Drug–Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.
{"title":"Drug–target prediction through self supervised learning with dual task ensemble approach","authors":"Surabhi Mishra, Ashish Chinthala, Mahua Bhattacharya","doi":"10.1016/j.compbiolchem.2024.108244","DOIUrl":"10.1016/j.compbiolchem.2024.108244","url":null,"abstract":"<div><div>Drug–Target interaction (DTI) prediction, a transformative approach in pharmaceutical research, seeks novel therapeutic applications for computational method based virtual screening, existing drugs to address untreated diseases and discovery of existing drugs side effects. The proposed model predict DTI through Heterogeneous biological network by combining drug, genes and disease related knowledge. For the purpose of embedding extraction Self-supervised learning (SSL) has been used which, trains models through pretext tasks, eliminating the need for manual annotations. The pretext tasks are related to either structural based information or similarity based information. To mitigate GNN vulnerability to non-robustness, ensemble learning can be incorporated into GNNs, harnessing multiple models to enhance robustness. This paper introduces a Graph neural network based architecture consisting of task based module and ensemble module for link prediction of DTI. The ensemble module of dual task combinations, both in cold start and warm start scenarios achieve very good performance as it provide 0.960 in cold start and 0.970 in warm start mean AUCROC score with less deviation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108244"},"PeriodicalIF":2.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514759","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 : 2024-10-09DOI: 10.1016/j.compbiolchem.2024.108232
Minakshee Patil , Prachi Mukherji , Vijay Wadhai
Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this affects the daily life of the person. This leads to educational and social impairments and this acts as a major risk for suicide. As a result, the identification and treatment for this disorder are essential. By applying Deep learning (DL) algorithms to medical data, the mental condition of a person can be predicted. However, the traditional deep learning models face the challenge in processing the huge sized data. Hence, FL has emerged as an efficient solution for addressing the data size issue of DL. Here, Depression detection in adolescents is carried out by considering the FL framework, which comprises two modules, namely the local module and the Global module. The detection process is done in the local module using the proposed Exponential African Pelican Optimization based Deep Convolutional Neural Network (ExpAPO-DCNN), whereas the Global module produces the aggregated output of the local module. In this research, FL utilizes the DL model in producing the output, where the DL model considered two modalities of inputs, such as speech signal and Magnetic Resonance Imaging (MRI) image. The processing steps used for this research are pre-processing, feature extraction and detection. For MRI and speech signals, all the above processes are carried out individually. Finally, both the outputs are fused utilizing the overlap coefficient. The ExpAPO-DCNN obtained accuracy, Loss, Root mean Squared error (RMSE), Mean Squared error (MSE), True Negative rate (TNR), and True Positive rate (TPR) of 98.00 %, 0.023, 0.058, 0.240, 97.90 %, and 96.30 %, respectively.
{"title":"Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection","authors":"Minakshee Patil , Prachi Mukherji , Vijay Wadhai","doi":"10.1016/j.compbiolchem.2024.108232","DOIUrl":"10.1016/j.compbiolchem.2024.108232","url":null,"abstract":"<div><div>Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes for maturity. Recently, Depression arises as a common mental health issue in adolescents and this affects the daily life of the person. This leads to educational and social impairments and this acts as a major risk for suicide. As a result, the identification and treatment for this disorder are essential. By applying Deep learning (DL) algorithms to medical data, the mental condition of a person can be predicted. However, the traditional deep learning models face the challenge in processing the huge sized data. Hence, FL has emerged as an efficient solution for addressing the data size issue of DL. Here, Depression detection in adolescents is carried out by considering the FL framework, which comprises two modules, namely the local module and the Global module. The detection process is done in the local module using the proposed Exponential African Pelican Optimization based Deep Convolutional Neural Network (ExpAPO-DCNN), whereas the Global module produces the aggregated output of the local module. In this research, FL utilizes the DL model in producing the output, where the DL model considered two modalities of inputs, such as speech signal and Magnetic Resonance Imaging (MRI) image. The processing steps used for this research are pre-processing, feature extraction and detection. For MRI and speech signals, all the above processes are carried out individually. Finally, both the outputs are fused utilizing the overlap coefficient. The ExpAPO-DCNN obtained accuracy, Loss, Root mean Squared error (RMSE), Mean Squared error (MSE), True Negative rate (TNR), and True Positive rate (TPR) of 98.00 %, 0.023, 0.058, 0.240, 97.90 %, and 96.30 %, respectively.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108232"},"PeriodicalIF":2.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445188","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 : 2024-10-09DOI: 10.1016/j.compbiolchem.2024.108238
Qurrat ul Ain , Sohaib Asif
The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100% for Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE), while achieving rates of 96.30% for Salmonella Typhimurium (ST), 87.13% for Staphylococcus aureus (SA), and 94.12% for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.
{"title":"A Novel Ensemble Approach with Deep Transfer Learning for Accurate Identification of Foodborne Bacteria from Hyperspectral Microscopy","authors":"Qurrat ul Ain , Sohaib Asif","doi":"10.1016/j.compbiolchem.2024.108238","DOIUrl":"10.1016/j.compbiolchem.2024.108238","url":null,"abstract":"<div><div>The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100% for <em>Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE)</em>, while achieving rates of 96.30% for <em>Salmonella Typhimurium (ST),</em> 87.13% for <em>Staphylococcus aureus (SA</em>), and 94.12% for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108238"},"PeriodicalIF":2.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428691","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 : 2024-10-06DOI: 10.1016/j.compbiolchem.2024.108236
Vinicius S. Nunes , Alexandre P. Rogério , Odonírio Abrahão , Charles N. Serhan
Leukotriene B4 (LTB4) is a lipid inflammatory mediator derived from arachidonic acid (AA). Leukotriene B4 receptor 1 (BLT1), a G protein-coupled receptor (GPCR), is a receptor of LTB4. Nonetheless, the resolution of inflammation is driven by specialized pro-resolving lipid mediators (SPMs) such as resolvins E1 (RvE1) and E2 (RvE2). Both resolvins are derived from omega-3 fatty acid eicosapentaenoic acid (EPA). Here, long-term molecular dynamics simulations (MD) were performed to investigate the activation of the BLT1 receptor using two pro-resolution agonists (RvE1 and RvE2) and an inflammatory agonist (LTB4). We have analyzed the receptor's activation state, electrostatic interactions, and the binding affinity the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach. The results showed that LTB4 and RvE1 have kept the receptor in an active state by higher simulation time. MD showed that the ligand-receptor interactions occurred mainly through residues H94, R156, and R267. The MMPBSA calculations showed residues R156 and R267 were the two mainly hotspots. Our MMPBSA results were compatible with experimental results from other studies. Overall, the results from this study provide new insights into the activation mechanisms of the BLT1 receptor, reinforcing the role of critical residues and interactions in the binding of pro-resolution and pro-inflammatory agonists.
{"title":"Leukotriene B4 receptor 1 (BLT1) activation by leukotriene B4 (LTB4) and E resolvins (RvE1 and RvE2)","authors":"Vinicius S. Nunes , Alexandre P. Rogério , Odonírio Abrahão , Charles N. Serhan","doi":"10.1016/j.compbiolchem.2024.108236","DOIUrl":"10.1016/j.compbiolchem.2024.108236","url":null,"abstract":"<div><div>Leukotriene B4 (LTB<sub>4</sub>) is a lipid inflammatory mediator derived from arachidonic acid (AA). Leukotriene B4 receptor 1 (BLT1), a G protein-coupled receptor (GPCR), is a receptor of LTB<sub>4</sub>. Nonetheless, the resolution of inflammation is driven by specialized pro-resolving lipid mediators (SPMs) such as resolvins E1 (RvE1) and E2 (RvE2). Both resolvins are derived from omega-3 fatty acid eicosapentaenoic acid (EPA). Here, long-term molecular dynamics simulations (MD) were performed to investigate the activation of the BLT1 receptor using two pro-resolution agonists (RvE1 and RvE2) and an inflammatory agonist (LTB<sub>4</sub>). We have analyzed the receptor's activation state, electrostatic interactions, and the binding affinity the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach. The results showed that LTB4 and RvE1 have kept the receptor in an active state by higher simulation time. MD showed that the ligand-receptor interactions occurred mainly through residues H94, R156, and R267. The MMPBSA calculations showed residues R156 and R267 were the two mainly hotspots. Our MMPBSA results were compatible with experimental results from other studies. Overall, the results from this study provide new insights into the activation mechanisms of the BLT1 receptor, reinforcing the role of critical residues and interactions in the binding of pro-resolution and pro-inflammatory agonists.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108236"},"PeriodicalIF":2.6,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428646","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}
Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as -helices, -sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that -helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to -helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at https://proseqanalyser.iitgn.ac.in/
{"title":"Statistical analysis of the unique characteristics of secondary structures in proteins","authors":"Nitin Kumar Singh , Manish Agarwal , Mithun Radhakrishna","doi":"10.1016/j.compbiolchem.2024.108237","DOIUrl":"10.1016/j.compbiolchem.2024.108237","url":null,"abstract":"<div><div>Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as <span><math><mi>α</mi></math></span>-helices, <span><math><mi>β</mi></math></span>-sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that <span><math><mi>α</mi></math></span>-helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to <span><math><mi>α</mi></math></span>-helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at <span><span>https://proseqanalyser.iitgn.ac.in/</span><svg><path></path></svg></span></div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108237"},"PeriodicalIF":2.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407387","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 : 2024-10-05DOI: 10.1016/j.compbiolchem.2024.108229
Bincan Jiang, Ziyang Chen, Jiajie Zhou
Background
Lower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently.
Methods
Survival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR)
Results
A risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing.
Conclusion
In conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.
{"title":"A novel prognostic risk score model based on RNA editing level in lower-grade glioma","authors":"Bincan Jiang, Ziyang Chen, Jiajie Zhou","doi":"10.1016/j.compbiolchem.2024.108229","DOIUrl":"10.1016/j.compbiolchem.2024.108229","url":null,"abstract":"<div><h3>Background</h3><div>Lower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently.</div></div><div><h3>Methods</h3><div>Survival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR)</div></div><div><h3>Results</h3><div>A risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing.</div></div><div><h3>Conclusion</h3><div>In conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108229"},"PeriodicalIF":2.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395947","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}
Banana Fusarium Wilt (BFW), caused by Fusarium oxysporum f. sp. cubense (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in Musa accuminata subsp. aalaccensis. Through in silico analyses, we explored interactions between Musa spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in Musa spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected Musa metabolites, such as 3-Phenylphenol, Catechin, and Epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7 kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in Musa spp. identified key domains like Chalcone synthase, Phenylalanine ammonia-lyase, Aminotran 1–2, and CoA-ligase, which are integral to phenylpropanoid production—a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for Musa spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.
{"title":"In silico profiling, docking analysis, and protein interactions of secondary metabolites in Musa spp. Against the SGE1 protein of Fusarium oxysporum f. sp. cubense","authors":"Preeti Sonkar , Shalini Purwar , Prachi Bhargva , Ravindra Pratap Singh , Jawaher Alkahtani , Abdulrahman Al-hashimi , Yheni Dwiningsih , Salim Khan","doi":"10.1016/j.compbiolchem.2024.108230","DOIUrl":"10.1016/j.compbiolchem.2024.108230","url":null,"abstract":"<div><div>Banana Fusarium Wilt (BFW), caused by <em>Fusarium oxysporum</em> f. sp. <em>cubense</em> (Foc), threatens banana crops globally, with the pathogen's virulence partially regulated by the Sge1 transcription factor, which enhances disease severity. Certain Musa species display resistance to Foc, suggesting inherent genetic traits that confer immunity against Sge1Foc. This study utilized bioinformatics tools to investigate the mechanisms underlying this resistance in <em>Musa accuminata</em> subsp. a<em>alaccensis</em>. Through in silico analyses, we explored interactions between <em>Musa</em> spp. and Foc, focusing on the Sge1 protein. Tools such as Anti-SMASH, AutoDockVina 4.0, STRING, and Phoenix facilitated the profiling of secondary metabolites in <em>Musa</em> spp. and the identification of biosynthetic gene clusters involved in defense. Our results indicate that secondary metabolites, including saccharides, terpenes, and polyketides, are crucial to the plant's immune response. Molecular docking studies of selected <em>Musa</em> metabolites, such as 3-Phenylphenol, Catechin, and Epicatechin, revealed 3-Phenylphenol as having the highest binding affinity to the Sge1Foc protein (-6.7 kcal/mol).Further analysis of gene clusters associated with secondary metabolite biosynthesis in <em>Musa</em> spp. identified key domains like Chalcone synthase, Phenylalanine ammonia-lyase, Aminotran 1–2, and CoA-ligase, which are integral to phenylpropanoid production—a critical pathway for secondary metabolites. The study highlights that the phenylpropanoid pathway and secondary metabolite biosynthesis are vital for <em>Musa</em> spp. resistance to Foc. Flavonoids and lignin may inhibit Sge1 protein formation, potentially disrupting Foc's cellular processes. These findings emphasize the role of phenylpropanoid pathways and secondary metabolites in combating BFW and suggest that targeting these pathways could offer innovative strategies for enhancing resistance and controlling BFW in banana crops. This research lays the groundwork for developing sustainable methods to protect banana cultivation and ensure food security.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108230"},"PeriodicalIF":2.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444593","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 : 2024-10-03DOI: 10.1016/j.compbiolchem.2024.108235
Narjes Asghari , Ali Kian Saei , Marco Cordani , Zahra Nayeri , Mohammad Amin Moosavi
Autophagy is a critical cellular process for degrading damaged organelles and proteins under stressful conditions and has casually been shown to contribute to tumor survival and drug resistance. Sequestosome-1 (SQSTM1/p62) is an autophagy receptor that interacts with its binding partners via the LC3-interacting region (LIR). The p62 protein has been a highly researched target for its critical role in selective autophagy. In this study, we aimed to identify FDA-approved drugs that bind to the LIR motif of p62 and inhibit its LIR function, which could be useful targets for modulating autophagy. To this, the homology model of the p62 protein was predicted using biological data, and docking analysis was performed using Molegro Virtual Docker and PyRx softwares. We further assessed the toxicity profile of the drugs using the ProTox-II server and performed dynamics simulations on the effective candidate drugs identified. The results revealed that the kanamycin, velpatasvir, verteporfin, and temoporfin significantly decreased the binding of LIR to the p62 protein. Finally, we experimentally confirmed that Kanamycin can inhibit autophagy-associated acidic vesicular formation in breast cancer MCF-7 and MDA-MB 231 cells. These repositioned drugs may represent novel autophagy modulators in clinical management, warranting further investigation.
{"title":"Drug repositioning identifies potential autophagy inhibitors for the LIR motif p62/SQSTM1 protein","authors":"Narjes Asghari , Ali Kian Saei , Marco Cordani , Zahra Nayeri , Mohammad Amin Moosavi","doi":"10.1016/j.compbiolchem.2024.108235","DOIUrl":"10.1016/j.compbiolchem.2024.108235","url":null,"abstract":"<div><div>Autophagy is a critical cellular process for degrading damaged organelles and proteins under stressful conditions and has casually been shown to contribute to tumor survival and drug resistance. Sequestosome-1 (SQSTM1/p62) is an autophagy receptor that interacts with its binding partners via the LC3-interacting region (LIR). The p62 protein has been a highly researched target for its critical role in selective autophagy. In this study, we aimed to identify FDA-approved drugs that bind to the LIR motif of p62 and inhibit its LIR function, which could be useful targets for modulating autophagy. To this, the homology model of the p62 protein was predicted using biological data, and docking analysis was performed using Molegro Virtual Docker and PyRx softwares. We further assessed the toxicity profile of the drugs using the ProTox-II server and performed dynamics simulations on the effective candidate drugs identified. The results revealed that the kanamycin, velpatasvir, verteporfin, and temoporfin significantly decreased the binding of LIR to the p62 protein. Finally, we experimentally confirmed that Kanamycin can inhibit autophagy-associated acidic vesicular formation in breast cancer MCF-7 and MDA-MB 231 cells. These repositioned drugs may represent novel autophagy modulators in clinical management, warranting further investigation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108235"},"PeriodicalIF":2.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382778","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 : 2024-10-02DOI: 10.1016/j.compbiolchem.2024.108234
Kottakkaran Sooppy Nisar , Iqra Naz , Muhammad Asif Zahoor Raja , Muhammad Shoaib
The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.
{"title":"Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks","authors":"Kottakkaran Sooppy Nisar , Iqra Naz , Muhammad Asif Zahoor Raja , Muhammad Shoaib","doi":"10.1016/j.compbiolchem.2024.108234","DOIUrl":"10.1016/j.compbiolchem.2024.108234","url":null,"abstract":"<div><div>The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108234"},"PeriodicalIF":2.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428690","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 : 2024-09-30DOI: 10.1016/j.compbiolchem.2024.108218
Shabbir Muhammad , Amina Faiz , Shamsa Bibi , Shafiq Ur Rehman , Mohammad Y. Alshahrani
Emerging antibiotic resistance in bacteria threatens immune efficacy and increases susceptibility to bone degradation and arthritic disorders. In our current study, we utilized a three-layer in-silico screening approach, employing quantum chemical methods, molecular docking, and molecular dynamic methods to explore the novel drug candidates similar in structure to floroquinolone (ciprofloxacin). We investigated the interaction of novel similar compounds of ciprofloxacin with both a bacterial protein S. aureus TyrRS (1JIJ) and a protein associated with gout arthritis Neutrophil collagenase (3DPE). UTIs and gout are interconnected through the elevation of uric acid levels. We aimed to identify compounds with dual functionality: antibacterial activity against UTIs and antirheumatic properties. Our screening based on several methods, sorted out six promising ligands. Four of these (L1, L2, L3, and L6) demonstrated favorable hydrogen bonding with both proteins and were selected for further analysis. These ligands showed binding affinities of −8.3 to −9.1 kcal/mol with both proteins, indicating strong interaction potential. Notably, L6 exhibited highest binding energies of −9.10 and −9.01 kcal/mol with S. aureus TyrRS and Neutrophil collagenase respectively. Additionally, the pkCSM online database conducted ADMET analysis on all lead ligand suggested that L6 might exhibit the highest intestinal absorption and justified total clearance rate. Moreover, L6 showed a best predicted inhibition constant with both proteins. The average RMSF values for all complex systems, namely L1, L2, L3 and L6 are 0.43 Å, 0.57 Å, 0.55 Å, and 0.51 Å, respectively where the ligand residues show maximum stability. The smaller energy gap of 3.85 eV between the HOMO and LUMO of the optimized molecule L1 and L6 suggests that these are biologically active compound. All the selected four drugs show considerable stabilization energy ranging from 44.78 to 103.87 kcal/mol, which means all four compounds are chemically and physically stable. Overall, this research opens exciting avenues for the development of new therapeutic agents with dual functionalities for antibacterial and antiarthritic drug designing.
{"title":"Investigation of dual inhibition of antibacterial and antiarthritic drug candidates using combined approach including molecular dynamics, docking and quantum chemical methods","authors":"Shabbir Muhammad , Amina Faiz , Shamsa Bibi , Shafiq Ur Rehman , Mohammad Y. Alshahrani","doi":"10.1016/j.compbiolchem.2024.108218","DOIUrl":"10.1016/j.compbiolchem.2024.108218","url":null,"abstract":"<div><div>Emerging antibiotic resistance in bacteria threatens immune efficacy and increases susceptibility to bone degradation and arthritic disorders. In our current study, we utilized a three-layer in-silico screening approach, employing quantum chemical methods, molecular docking, and molecular dynamic methods to explore the novel drug candidates similar in structure to floroquinolone (ciprofloxacin). We investigated the interaction of novel similar compounds of ciprofloxacin with both a bacterial protein S. aureus TyrRS (1JIJ) and a protein associated with gout arthritis Neutrophil collagenase (3DPE). UTIs and gout are interconnected through the elevation of uric acid levels. We aimed to identify compounds with dual functionality: antibacterial activity against UTIs and antirheumatic properties. Our screening based on several methods, sorted out six promising ligands. Four of these (<strong>L1</strong>, <strong>L2</strong>, <strong>L3</strong>, and <strong>L6</strong>) demonstrated favorable hydrogen bonding with both proteins and were selected for further analysis. These ligands showed binding affinities of −8.3 to −9.1 kcal/mol with both proteins, indicating strong interaction potential. Notably, <strong>L6</strong> exhibited highest binding energies of −9.10 and −9.01 kcal/mol with S. aureus TyrRS and Neutrophil collagenase respectively. Additionally, the pkCSM online database conducted ADMET analysis on all lead ligand suggested that <strong>L6</strong> might exhibit the highest intestinal absorption and justified total clearance rate. Moreover, <strong>L6</strong> showed a best predicted inhibition constant with both proteins. The average RMSF values for all complex systems, namely <strong>L1</strong>, <strong>L2</strong>, <strong>L3</strong> and <strong>L6</strong> are 0.43 Å, 0.57 Å, 0.55 Å, and 0.51 Å, respectively where the ligand residues show maximum stability. The smaller energy gap of 3.85 eV between the HOMO and LUMO of the optimized molecule <strong>L1</strong> and <strong>L6</strong> suggests that these are biologically active compound. All the selected four drugs show considerable stabilization energy ranging from 44.78 to 103.87 kcal/mol, which means all four compounds are chemically and physically stable. Overall, this research opens exciting avenues for the development of new therapeutic agents with dual functionalities for antibacterial and antiarthritic drug designing.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108218"},"PeriodicalIF":2.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395949","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号