadman Hafiz , Jamin Rahman Jim , Md Mohsin Kabir , M.F. Mridha
Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.
{"title":"A systematic review of single-cell RNA sequencing applications and innovations","authors":"Fahamidur Rahaman Rafi , Nafeya Rahman Heya , Md Sadman Hafiz , Jamin Rahman Jim , Md Mohsin Kabir , M.F. Mridha","doi":"10.1016/j.compbiolchem.2025.108362","DOIUrl":"10.1016/j.compbiolchem.2025.108362","url":null,"abstract":"<div><div>Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108362"},"PeriodicalIF":2.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143209952","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 : 2025-01-30DOI: 10.1016/j.compbiolchem.2025.108369
Ayman M. Al-Qaaneh , Munthar Kadhim Abosaoda , Lalji Baldaniya , Junainah Abd Hamid , A. Sabarivani , Rajashree Panigrahi , Aman Shankhyan , M.F. Alajmi , Mounir M. Bekhit
The study employs density functional theory (DFT) to examine the drug-loading efficiency of graphyne (GYN) as a vehicle for the Tioguanine (TG) drug. The researchers analyzed the interaction energy, electrical properties of pure GYN, TG molecules, and TG@GYN complex to determine their effectiveness as a carrier. Configuration a, which utilized nitrogen and sulfur atoms in interactions, was deemed the most suitable among the three considered TG sites. Gas-phase interaction between TG drug and GYN resulted in an energy of adsorption about −1.64 eV. The study utilized non-covalent interaction (NCI) analysis to assess the interaction between GYN and TG drug, indicating weak forces of interaction in the TG@GYN complex. The HOMO-LUMO and charge-decomposition analysis described the transfer of charge from TG molecules to pure GYN during formation of TG@GYN. The results suggest that GYN could function as a promising candidate for carrying and delivering TG drug, leading to further research into similar 2D nanomaterials for drug transport applications.
{"title":"Computational investigation of graphyne monolayer as a promising carrier for anticancer drug delivery","authors":"Ayman M. Al-Qaaneh , Munthar Kadhim Abosaoda , Lalji Baldaniya , Junainah Abd Hamid , A. Sabarivani , Rajashree Panigrahi , Aman Shankhyan , M.F. Alajmi , Mounir M. Bekhit","doi":"10.1016/j.compbiolchem.2025.108369","DOIUrl":"10.1016/j.compbiolchem.2025.108369","url":null,"abstract":"<div><div>The study employs density functional theory (DFT) to examine the drug-loading efficiency of graphyne (GYN) as a vehicle for the Tioguanine (TG) drug. The researchers analyzed the interaction energy, electrical properties of pure GYN, TG molecules, and TG@GYN complex to determine their effectiveness as a carrier. Configuration a, which utilized nitrogen and sulfur atoms in interactions, was deemed the most suitable among the three considered TG sites. Gas-phase interaction between TG drug and GYN resulted in an energy of adsorption about −1.64 eV. The study utilized non-covalent interaction (NCI) analysis to assess the interaction between GYN and TG drug, indicating weak forces of interaction in the TG@GYN complex. The HOMO-LUMO and charge-decomposition analysis described the transfer of charge from TG molecules to pure GYN during formation of TG@GYN. The results suggest that GYN could function as a promising candidate for carrying and delivering TG drug, leading to further research into similar 2D nanomaterials for drug transport applications.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108369"},"PeriodicalIF":2.6,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123037","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 : 2025-01-29DOI: 10.1016/j.compbiolchem.2025.108367
Wentao Xia , Jiasai Shu , Chunjiang Sang , Kang Wang , Yan Wang , Tingting Sun , Xiaojun Xu
Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.
{"title":"The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning","authors":"Wentao Xia , Jiasai Shu , Chunjiang Sang , Kang Wang , Yan Wang , Tingting Sun , Xiaojun Xu","doi":"10.1016/j.compbiolchem.2025.108367","DOIUrl":"10.1016/j.compbiolchem.2025.108367","url":null,"abstract":"<div><div>Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108367"},"PeriodicalIF":2.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153256","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 : 2025-01-27DOI: 10.1016/j.compbiolchem.2025.108360
Xinyue Yan, Meng Wang, Lurao Ji, Xiaoqin Li, Bin Gao
Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD patterns requires further investigation. This study identified a novel molecular classification named PCD subtype (C1, C2, and C3) based on the genes associated with 19 PCD patterns, distinguished by clinical, biological functional pathways, mutations, immune characteristics, and drug sensitivity. Validated in 4 independent datasets, diverse cell death pathways were enriched in the C3 subtype, including apoptosis, pyroptosis, and autophagy, it also exhibited a highly infiltrative immunosuppressive microenvironment and demonstrated higher sensitivity to compounds such as Paclitaxel, Bortezomib, and YK-4–279, while C1 subtype was significantly enriched in cuproptosis and metabolism-related pathways, suggesting that it may be more suitable for cuproptosis-inducing agent therapy. Subsequently, utilizing the machine learning algorithms, we constructed a cell death-related index (CDRI) with 22 gene features and constructed prognostic nomograms with high predictive performance by combining CDRI with clinical features. Notably, we found that CDRI effectively predicted the response of HCC patients to therapeutic strategies, where patients with high CDRI were more suitable for sorafenib drug therapy and patients with low CDRI were more ideal for transarterial chemoembolization (TACE). In conclusion, the PCD subtype and CDRI demonstrate significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. These findings offer valuable insights for the development of precise, individualized treatment strategies.
{"title":"Machine learning and molecular subtyping reveal the impact of diverse patterns of cell death on the prognosis and treatment of hepatocellular carcinoma","authors":"Xinyue Yan, Meng Wang, Lurao Ji, Xiaoqin Li, Bin Gao","doi":"10.1016/j.compbiolchem.2025.108360","DOIUrl":"10.1016/j.compbiolchem.2025.108360","url":null,"abstract":"<div><div>Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD patterns requires further investigation. This study identified a novel molecular classification named PCD subtype (C1, C2, and C3) based on the genes associated with 19 PCD patterns, distinguished by clinical, biological functional pathways, mutations, immune characteristics, and drug sensitivity. Validated in 4 independent datasets, diverse cell death pathways were enriched in the C3 subtype, including apoptosis, pyroptosis, and autophagy, it also exhibited a highly infiltrative immunosuppressive microenvironment and demonstrated higher sensitivity to compounds such as Paclitaxel, Bortezomib, and YK-4–279, while C1 subtype was significantly enriched in cuproptosis and metabolism-related pathways, suggesting that it may be more suitable for cuproptosis-inducing agent therapy. Subsequently, utilizing the machine learning algorithms, we constructed a cell death-related index (CDRI) with 22 gene features and constructed prognostic nomograms with high predictive performance by combining CDRI with clinical features. Notably, we found that CDRI effectively predicted the response of HCC patients to therapeutic strategies, where patients with high CDRI were more suitable for sorafenib drug therapy and patients with low CDRI were more ideal for transarterial chemoembolization (TACE). In conclusion, the PCD subtype and CDRI demonstrate significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. These findings offer valuable insights for the development of precise, individualized treatment strategies.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108360"},"PeriodicalIF":2.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061094","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 : 2025-01-25DOI: 10.1016/j.compbiolchem.2025.108363
Lokanathan Jimson , John Patrick Ananth
Lung Cancer is regarded as a common fatal disease affecting humans throughout the entire world. Early diagnosis is vital to save the patient’s life and Computed Tomography (CT) scans are referred to as the major imaging modes but, the manual examination of a CT scan is time-consuming and results in errors. Hence, a novel system of Neuron Attention Visual Taylor Network (NAVT-Net) is developed to detect lung cancer. At first, the CT image is acquired, and then, the input image is filtered based on homomorphic filtering. Then, the lung nodule is segmented using the Dual-Branch-UNet (DB-UNet). Later, the image augmentation is achieved by resizing, flipping, as well as rotation. Next, the shape-based features are extracted and subjected to the last stage of lung cancer detection, which is done by the NAVT-Net system that is established on the basis of Neuron Attention Stage-by-Stage Network (NASNet), Visual Geometry Group-16 (VGG16), and Taylor series. Hence, the experimental results of the developed NAVT-Net system achieved high values of 92.176 % accuracy, 93.997 % of True Positive Rate (TPR), 92.189 % of True Negative Rate (TNR), F1-score of 90.999 %, and precision of 91.998 %, computational time, and memory usage of 37.879 s, and 41.100MB at K-values of 9.
{"title":"NAVT-net neuron attention visual taylor network for lung cancer detection using CT images","authors":"Lokanathan Jimson , John Patrick Ananth","doi":"10.1016/j.compbiolchem.2025.108363","DOIUrl":"10.1016/j.compbiolchem.2025.108363","url":null,"abstract":"<div><div>Lung Cancer is regarded as a common fatal disease affecting humans throughout the entire world. Early diagnosis is vital to save the patient’s life and Computed Tomography (CT) scans are referred to as the major imaging modes but, the manual examination of a CT scan is time-consuming and results in errors. Hence, a novel system of Neuron Attention Visual Taylor Network (NAVT-Net) is developed to detect lung cancer. At first, the CT image is acquired, and then, the input image is filtered based on homomorphic filtering. Then, the lung nodule is segmented using the Dual-Branch-UNet (DB-UNet). Later, the image augmentation is achieved by resizing, flipping, as well as rotation. Next, the shape-based features are extracted and subjected to the last stage of lung cancer detection, which is done by the NAVT-Net system that is established on the basis of Neuron Attention Stage-by-Stage Network (NASNet), Visual Geometry Group-16 (VGG16), and Taylor series. Hence, the experimental results of the developed NAVT-Net system achieved high values of 92.176 % accuracy, 93.997 % of True Positive Rate (TPR), 92.189 % of True Negative Rate (TNR), F1-score of 90.999 %, and precision of 91.998 %, computational time, and memory usage of 37.879 s, and 41.100MB at K-values of 9.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108363"},"PeriodicalIF":2.6,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143209950","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 : 2025-01-23DOI: 10.1016/j.compbiolchem.2025.108355
Ping Lu , Liwei Zheng , Junpeng Lin , Zhongqi Cai , Bin Dai , Kaibiao Lin , Fan Yang
Drug–drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient safety. However, existing DDI prediction methods often overlook the intricate interactions among chemical substructures within drugs, resulting in incomplete characterization of molecular properties. To address this limitation, we propose a novel model named Multi-Layer Soft Mask Dual-View Learning for Drug-Drug Interactions (MSMDL-DDI), which integrates dual-view learning with multi-layer soft mask graph neural networks to comprehensively capture intra- and inter-molecular interactions. Specifically, our model first employs a multi-layer soft-masked graph neural network to extract key substructures from drug molecule graphs. Subsequently, our model implements a novel dual-view learning strategy to capture intra- and inter-molecular interactions resulting in enriched drug pair representations. Finally, the model predicts the likelihood of DDIs by utilizing a decoder to compute the shared attention scores of these enhanced representations. In addition, experimental results on three real-world datasets show that MSMDL-DDI outperforms nine state-of-the-art methods in both transductive and inductive DDI prediction tasks. Notably, the model achieves an accuracy of 0.9647 on the Twosides dataset for the transductive task, marking a 10.2% improvement over the second-best-performing method.
{"title":"MSMDL-DDI: Multi-Layer Soft Mask Dual-View Learning for Drug–Drug Interactions","authors":"Ping Lu , Liwei Zheng , Junpeng Lin , Zhongqi Cai , Bin Dai , Kaibiao Lin , Fan Yang","doi":"10.1016/j.compbiolchem.2025.108355","DOIUrl":"10.1016/j.compbiolchem.2025.108355","url":null,"abstract":"<div><div>Drug–drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient safety. However, existing DDI prediction methods often overlook the intricate interactions among chemical substructures within drugs, resulting in incomplete characterization of molecular properties. To address this limitation, we propose a novel model named <strong>M</strong>ulti-Layer <strong>S</strong>oft <strong>M</strong>ask <strong>D</strong>ual-View <strong>L</strong>earning for <strong>D</strong>rug-<strong>D</strong>rug <strong>I</strong>nteractions (MSMDL-DDI), which integrates dual-view learning with multi-layer soft mask graph neural networks to comprehensively capture intra- and inter-molecular interactions. Specifically, our model first employs a multi-layer soft-masked graph neural network to extract key substructures from drug molecule graphs. Subsequently, our model implements a novel dual-view learning strategy to capture intra- and inter-molecular interactions resulting in enriched drug pair representations. Finally, the model predicts the likelihood of DDIs by utilizing a decoder to compute the shared attention scores of these enhanced representations. In addition, experimental results on three real-world datasets show that MSMDL-DDI outperforms nine state-of-the-art methods in both transductive and inductive DDI prediction tasks. Notably, the model achieves an accuracy of 0.9647 on the Twosides dataset for the transductive task, marking a 10.2% improvement over the second-best-performing method.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108355"},"PeriodicalIF":2.6,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152670","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}
Plastics play an essential role in modern fisheries and their degradation releases micro- and nano-sized plastic particles which further causes ecological and human health hazards through various environmental contamination pathways and toxicity mechanisms, which can cause respiratory problems, cancer, reproductive toxicity, endocrine disruption and neurological effects in humans. This study utilized various bioinformatics tools through multi-step computational analyses to investigate the interactions between prevalent fisheries microplastics and the key protein receptor acetylcholinesterase (AChE), which is associated with neurotoxicity, as it can interfere with nerve impulses and muscle control. Our results indicate that the binding of seven polymers within AChE's active site, with dodecane and polypropylene exhibited highest affinity with hydrogen bonding were observed through Molecular docking of different program (PyRx) and servers (CB-Dock, eDock) then the stability of AChE-dodecane and AChE-polypropylene complexes were observed through MD simulations for 100 ns. Further analysis of dodecane was done by using pharmacophore modelling and virtual screening. The pharmacophore model of dodecane is based on six hydrophobic rings. Using this model, we screened among thousands of substrates form (CMNPD, COCONUT, NPASS, NANPDB, and ZINC) database and identified fifty highly similar candidates that align with dodecane's structure and interaction with acetylcholinesterase (AChE). The compound triacontafluoropentadec-1-ene exhibited highest binding affinity (score: −9.6) which was further confirmed through molecular dynamics for 100 ns. The key finding for this study is triacontafluoropentadec-1-ene as a promising alternative to dodecane, and the study highlights that the integrated in silico framework presents a valuable computational model for guiding future guidelines on environmental safety through prioritizing constituents and accelerated discovery of alternatives. These findings will help us identify the most hazardous plastics through ranking and characterizing the substance for sustainably "greening" fisheries worldwide. The study forecasts the groundwork of these compounds, which may be able to reduce the environmental toxicity of microplastics in future.
{"title":"In silico analysis of novel Triacontafluoropentadec-1-ene as a sustainable replacement for dodecane in fisheries microplastics: Molecular docking, dynamics simulation and pharmacophore studies of acetylcholinesterase activity","authors":"Rahul Thakur , Vibhor Joshi , Ganesh Chandra Sahoo , Rajnarayan R. Tiwari , Sindhuprava Rana","doi":"10.1016/j.compbiolchem.2025.108358","DOIUrl":"10.1016/j.compbiolchem.2025.108358","url":null,"abstract":"<div><div>Plastics play an essential role in modern fisheries and their degradation releases micro- and nano-sized plastic particles which further causes ecological and human health hazards through various environmental contamination pathways and toxicity mechanisms, which can cause respiratory problems, cancer, reproductive toxicity, endocrine disruption and neurological effects in humans. This study utilized various bioinformatics tools through multi-step computational analyses to investigate the interactions between prevalent fisheries microplastics and the key protein receptor acetylcholinesterase (AChE), which is associated with neurotoxicity, as it can interfere with nerve impulses and muscle control. Our results indicate that the binding of seven polymers within AChE's active site, with dodecane and polypropylene exhibited highest affinity with hydrogen bonding were observed through Molecular docking of different program (PyRx) and servers (CB-Dock, eDock) then the stability of AChE-dodecane and AChE-polypropylene complexes were observed through MD simulations for 100 ns. Further analysis of dodecane was done by using pharmacophore modelling and virtual screening. The pharmacophore model of dodecane is based on six hydrophobic rings. Using this model, we screened among thousands of substrates form (CMNPD, COCONUT, NPASS, NANPDB, and ZINC) database and identified fifty highly similar candidates that align with dodecane's structure and interaction with acetylcholinesterase (AChE). The compound triacontafluoropentadec-1-ene exhibited highest binding affinity (score: −9.6) which was further confirmed through molecular dynamics for 100 ns. The key finding for this study is triacontafluoropentadec-1-ene as a promising alternative to dodecane, and the study highlights that the integrated in silico framework presents a valuable computational model for guiding future guidelines on environmental safety through prioritizing constituents and accelerated discovery of alternatives. These findings will help us identify the most hazardous plastics through ranking and characterizing the substance for sustainably \"greening\" fisheries worldwide. The study forecasts the groundwork of these compounds, which may be able to reduce the environmental toxicity of microplastics in future.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108358"},"PeriodicalIF":2.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043499","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 : 2025-01-18DOI: 10.1016/j.compbiolchem.2025.108357
Dilshod A. Mansurov , Alisher Kh. Khaitbaev , Khamid Kh. Khaitbaev , Khamza S. Toshov , Enrico Benassi
Menthol is a naturally occurring cyclic terpene alcohol and is the major component of peppermint and corn mint essential oils extracted from Mentha piperita L. and Mentha arvensis L.. Menthol and its derivatives are widely used in pharmaceutical, cosmetic and food industries. Among its eight isomers, (-)-menthol is the most effective one in terms of refreshing effect. While the invigorating property of (-)-menthol is generally known, this claim is based on a substantial amount of literature and experience. (-)-Menthol has consistently been reported to possess better cooling and refreshing qualities in comparison to its isomers, making it the preferred choice in a broad range of applications such as personal care products, pharmaceuticals and food additives. Additionally, the (-)-menthol molecular structure allows it to have a tighter fitting with the thermoreceptors in the skin and mucous membranes, and thus to provide a more intense cooling feeling. Thus, although others have similar properties to a degree, (-)-menthol is the best compared to all in its refreshing capacity. This study focuses on menthol and some of its esters, viz. menthyl acetate, propionate, butyrate, valerate and hexanoate, with the purpose of establish a connection between structural, electrostatic and electronic characteristics and biological effects. The mostly favoured interactions of the esters with biotargets were investigated at a molecular level, offering a plausible foundation for their bioactivity elucidation. This study is conducted at a quantum mechanical and molecular docking level. The results may be of possible usefulness in areas of applications, such as pharmacological research and drug.
{"title":"Relationship between structural properties and biological activity of (-)-menthol and some menthyl esters","authors":"Dilshod A. Mansurov , Alisher Kh. Khaitbaev , Khamid Kh. Khaitbaev , Khamza S. Toshov , Enrico Benassi","doi":"10.1016/j.compbiolchem.2025.108357","DOIUrl":"10.1016/j.compbiolchem.2025.108357","url":null,"abstract":"<div><div>Menthol is a naturally occurring cyclic terpene alcohol and is the major component of peppermint and corn mint essential oils extracted from <em>Mentha piperita L.</em> and <em>Mentha arvensis L.</em>. Menthol and its derivatives are widely used in pharmaceutical, cosmetic and food industries. Among its eight isomers, (-)-menthol is the most effective one in terms of refreshing effect. While the invigorating property of (-)-menthol is generally known, this claim is based on a substantial amount of literature and experience. (-)-Menthol has consistently been reported to possess better cooling and refreshing qualities in comparison to its isomers, making it the preferred choice in a broad range of applications such as personal care products, pharmaceuticals and food additives. Additionally, the (-)-menthol molecular structure allows it to have a tighter fitting with the thermoreceptors in the skin and mucous membranes, and thus to provide a more intense cooling feeling. Thus, although others have similar properties to a degree, (-)-menthol is the best compared to all in its refreshing capacity. This study focuses on menthol and some of its esters, <em>viz.</em> menthyl acetate, propionate, butyrate, valerate and hexanoate, with the purpose of establish a connection between structural, electrostatic and electronic characteristics and biological effects. The mostly favoured interactions of the esters with biotargets were investigated at a molecular level, offering a plausible foundation for their bioactivity elucidation. This study is conducted at a quantum mechanical and molecular docking level. The results may be of possible usefulness in areas of applications, such as pharmacological research and drug.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108357"},"PeriodicalIF":2.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.compbiolchem.2025.108356
Wei Qin , Shao Xu , Jiatian Wei , Fuxi Li , Chuanxia Zhang , Huantian Zhang , Yuanxian Liu
The pathophysiological distinctions between osteoarthritis (OA) and diabetic osteoarthritis (DOA) are critical yet not well delineated. In this study, we employed single-cell RNA sequencing to clarify the unique cellular and molecular mechanisms underpinning the progression of both conditions. We identified a novel subpopulation of chondrocytes in DOA, termed 'Heat Shock' chondrocytes, marked by the expression of distinct molecular markers including HSPA1A, HSPA1B, HSPB1, and HSPA8. Our comprehensive gene expression analysis revealed a pronounced upregulation of inflammatory pathways associated with oxidative stress—namely the MAPK, NF-κB, and PI3K signaling pathways—in the effector and proliferating chondrocyte subpopulations, with a predominance in DOA. Further, our investigation into cell-cell communication demonstrated a significant diminution of intercellular signaling in DOA compared to OA. These insights not only elucidate distinct cellular heterogeneities and potential pathogenic mechanisms differentiating OA from DOA but also enhance our understanding of their molecular pathophysiology, offering novel avenues for targeted therapeutic strategies.
{"title":"Deciphering chondrocyte diversity in diabetic osteoarthritis through single-cell transcriptomics","authors":"Wei Qin , Shao Xu , Jiatian Wei , Fuxi Li , Chuanxia Zhang , Huantian Zhang , Yuanxian Liu","doi":"10.1016/j.compbiolchem.2025.108356","DOIUrl":"10.1016/j.compbiolchem.2025.108356","url":null,"abstract":"<div><div>The pathophysiological distinctions between osteoarthritis (OA) and diabetic osteoarthritis (DOA) are critical yet not well delineated. In this study, we employed single-cell RNA sequencing to clarify the unique cellular and molecular mechanisms underpinning the progression of both conditions. We identified a novel subpopulation of chondrocytes in DOA, termed 'Heat Shock' chondrocytes, marked by the expression of distinct molecular markers including HSPA1A, HSPA1B, HSPB1, and HSPA8. Our comprehensive gene expression analysis revealed a pronounced upregulation of inflammatory pathways associated with oxidative stress—namely the MAPK, NF-κB, and PI3K signaling pathways—in the effector and proliferating chondrocyte subpopulations, with a predominance in DOA. Further, our investigation into cell-cell communication demonstrated a significant diminution of intercellular signaling in DOA compared to OA. These insights not only elucidate distinct cellular heterogeneities and potential pathogenic mechanisms differentiating OA from DOA but also enhance our understanding of their molecular pathophysiology, offering novel avenues for targeted therapeutic strategies.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108356"},"PeriodicalIF":2.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.compbiolchem.2025.108353
Myeonghoon Cho , Byungkyu Park , Kyungsook Han
Cancer metastasis is the dissemination of tumor cells from the primary tumor site to other parts of the body via the lymph system or bloodstream. Metastasis is the leading cause of cancer associated death. Despite the significant advances in cancer research and treatment over the past decades, metastasis is not fully understood and difficult to predict in advance. In particular, distant metastasis is more difficult to predict than lymph node metastasis, which is the spread of cancer cells to nearby lymph nodes. Distant metastatic sites is even more difficult to predict than the occurrence of distant metastasis because the problem of predicting distant metastatic sites is a multi-class and multi-label classification problem; there are more than two classes for distant metastatic sites (bone, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. This paper presents a new method for predicting distant metastatic sites based on correlation changes of miRNAs with competing endogenous RNAs (ceRNAs) in individual cancer patients. Testing the method on independent datasets of several cancer types demonstrated a high prediction performance. In comparison of our method with other state of the art methods, our method showed a much better and more stable performance than the others. Our method can be used as useful aids in determining treatment options by predicting if and where metastasis will occur in cancer patients at early stages.
{"title":"Predicting distant metastatic sites of cancer using perturbed correlations of miRNAs with competing endogenous RNAs","authors":"Myeonghoon Cho , Byungkyu Park , Kyungsook Han","doi":"10.1016/j.compbiolchem.2025.108353","DOIUrl":"10.1016/j.compbiolchem.2025.108353","url":null,"abstract":"<div><div>Cancer metastasis is the dissemination of tumor cells from the primary tumor site to other parts of the body via the lymph system or bloodstream. Metastasis is the leading cause of cancer associated death. Despite the significant advances in cancer research and treatment over the past decades, metastasis is not fully understood and difficult to predict in advance. In particular, distant metastasis is more difficult to predict than lymph node metastasis, which is the spread of cancer cells to nearby lymph nodes. Distant metastatic sites is even more difficult to predict than the occurrence of distant metastasis because the problem of predicting distant metastatic sites is a multi-class and multi-label classification problem; there are more than two classes for distant metastatic sites (bone, liver, lung, and other organs), and a single sample can have multiple labels for multiple metastatic sites. This paper presents a new method for predicting distant metastatic sites based on correlation changes of miRNAs with competing endogenous RNAs (ceRNAs) in individual cancer patients. Testing the method on independent datasets of several cancer types demonstrated a high prediction performance. In comparison of our method with other state of the art methods, our method showed a much better and more stable performance than the others. Our method can be used as useful aids in determining treatment options by predicting if and where metastasis will occur in cancer patients at early stages.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108353"},"PeriodicalIF":2.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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