Pub Date : 2025-03-01DOI: 10.1016/j.compbiolchem.2025.108412
Xiaoli Lei , Feifei Wang , Xinying Zhang , Jiaxi Huang , Yanqin Huang
Background
Thyroid cancer (TC) is the most prevalent endocrine malignancy, with a rising incidence necessitating safer treatment strategies to reduce overtreatment and its side effects. Xiaoyao Powder (XYP), a widely used herbal formula, shows promise in treating TC. This study aims to investigate the mechanisms by which XYP may affect TC.
Methods
The components of XYP were identified through database retrieval, and targets related to TC were collected to construct a target network for key screening. GEO dataset samples analyzed immune cells and identified significantly differentially expressed core genes (SDECGs). Based on SDECG expression and clustering, samples were classified for comparison. WGCNA was employed to identify gene modules linked to clinical characteristics. ML models screened characteristic genes and constructed a nomogram validated using another GEO dataset. MR methods explored causal relationships between genes and TC.
Results
The top ten active components of XYP were identified, along with 27 SDECGs that exhibited significant differences in immune cell infiltration between TC patients and normal controls. The nomogram effectively predicted TC risk, validated through ROC curves. Key characteristic genes included SMIM1, PPP1R16A, KIAA1462, DNAJC22, and EFNA5.
Conclusion
XYP may treat TC by regulating SMIM1, PPP1R16A, KIAA1462, DNAJC22, EFNA5, and associated immune pathways; this provides theoretical support for its potential mechanisms.
{"title":"The potential mechanisms by which Xiaoyao Powder may exert therapeutic effects on thyroid cancer were examined at various levels","authors":"Xiaoli Lei , Feifei Wang , Xinying Zhang , Jiaxi Huang , Yanqin Huang","doi":"10.1016/j.compbiolchem.2025.108412","DOIUrl":"10.1016/j.compbiolchem.2025.108412","url":null,"abstract":"<div><h3>Background</h3><div>Thyroid cancer (TC) is the most prevalent endocrine malignancy, with a rising incidence necessitating safer treatment strategies to reduce overtreatment and its side effects. Xiaoyao Powder (XYP), a widely used herbal formula, shows promise in treating TC. This study aims to investigate the mechanisms by which XYP may affect TC.</div></div><div><h3>Methods</h3><div>The components of XYP were identified through database retrieval, and targets related to TC were collected to construct a target network for key screening. GEO dataset samples analyzed immune cells and identified significantly differentially expressed core genes (SDECGs). Based on SDECG expression and clustering, samples were classified for comparison. WGCNA was employed to identify gene modules linked to clinical characteristics. ML models screened characteristic genes and constructed a nomogram validated using another GEO dataset. MR methods explored causal relationships between genes and TC.</div></div><div><h3>Results</h3><div>The top ten active components of XYP were identified, along with 27 SDECGs that exhibited significant differences in immune cell infiltration between TC patients and normal controls. The nomogram effectively predicted TC risk, validated through ROC curves. Key characteristic genes included SMIM1, PPP1R16A, KIAA1462, DNAJC22, and EFNA5.</div></div><div><h3>Conclusion</h3><div>XYP may treat TC by regulating SMIM1, PPP1R16A, KIAA1462, DNAJC22, EFNA5, and associated immune pathways; this provides theoretical support for its potential mechanisms.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108412"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563517","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}
Sugars are the basic structural components of carbohydrates. Sugar transport is crucial for plants to ensure their optimal growth and development. Long-distance sugar transport occurs through either diffusion-based passive or active transport mediated by transporter proteins. In potatoes, STs play a vital role in sugar transport and total sugar accumulation. To better understand the roles of these transporters, in-depth structural, protein characterization, and tissue-specific expression analysis were performed. A total of 61 StSTs were identified and classified into eight sub-families (STP, PLT, ERD6L, INT, TMT, pGlcT, SUC, and VGT). The majority of StSTs were found in the plasma membrane, and all of them were dispersed throughout the 12 chromosomes. Exon and motif counts ranged from 1–18 and 1–10, respectively. In synteny analysis with four plant genomes, the highest (38) orthologous gene pair was found with S. lycopersicum (tomato). In 3D protein modeling, the alpha helix and transmembrane helices range varied from 32 % to 78 % and 53 %-57 %, respectively. During molecular docking analysis, the lowest binding energy was observed for Glu-StINT1 (ΔG: − 6.6 kcal/mol), Fru-StVGT1 (ΔG: − 6.1 kcal/mol), Gal-StSTP10 (ΔG: − 6.5 kcal/mol), and Suc-StINT2 (ΔG: − 7.5 kcal/mol), among 244 docking results. These complexes showed significant hydrogen and hydrophobic interactions, due to having significant amino acid residues. The molecular dynamics (MD) simulation of four complexes (Glu-StINT1, Fru-StVGT1, Gal-StSTP10, and Suc-StINT2) validated the ligand's stable attachment to the intended target proteins and it can be predicted that these complexes are the best sugar transporters of potato. In RNA-seq mediated expression analysis, StSTP12, StERD6L-6, 12, StpGlcT3, StVGT1, and StVGT2, were significantly upregulated in vegetative tissues/organs, revealing their significant role in vegetative organ development. In addition, stu-miRNA395 was the largest family interacting with StERD6L-1 and StTMT2 genes, demonstrating their significant role in sulfate metabolism. The detection and visualization of potential transcription factors (TFs) like ERF, Dof, MYB, BBR-BPC, LBD, and NAC in conjunction with the StSTs gene indicate their significant contribution to stress tolerance and DNA conversion and transcription into RNA. A significant interaction of StSTs in the PPI network might be due to their cumulative role in the same signaling pathways. The integration of these findings will guide the development of programming-based sugar transporter-mediated genetic circuits to improve the sugar accumulation in potatoes using synthetic biology approaches.
{"title":"Genome-wide exploration: Evolution, structural characterization, molecular docking, molecular dynamics simulation and expression analysis of sugar transporter (ST) gene family in potato (Solanum tuberosum)","authors":"Md. Sohel Mia , Sourav Biswas Nayan , Md. Numan Islam , Md. Enamul Kabir Talukder , Md. Sakib Hasan , Md. Riazuddin , Md. Saklain Tanver Shadhin , Md. Nayim Hossain , Tanveer A. Wani , Seema Zargar , Md. Golam Rabby","doi":"10.1016/j.compbiolchem.2025.108402","DOIUrl":"10.1016/j.compbiolchem.2025.108402","url":null,"abstract":"<div><div>Sugars are the basic structural components of carbohydrates. Sugar transport is crucial for plants to ensure their optimal growth and development. Long-distance sugar transport occurs through either diffusion-based passive or active transport mediated by transporter proteins. In potatoes, STs play a vital role in sugar transport and total sugar accumulation. To better understand the roles of these transporters, in-depth structural, protein characterization, and tissue-specific expression analysis were performed. A total of 61 StSTs were identified and classified into eight sub-families (STP, PLT, ERD6L, INT, TMT, pGlcT, SUC, and VGT). The majority of StSTs were found in the plasma membrane, and all of them were dispersed throughout the 12 chromosomes. Exon and motif counts ranged from 1–18 and 1–10, respectively. In synteny analysis with four plant genomes, the highest (38) orthologous gene pair was found with <em>S</em>. <em>lycopersicum</em> (tomato). In 3D protein modeling, the alpha helix and transmembrane helices range varied from 32 % to 78 % and 53 %-57 %, respectively. During molecular docking analysis, the lowest binding energy was observed for Glu-StINT1 (ΔG: − 6.6 kcal/mol), Fru-StVGT1 (ΔG: − 6.1 kcal/mol), Gal-StSTP10 (ΔG: − 6.5 kcal/mol), and Suc-StINT2 (ΔG: − 7.5 kcal/mol), among 244 docking results. These complexes showed significant hydrogen and hydrophobic interactions, due to having significant amino acid residues. The molecular dynamics (MD) simulation of four complexes (Glu-StINT1, Fru-StVGT1, Gal-StSTP10, and Suc-StINT2) validated the ligand's stable attachment to the intended target proteins and it can be predicted that these complexes are the best sugar transporters of potato. In RNA-seq mediated expression analysis, <em>StSTP12, StERD6L-6, 12, StpGlcT3, StVGT1,</em> and <em>StVGT2,</em> were significantly upregulated in vegetative tissues/organs, revealing their significant role in vegetative organ development. In addition, stu-miRNA395 was the largest family interacting with <em>StERD6L-1</em> and <em>StTMT2</em> genes, demonstrating their significant role in sulfate metabolism. The detection and visualization of potential transcription factors (TFs) like ERF, Dof, MYB, BBR-BPC, LBD, and NAC in conjunction with the StSTs gene indicate their significant contribution to stress tolerance and DNA conversion and transcription into RNA. A significant interaction of StSTs in the PPI network might be due to their cumulative role in the same signaling pathways. The integration of these findings will guide the development of programming-based sugar transporter-mediated genetic circuits to improve the sugar accumulation in potatoes using synthetic biology approaches.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108402"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563518","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 : 2025-03-01DOI: 10.1016/j.compbiolchem.2025.108413
Christiana E. Aruwa, Yamkela Dweba, Oladunni M. Ayodele, Saheed Sabiu
Plant products have been integral to the derivation of interventive therapies to mitigate the current scourge of antimicrobial resistance (AMR). The South African plant, Daniellia oliveri, may yet hold promise against WHO-listed critical priority pathogens like Acinetobacter baumannii and its quorum sensing (QS) system, BfmR (RstA). Hence, we bio-prospected D. oliveri compounds in a bid to provide alternative antimicrobial therapeutics, specifically, potential quorum sensing inhibitors (QSIs). This study utilized a range of in silico validated tools for the cheminformatic analysis of RstA modulating properties of D. oliveri-associated compounds. The two (2) lead compounds identified (β-carotene, β-amyrin) had docking scores of −6.8 kcal/mol, relative to −6.7 kcal/mol observed for the azithromycin reference. Only cis-Calamenene and β-amyrin had pharmacokinetic features conformed to the rule of 5 (Ro5) for selection as potential oral drug candidates. β-carotene and rutin had the best quantum reactivity attributes (lowest energy gap, and highest electronegativity and global electrophilicity). Molecular dynamics (MD) simulation revealed that all lead ligands bound to RstA stabilized system compactness and thermodynamics. Although the azithromycin-RstA system had the least ∆Gbind (-40.48 kcal/mol), rutin had the next highest ∆Gbind (-31.53 kcal/mol) of all D. oliveri compounds. Overall, these lead Daniellia oliveri metabolites may yet have potential RstA inhibitory/modulatory action upon further structural modification, and in vitro and in vivo validation tests prior to formulation into oral, interventive QSIs targeting A. baumannii RstA modulation.
{"title":"Modulating Acinetobacter baumannii BfmR (RstA) drug target: Daniellia oliveri compounds as RstA quorum sensing inhibitors","authors":"Christiana E. Aruwa, Yamkela Dweba, Oladunni M. Ayodele, Saheed Sabiu","doi":"10.1016/j.compbiolchem.2025.108413","DOIUrl":"10.1016/j.compbiolchem.2025.108413","url":null,"abstract":"<div><div>Plant products have been integral to the derivation of interventive therapies to mitigate the current scourge of antimicrobial resistance (AMR). The South African plant, <em>Daniellia oliveri</em>, may yet hold promise against WHO-listed critical priority pathogens like <em>Acinetobacter baumannii</em> and its quorum sensing (QS) system, BfmR (RstA). Hence, we bio-prospected <em>D. oliveri</em> compounds in a bid to provide alternative antimicrobial therapeutics, specifically, potential quorum sensing inhibitors (QSIs). This study utilized a range of <em>in silico</em> validated tools for the cheminformatic analysis of RstA modulating properties of <em>D. oliveri-</em>associated compounds. The two (2) lead compounds identified (β-carotene, β-amyrin) had docking scores of −6.8 kcal/mol, relative to −6.7 kcal/mol observed for the azithromycin reference. Only cis-Calamenene and β-amyrin had pharmacokinetic features conformed to the rule of 5 (Ro5) for selection as potential oral drug candidates. β-carotene and rutin had the best quantum reactivity attributes (lowest energy gap, and highest electronegativity and global electrophilicity). Molecular dynamics (MD) simulation revealed that all lead ligands bound to RstA stabilized system compactness and thermodynamics. Although the azithromycin-RstA system had the least ∆G<sub>bind</sub> (-40.48 kcal/mol), rutin had the next highest ∆G<sub>bind</sub> (-31.53 kcal/mol) of all <em>D. oliveri</em> compounds. Overall, these lead <em>Daniellia oliveri</em> metabolites may yet have potential RstA inhibitory/modulatory action upon further structural modification, and <em>in vitro</em> and <em>in vivo</em> validation tests prior to formulation into oral, interventive QSIs targeting <em>A. baumannii</em> RstA modulation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108413"},"PeriodicalIF":2.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563519","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 : 2025-02-28DOI: 10.1016/j.compbiolchem.2025.108415
Jihad Alrehaili , Razique Anwer , Faizan Abul Qais
Neurodegenerative diseases such as Parkinson's and Alzheimer's lead to the gradual decline of the nervous system, resulting in cognitive and motor impairments. With an aging population, the prevalence and associated healthcare costs are anticipated to rise. Misfolded protein aggregates are central to these diseases, disrupting cellular function and causing neuronal death. Preventing these toxic aggregates could preserve neurons and slow disease progression. Understanding how to inhibit protein aggregation is crucial for developing effective treatments. We explored the effect of nalidixic acid (NA) on protein aggregation using human serum albumin (HSA) as model protein. In vitro assays demonstrated that NA significantly reduced ThT fluorescence by 47.10 % and decreased turbidity by 63.07 %. NA also protected the protein’s hydrophobic surfaces. The α-helical content of HSA dropped from 56.23 % to 11.43 % but was restored to 38.53 % with NA. We then utilized advanced molecular simulations to understand the kinetics and mechanism of aggregation inhibition by NA. Binding studies showed that NA attaches to HSA’s subdomain IIA with a binding energy of −7.8 kcal/mol through hydrogen bonds, Van der Waals forces, and hydrophobic interactions. Molecular simulations confirmed the stability of HSA-NA complex. Additionally, NA increased solvent accessibility of HSA282–292 oligomers, reduced hydrogen bonding, and prevented β-sheet formation. Compared to existing anti-aggregation strategies, NA offers a promising alternative with its potential therapeutic applications in neurodegenerative diseases by stabilizing protein structures and preventing misfolding. These findings highlight NA's potential as a candidate for inhibiting protein aggregation and offer insights for therapeutic approaches. Further experimental studies utilizing in vivo models are needed to validate the anti-aggregation potential of NA.
{"title":"Nalidixic acid inhibits the aggregation of HSA: Utilizing the molecular simulations to uncover the detailed insights","authors":"Jihad Alrehaili , Razique Anwer , Faizan Abul Qais","doi":"10.1016/j.compbiolchem.2025.108415","DOIUrl":"10.1016/j.compbiolchem.2025.108415","url":null,"abstract":"<div><div>Neurodegenerative diseases such as Parkinson's and Alzheimer's lead to the gradual decline of the nervous system, resulting in cognitive and motor impairments. With an aging population, the prevalence and associated healthcare costs are anticipated to rise. Misfolded protein aggregates are central to these diseases, disrupting cellular function and causing neuronal death. Preventing these toxic aggregates could preserve neurons and slow disease progression. Understanding how to inhibit protein aggregation is crucial for developing effective treatments. We explored the effect of nalidixic acid (NA) on protein aggregation using human serum albumin (HSA) as model protein. In vitro assays demonstrated that NA significantly reduced ThT fluorescence by 47.10 % and decreased turbidity by 63.07 %. NA also protected the protein’s hydrophobic surfaces. The α-helical content of HSA dropped from 56.23 % to 11.43 % but was restored to 38.53 % with NA. We then utilized advanced molecular simulations to understand the kinetics and mechanism of aggregation inhibition by NA. Binding studies showed that NA attaches to HSA’s subdomain IIA with a binding energy of −7.8 kcal/mol through hydrogen bonds, Van der Waals forces, and hydrophobic interactions. Molecular simulations confirmed the stability of HSA-NA complex. Additionally, NA increased solvent accessibility of HSA<sub>282–292</sub> oligomers, reduced hydrogen bonding, and prevented β-sheet formation. Compared to existing anti-aggregation strategies, NA offers a promising alternative with its potential therapeutic applications in neurodegenerative diseases by stabilizing protein structures and preventing misfolding. These findings highlight NA's potential as a candidate for inhibiting protein aggregation and offer insights for therapeutic approaches. Further experimental studies utilizing in vivo models are needed to validate the anti-aggregation potential of NA.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108415"},"PeriodicalIF":2.6,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529172","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 : 2025-02-27DOI: 10.1016/j.compbiolchem.2025.108401
Chenhui Song
Network biology facilitates a better understanding of complex diseases. Single-sample networks retain individual information and have the potential to distinguish disease status. Previous studies mainly used bulk RNA sequencing data to construct single-sample networks, but different cell types in the tissue microenvironment perform significantly different functions. In this study, we investigated whether network topology features of cell-type-specific networks varied in different pathological states at the individual level. Protein-protein interaction network (PPI) and co-expression network of cancer and ulcerative colitis were established using four publicly single-cell RNA sequencing (scRNA-seq) datasets. We analyzed cell-cell interactions of epithelial cells and immune cells using CellChat R package. Network topology changes between normal tissues and pathological tissues were analyzed using Cytoscape software and QUACN R package. Results showed cell-cell interactions of epithelial cells were enhanced in carcinoma and adenoma. The average number of neighbors and graphindex of co-expression network increased in epithelial cells of adenoma, carcinoma and paracancer compared with normal tissues. The co-expression network density of T cells in tumors was significantly higher than that in normal tissues. The co-expression network complexity of epithelial cells in the benign tissues was associated with the grade group of paired tumors. This study suggests topological properties of cell-type-specific individual network vary in different pathological states, providing an insight into understanding complex diseases.
{"title":"Single-cell transcriptomic reveals network topology changes of cancer at the individual level","authors":"Chenhui Song","doi":"10.1016/j.compbiolchem.2025.108401","DOIUrl":"10.1016/j.compbiolchem.2025.108401","url":null,"abstract":"<div><div>Network biology facilitates a better understanding of complex diseases. Single-sample networks retain individual information and have the potential to distinguish disease status. Previous studies mainly used bulk RNA sequencing data to construct single-sample networks, but different cell types in the tissue microenvironment perform significantly different functions. In this study, we investigated whether network topology features of cell-type-specific networks varied in different pathological states at the individual level. Protein-protein interaction network (PPI) and co-expression network of cancer and ulcerative colitis were established using four publicly single-cell RNA sequencing (scRNA-seq) datasets. We analyzed cell-cell interactions of epithelial cells and immune cells using CellChat R package. Network topology changes between normal tissues and pathological tissues were analyzed using Cytoscape software and QUACN R package. Results showed cell-cell interactions of epithelial cells were enhanced in carcinoma and adenoma. The average number of neighbors and graphindex of co-expression network increased in epithelial cells of adenoma, carcinoma and paracancer compared with normal tissues. The co-expression network density of T cells in tumors was significantly higher than that in normal tissues. The co-expression network complexity of epithelial cells in the benign tissues was associated with the grade group of paired tumors. This study suggests topological properties of cell-type-specific individual network vary in different pathological states, providing an insight into understanding complex diseases.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108401"},"PeriodicalIF":2.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529033","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}
Staphylococcus aureus, a gram-positive bacterium, causes infective endocarditis, osteoarticular, skin, and respiratory infections. The emergence of multidrug-resistant strains, particularly Methicillin-resistant Staphylococcus aureus (MRSA), has caused a 21–35 % rise in bloodstream infections, complicating treatment strategies. Filamentous temperature-sensitive protein Z (FtsZ), a critical protein involved in bacterial cell division, forms a Z-ring at the division site, making it a key target for novel antibacterial therapies. In this study, 1165 phytochemicals were screened, and three lead molecules namely, Aromadendrin, Leucopelargonidin, and 7-Deacetoxy-7-oxogedunin were identified based on their favorable physicochemical properties, drug-likeness, and estimated binding affinities (− 11.73 kcal/mol, − 10.77 kcal/mol, and − 10.38 kcal/mol, respectively) against FtsZ. 100 ns Molecular dynamics simulations conducted in triplicates confirmed the stability of the FtsZ-ligand complexes.Binding free energy calculations revealed that IMPHY003535 (Leucopelargonidin) exhibited the most favorable binding free energy (-27.25 kcal/mol), followed by 7-Deacetoxy-7-oxogedunin (-15.31 kcal/mol) and Aromadendrin (-13.38 kcal/mol). Leucopelargonidin emerged as the most promising inhibitor, highlighting its potential as a lead compound for developing antibacterial agents targeting FtsZ. These findings demonstrate the significant role of phytochemicals in combating antibiotic resistance and the importance of further optimization, including in vivo studies, to assess their therapeutic potential, which could provide new treatment avenues to overcome bacterial resistance mechanisms.
{"title":"Computational insights into the inhibition of cell division in Staphylococcus aureus: Towards novel therapeutics","authors":"Roopali Bhati, Ayesha Parvez Saifi, Manisha Sangwan, Pragati Mahur, Abhishek Sharma, Amit Kumar Singh, Jayaraman Muthukumaran, Monika Jain","doi":"10.1016/j.compbiolchem.2025.108391","DOIUrl":"10.1016/j.compbiolchem.2025.108391","url":null,"abstract":"<div><div><em>Staphylococcus aureus</em>, a gram-positive bacterium, causes infective endocarditis, osteoarticular, skin, and respiratory infections. The emergence of multidrug-resistant strains, particularly Methicillin-resistant <em>Staphylococcus aureus</em> (MRSA), has caused a 21–35 % rise in bloodstream infections, complicating treatment strategies. Filamentous temperature-sensitive protein Z (FtsZ), a critical protein involved in bacterial cell division, forms a Z-ring at the division site, making it a key target for novel antibacterial therapies. In this study, 1165 phytochemicals were screened, and three lead molecules namely, Aromadendrin, Leucopelargonidin, and 7-Deacetoxy-7-oxogedunin were identified based on their favorable physicochemical properties, drug-likeness, and estimated binding affinities (− 11.73 kcal/mol, − 10.77 kcal/mol, and − 10.38 kcal/mol, respectively) against FtsZ. 100 ns Molecular dynamics simulations conducted in triplicates confirmed the stability of the FtsZ-ligand complexes.Binding free energy calculations revealed that IMPHY003535 (Leucopelargonidin) exhibited the most favorable binding free energy (-27.25 kcal/mol), followed by 7-Deacetoxy-7-oxogedunin (-15.31 kcal/mol) and Aromadendrin (-13.38 kcal/mol). Leucopelargonidin emerged as the most promising inhibitor, highlighting its potential as a lead compound for developing antibacterial agents targeting FtsZ. These findings demonstrate the significant role of phytochemicals in combating antibiotic resistance and the importance of further optimization, including in vivo studies, to assess their therapeutic potential, which could provide new treatment avenues to overcome bacterial resistance mechanisms.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108391"},"PeriodicalIF":2.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551174","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 : 2025-02-25DOI: 10.1016/j.compbiolchem.2025.108393
Xin Zhao , Shuyi Zhang , Tao Zhang , Yahui Cao , Jingjing Liu
Toxicity prediction is crucial in drug discovery, helping identify safe compounds and reduce development risks. However, the lack of known toxicity data for most compounds is a major challenge. Recently, data-driven models have gained attention as a more efficient alternative to traditional in vivo and in vitro experiments. In this paper, we propose a small-scale, data-driven toxicity prediction method based on Graph Neural Network (GNN). We introduce a joint learning strategy for multiple toxicity types and construct a graph-based model, JLGCN-MTT, to improve prediction accuracy. In addition, we integrate a transfer learning strategy that leverages data from multiple toxicity types, allowing the model to make reliable predictions even when data for a specific toxicity type is limited. We conducted experiments using data from 3566 compounds in the Tox21 dataset, which contains 12 types of toxicity-related bioactivity data. The experimental results show that JLGCN-MTT outperforms traditional machine learning methods and single-task GNN in all 12 toxicity prediction tasks, with AUC improving by over 10% in 11 tasks. For small-scale data with 50, 100, and 300 training samples, the AUC improved in all cases, with the highest improvement of 11% observed when the sample size was 50. These results demonstrate that the small-scale, data-driven toxicity prediction method we propose can achieve high prediction accuracy.
{"title":"A small-scale data driven and graph neural network based toxicity prediction method of compounds","authors":"Xin Zhao , Shuyi Zhang , Tao Zhang , Yahui Cao , Jingjing Liu","doi":"10.1016/j.compbiolchem.2025.108393","DOIUrl":"10.1016/j.compbiolchem.2025.108393","url":null,"abstract":"<div><div>Toxicity prediction is crucial in drug discovery, helping identify safe compounds and reduce development risks. However, the lack of known toxicity data for most compounds is a major challenge. Recently, data-driven models have gained attention as a more efficient alternative to traditional in vivo and in vitro experiments. In this paper, we propose a small-scale, data-driven toxicity prediction method based on Graph Neural Network (GNN). We introduce a joint learning strategy for multiple toxicity types and construct a graph-based model, JLGCN-MTT, to improve prediction accuracy. In addition, we integrate a transfer learning strategy that leverages data from multiple toxicity types, allowing the model to make reliable predictions even when data for a specific toxicity type is limited. We conducted experiments using data from 3566 compounds in the Tox21 dataset, which contains 12 types of toxicity-related bioactivity data. The experimental results show that JLGCN-MTT outperforms traditional machine learning methods and single-task GNN in all 12 toxicity prediction tasks, with AUC improving by over 10% in 11 tasks. For small-scale data with 50, 100, and 300 training samples, the AUC improved in all cases, with the highest improvement of 11% observed when the sample size was 50. These results demonstrate that the small-scale, data-driven toxicity prediction method we propose can achieve high prediction accuracy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108393"},"PeriodicalIF":2.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551175","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 : 2025-02-22DOI: 10.1016/j.compbiolchem.2025.108392
Jianxin Wang , Yongxin Zhu , Yushuang Liu , Bin Yu
The goal of drug discovery based on deep learning is to generate drug molecules that bind to a given target protein. Recently, the use of three-dimensional molecular structures has shown superior performance over other two-dimensional structural models. However, most of the current depth generation models are based on ligands, and in the process of molecular generation, the models only learn the independent information of ligands or targets, without considering the complex interaction information of them. In addition, chemical knowledge was not considered in the process of molecular formation, which led to generation unreasonable drug molecular structure. In order to solve above problems, this paper proposes DTF-diffusion, a 3D equivariant diffusion generation model based on ligand-target information fusion. Firstly based on the diffusion model, DTF-diffusion uses multimodal feature fusion module proposed in this paper to fuse the three-dimensional position feature information of ligand molecules and targets, and extract advanced hidden features from ligand atom information and target sequence information. Secondly, this paper designs a chemical rule discrimination module, and learns the real ligand molecular structure and the characteristic information of the generated ligand molecules through the discriminator, and then capture the chemical rules in the drug molecular structure, which effectively improve the rationality of the ligand structure generated by the model. This paper evaluates the generation performance of DTF-diffusion and other baseline methods from multiple perspectives based on the CrossDocket2020 dataset. In the quantitative estimate of drug-likeness index, DTF-diffusion is 3.85 % higher than the existing optimal model, the drug validity index increased by 4.34 %. More generation experiments have proved that DTF-diffusion has excellent performance, indicating that it has a good application prospect in the field of drug molecule generation.
{"title":"DTF-diffusion: A 3D equivariant diffusion generation model based on ligand-target information fusion","authors":"Jianxin Wang , Yongxin Zhu , Yushuang Liu , Bin Yu","doi":"10.1016/j.compbiolchem.2025.108392","DOIUrl":"10.1016/j.compbiolchem.2025.108392","url":null,"abstract":"<div><div>The goal of drug discovery based on deep learning is to generate drug molecules that bind to a given target protein. Recently, the use of three-dimensional molecular structures has shown superior performance over other two-dimensional structural models. However, most of the current depth generation models are based on ligands, and in the process of molecular generation, the models only learn the independent information of ligands or targets, without considering the complex interaction information of them. In addition, chemical knowledge was not considered in the process of molecular formation, which led to generation unreasonable drug molecular structure. In order to solve above problems, this paper proposes DTF-diffusion, a 3D equivariant diffusion generation model based on ligand-target information fusion. Firstly based on the diffusion model, DTF-diffusion uses multimodal feature fusion module proposed in this paper to fuse the three-dimensional position feature information of ligand molecules and targets, and extract advanced hidden features from ligand atom information and target sequence information. Secondly, this paper designs a chemical rule discrimination module, and learns the real ligand molecular structure and the characteristic information of the generated ligand molecules through the discriminator, and then capture the chemical rules in the drug molecular structure, which effectively improve the rationality of the ligand structure generated by the model. This paper evaluates the generation performance of DTF-diffusion and other baseline methods from multiple perspectives based on the CrossDocket2020 dataset. In the quantitative estimate of drug-likeness index, DTF-diffusion is 3.85 % higher than the existing optimal model, the drug validity index increased by 4.34 %. More generation experiments have proved that DTF-diffusion has excellent performance, indicating that it has a good application prospect in the field of drug molecule generation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108392"},"PeriodicalIF":2.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508099","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 : 2025-02-21DOI: 10.1016/j.compbiolchem.2025.108390
Manoj Kumar Prajapati , Abhilasha Mittal , Pritipadma Panda
Melanoma, an aggressive skin cancer, poses significant therapeutic challenges due to its resistance to conventional therapies and high metastatic potential. From this perspective, phytoflavonoids from different medicinal and aromatic plants gained attention due to their diverse multimodal anticancer effects with higher antioxidant and anti-inflammatory properties. This study explores phytoflavonoid potency against melanoma via a computer-aided drug design (CADD) platform. Using the core moiety of flavonoids (flavan), four most putative targets, such as cyclin-dependent kinases 1 and 5 (CDK1, CDK5), cell division cycles 25B and 225 C (CDC25B, and CDC225C), have been identified through a network pharmacology approach using TNMplot datasets (GenChip and RNA sequence). Further, 44 phytoflavonoids were selected from extensive literature, and molecular docking studies were carried out against four targets along with standard drugs using AutoDock 4.2 software. Subsequently, physicochemical, toxicity, pharmacokinetics, and drug-ability profiles of phytoflavonoids were predicted. Based on potency and drug-ability, we have selected ‘CDK1-naringenin’ with the standard drug complex, ‘CDK1-dinaciclib,’ for molecular dynamic simulation at 100 nanoseconds using GROMACS 2020 software. Based on potency (average docking score: 8.35 kcal/mol.), physicochemical properties (obeyed Lipinski rule of five), toxicity (class-IV), fifty percent lethal dose (2000 mg/kg), bioavailability (0.55), drug-likeness score (0.82), along with ideal pharmacokinetics profiles and higher protein-ligand stability, naringenin is considered as a potential and non-toxic anticancer candidate to be used for melanoma as alternative or complementary agent. The integrative and systematic analyses not only highlight the potential of phytoflavonoids but also select the potential lead from the library within limited resources to accelerate the current anticancer drug discovery process.
{"title":"Phytoflavonoids as alternative therapeutic effect for melanoma: Integrative Network pharmacology, molecular dynamics and drug-likeness profiling for lead discovery","authors":"Manoj Kumar Prajapati , Abhilasha Mittal , Pritipadma Panda","doi":"10.1016/j.compbiolchem.2025.108390","DOIUrl":"10.1016/j.compbiolchem.2025.108390","url":null,"abstract":"<div><div>Melanoma, an aggressive skin cancer, poses significant therapeutic challenges due to its resistance to conventional therapies and high metastatic potential. From this perspective, phytoflavonoids from different medicinal and aromatic plants gained attention due to their diverse multimodal anticancer effects with higher antioxidant and anti-inflammatory properties. This study explores phytoflavonoid potency against melanoma via a computer-aided drug design (CADD) platform. Using the core moiety of flavonoids (flavan), four most putative targets, such as cyclin-dependent kinases 1 and 5 (CDK1, CDK5), cell division cycles 25B and 225 C (CDC25B, and CDC225C), have been identified through a network pharmacology approach using TNMplot datasets (GenChip and RNA sequence). Further, 44 phytoflavonoids were selected from extensive literature, and molecular docking studies were carried out against four targets along with standard drugs using AutoDock 4.2 software. Subsequently, physicochemical, toxicity, pharmacokinetics, and drug-ability profiles of phytoflavonoids were predicted. Based on potency and drug-ability, we have selected ‘CDK1-naringenin’ with the standard drug complex, ‘CDK1-dinaciclib,’ for molecular dynamic simulation at 100 nanoseconds using GROMACS 2020 software. Based on potency (average docking score: 8.35 kcal/mol.), physicochemical properties (obeyed Lipinski rule of five), toxicity (class-IV), fifty percent lethal dose (2000 mg/kg), bioavailability (0.55), drug-likeness score (0.82), along with ideal pharmacokinetics profiles and higher protein-ligand stability, naringenin is considered as a potential and non-toxic anticancer candidate to be used for melanoma as alternative or complementary agent. The integrative and systematic analyses not only highlight the potential of phytoflavonoids but also select the potential lead from the library within limited resources to accelerate the current anticancer drug discovery process.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108390"},"PeriodicalIF":2.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563520","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}
Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by cognitive decline due to the accumulation of amyloid-beta plaques, neurofibrillary tangles, and decreased acetylcholine levels caused by acetylcholinesterase (AChE) activity. Current treatments using synthetic acetylcholinesterase inhibitors (AChEIs) provide only symptomatic relief and are associated with adverse effects, highlighting the need for safer and more effective alternatives. This study investigates the potential of phytoconstituents from the plant Senecio candicans as natural AChE inhibitors for AD treatment. Using structure-based virtual screening, molecular docking, and molecular dynamics simulations, we evaluated several compounds from Senecio candicans for their binding affinity, stability, and inhibitory activity against AChE. The findings identified compounds such as Estra-135(10)-trien-17β-ol and Vulgarone A, which demonstrated strong binding affinities and stable interactions with AChE, comparable to or surpassing the clinically used drug Donepezil. These phytoconstituents exhibited potential as effective AChEIs with potentially fewer side effects. The results underscore the therapeutic potential of plant-based molecules for drug discovery, offering a promising avenue for developing new treatments for neurodegenerative diseases. Combining phytochemical studies with computational methods provides a powerful approach to identifying novel therapeutic agents. This study suggests that phytoconstituents from Senecio candicans could serve as safer alternatives for managing AD. Further experimental validation and clinical studies are necessary to confirm these compounds' efficacy and safety, paving the way for innovative, plant-derived treatments for Alzheimer's disease.
{"title":"Phytocompounds of Senecio candicans as potential acetylcholinesterase inhibitors targeting Alzheimer's disease: A structure-based virtual screening and molecular dynamics simulation study","authors":"Tamilarasi Sambu Periyasamy, Ajay Kasivishwanathan, Gilbert Roy, Nishu Sekar, Hariprasath Lakshmanan","doi":"10.1016/j.compbiolchem.2025.108396","DOIUrl":"10.1016/j.compbiolchem.2025.108396","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by cognitive decline due to the accumulation of amyloid-beta plaques, neurofibrillary tangles, and decreased acetylcholine levels caused by acetylcholinesterase (AChE) activity. Current treatments using synthetic acetylcholinesterase inhibitors (AChEIs) provide only symptomatic relief and are associated with adverse effects, highlighting the need for safer and more effective alternatives. This study investigates the potential of phytoconstituents from the plant <em>Senecio candicans</em> as natural AChE inhibitors for AD treatment. Using structure-based virtual screening, molecular docking, and molecular dynamics simulations, we evaluated several compounds from <em>Senecio candicans</em> for their binding affinity, stability, and inhibitory activity against AChE. The findings identified compounds such as Estra-135(10)-trien-17β-ol and Vulgarone A, which demonstrated strong binding affinities and stable interactions with AChE, comparable to or surpassing the clinically used drug Donepezil. These phytoconstituents exhibited potential as effective AChEIs with potentially fewer side effects. The results underscore the therapeutic potential of plant-based molecules for drug discovery, offering a promising avenue for developing new treatments for neurodegenerative diseases. Combining phytochemical studies with computational methods provides a powerful approach to identifying novel therapeutic agents. This study suggests that phytoconstituents from <em>Senecio candicans</em> could serve as safer alternatives for managing AD. Further experimental validation and clinical studies are necessary to confirm these compounds' efficacy and safety, paving the way for innovative, plant-derived treatments for Alzheimer's disease.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"117 ","pages":"Article 108396"},"PeriodicalIF":2.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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