Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
Huosu Yangwei(HSYW) Formula is a traditioanl Chinese herbal medicine that has been extensively used to treat chronic atrophic gastritis, precancerous lesions of gastric cancer and advanced gastric cancer. However, the...Huosu Yangwei(HSYW) Formula is a traditioanl Chinese herbal medicine that has been extensively used to treat chronic atrophic gastritis, precancerous lesions of gastric cancer and advanced gastric cancer. However, the effective compounds of HSYW and its related anti-tumor mechanisms are not completely understood. In the current study, 160 ingredients of HSYW were identified and 64 effective compounds were screened by the ADMET evaluation. Furthermore, 64 effective compounds and 2579 potential targets were mapped based on public databases. Animal experiments demonstrated that HSYW significantly inhibited tumor growth in vivo. Transcriptional profiles revealed that 81 mRNAs were differentially expressed in HSYW-treated N87-bearing Balb/c mice. Network pharmacology and PPI network showed that 12 core genes acted as potential markers to evaluate the curative effects of HSYW. Bioinformatics and qRT-PCR results suggested that HSYW might regulate the mRNA expression of DNAJB4, CALD,AKR1C1, CST1, CASP1, PREX1, SOCS3 and PRDM1 against tumor growth in N87-bearing Balb/c mice.展开更多
Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has ...Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.展开更多
Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,a...Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.展开更多
Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported t...Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.展开更多
Lung cancer is a prevalent malignancy,and fatalities of the disease exceed 400,000 cases worldwide.Lung squamous cell carcinoma(LUSC)has been recognized as the most common pathological form of lung cancer.The comprehe...Lung cancer is a prevalent malignancy,and fatalities of the disease exceed 400,000 cases worldwide.Lung squamous cell carcinoma(LUSC)has been recognized as the most common pathological form of lung cancer.The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management.In this study,we aim to identify a panel of signature genes closely associated with LUSC,which can provide novel insights into the progression of LUSC.Gene expression profiles were retrieved from public resources including gene expression omnibus(GEO)and the cancer genome atlas(TCGA)database.Differentially expressed genes(DEGs)between LUSC specimens and normal lung tissues were identified by bioinformatics analyses.A total of 66 DEGs were identified based on two cohorts of data.CytoHubba plugin of Cytoscape software was utilized for the further analyses of the top 10 candidate hub genes including OGN,ABI3BP,MAMDC2,FGF7,FAM107A,SPARCL1,DCN,COL14A1,and MFAP4 and CHRDL1,which showed significant downregulation in LUSC.Two LUSC cell lines were used to validate the functions of CHRDL1 and FAM107A through overexpression experiment.Together,our data revealed novel candidate tumor-suppressor genes in LUSC,suggesting previously unappreciated mechanisms in the progression of LUSC.展开更多
Predicting essential proteins is crucial for discovering the process of cellular organization and viability.We propose biased random walk with restart algorithm for essential proteins prediction,called BRWR.Firstly,th...Predicting essential proteins is crucial for discovering the process of cellular organization and viability.We propose biased random walk with restart algorithm for essential proteins prediction,called BRWR.Firstly,the common process of practice walk often sets the probability of particles transferring to adjacent nodes to be equal,neglecting the influence of the similarity structure on the transition probability.To address this problem,we redefine a novel transition probability matrix by integrating the gene express similarity and subcellular location similarity.The particles can obtain biased transferring probabilities to perform random walk so as to further exploit biological properties embedded in the network structure.Secondly,we use gene ontology(GO)terms score and subcellular score to calculate the initial probability vector of the random walk with restart.Finally,when the biased random walk with restart process reaches steady state,the protein importance score is obtained.In order to demonstrate superiority of BRWR,we conduct experiments on the YHQ,BioGRID,Krogan and Gavin PPI networks.The results show that the method BRWR is superior to other state-of-the-art methods in essential proteins recognition performance.Especially,compared with the contrast methods,the improvements of BRWR in terms of the ACC results range in 1.4%–5.7%,1.3%–11.9%,2.4%–8.8%,and 0.8%–14.2%,respectively.Therefore,BRWR is effective and reasonable.展开更多
Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have b...Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.展开更多
Tuberculosis(TB)disease has become one of the major public health concerns globally,especially in developing countries.Numerous research studies have already been carried out for TB,but we are still struggling for a c...Tuberculosis(TB)disease has become one of the major public health concerns globally,especially in developing countries.Numerous research studies have already been carried out for TB,but we are still struggling for a complete and quick cure for it.The progress of Mycobacterium tuberculosis(MTB)strains resistant to existing drugs makes its cure and control very complicated.Therefore,it is the need of the hour to search for newer and effective drugs that can inhibit an increasing number of putative drug targets.We applied the drug repurposing concept to identify promising FDAapproved drugs against five key-regulatory genes(FurB,IdeR,KstR,MosR,and RegX3)of the MTB.The FDA drugs were virtually screened using a structure-based approach by GOLD versions 5.2,and subjected to rigid docking followed by an induced-fit docking algorithm to enhance the accuracy and prioritize drugs for repurposing.We found 11 candidate drugs(including ZINC03871613,ZINC03871614,ZINC03871615 as top scorer candidate drugs)that were frequently present within the top 20 GoldScore ranks and showed promising results.Furthermore,molecular dynamics simulation was performed to monitor the effect of the top scorer drugs on the structural stability of all the five targets,indicating that inhibitors preferentially bind to the active site of the targets.This work suggests that these known FDA-approved drugs open new application domains in the form of anti-tuberculosis agents.展开更多
Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mec...Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mechanisms of TCM compounds.Therefore,network pharmacology and pharmacophore screening were adopted with molecular docking to identify the bioactive compounds possessing excellent oral bioavailability and drug-likeness.The method of pharmacophore screening was then employed to examine molecular interactions occurred between the compounds and targets.The gene-disease associations were collected from the DisGeNET database.The STRING database was utilized to cluster overlapping targets.The key targets were identified,and molecular docking with quercetin and kaempferol was performed against these targets to further characterize drug binding affinities,which verified strong binding affinities comparable with the known anticancer drugs.The multitarget inhibitor was identified and exerted a powerful inhibitory effect on tumor cells,as demonstrated by the CCK-8 assay.Quercetin and kaempferol components derived from TCM with good oral bioavailability and drug-likeness held promise for effective antitumor treatment,especially for tumors resistant to other treatment.展开更多
Protein–protein interaction(PPI)network analysis is an effective method to identify key proteins during plant development,especially in species for which basic molecular research is lacking,such as apple(Malus domest...Protein–protein interaction(PPI)network analysis is an effective method to identify key proteins during plant development,especially in species for which basic molecular research is lacking,such as apple(Malus domestica).Here,an MdPPI network containing 30806 PPIs was inferred in apple and its quality and reliability were rigorously verified.Subsequently,a rootgrowth subnetwork was extracted to screen for critical proteins in root growth.Because hormone-related proteins occupied the largest proportion of critical proteins,a hormonerelated sub-subnetwork was further extracted from the root-growth subnetwork.Among these proteins,auxin-related M.domestica TRANSPORT INHIBITOR RESISTANT 1(MdTIR1)served as the central,high-degree node,implying that this protein exerts essential roles in root growth.Furthermore,transgenic apple roots overexpressing an MdTIR1 transgene displayed increased primary root elongation.Expression analysis showed that MdTIR1 significantly upregulated auxin-responsive genes in apple roots,indicating that it mediates root growth in an auxin-dependent manner.Further experimental validation revealed that MdTIR1 interacted with and accelerated the degradation of MdIAA28,MdIAA43,andMdIAA46.Thus,MdTIR1-mediated degradation of MdIAAs is critical in auxin signal transduction and root growth regulation in apple.Moreover,our network analysis and high-degree node screening provide a novel research technique for more generally characterizing molecular mechanisms.展开更多
Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,g...Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.展开更多
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
基金supported by the Cultivation Project of Clinical Research of Shanghai Shenkang Hospital Development Center (No.SHDC12018X30)the Natural Science Foundation of Shanghai Science and Technology Commission (No.19ZR1452100 and 20ZR 1459300)the Key Program of Yueyang Hospital of Shanghai University of Traditional Chinese Medicine (No.2019YYZ01)。
文摘Huosu Yangwei(HSYW) Formula is a traditioanl Chinese herbal medicine that has been extensively used to treat chronic atrophic gastritis, precancerous lesions of gastric cancer and advanced gastric cancer. However, the effective compounds of HSYW and its related anti-tumor mechanisms are not completely understood. In the current study, 160 ingredients of HSYW were identified and 64 effective compounds were screened by the ADMET evaluation. Furthermore, 64 effective compounds and 2579 potential targets were mapped based on public databases. Animal experiments demonstrated that HSYW significantly inhibited tumor growth in vivo. Transcriptional profiles revealed that 81 mRNAs were differentially expressed in HSYW-treated N87-bearing Balb/c mice. Network pharmacology and PPI network showed that 12 core genes acted as potential markers to evaluate the curative effects of HSYW. Bioinformatics and qRT-PCR results suggested that HSYW might regulate the mRNA expression of DNAJB4, CALD,AKR1C1, CST1, CASP1, PREX1, SOCS3 and PRDM1 against tumor growth in N87-bearing Balb/c mice.
基金Project supported by the National Natural Science Foundation of China(No.11172158)
文摘Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.
基金funded by KAU Endowment(WAQF)at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.
基金supported by the National Natural Science Foundation of China,No.81870975(to SZ)。
文摘Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.
基金Department of Science and Technology of Yunnan Province,Provincial Basic Research Program(Kunkun-Medical Joint Special Project),202101AY070001-134Yunnan Provincial Department of Science and Technology,Yunnan Provincial Gerontology Research Center,202102AA310069Yunnan Provincial Department of Science and Technology-Kunming Medical University Basic Research Joint Special Key Project,202201AY070001-136.
文摘Lung cancer is a prevalent malignancy,and fatalities of the disease exceed 400,000 cases worldwide.Lung squamous cell carcinoma(LUSC)has been recognized as the most common pathological form of lung cancer.The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management.In this study,we aim to identify a panel of signature genes closely associated with LUSC,which can provide novel insights into the progression of LUSC.Gene expression profiles were retrieved from public resources including gene expression omnibus(GEO)and the cancer genome atlas(TCGA)database.Differentially expressed genes(DEGs)between LUSC specimens and normal lung tissues were identified by bioinformatics analyses.A total of 66 DEGs were identified based on two cohorts of data.CytoHubba plugin of Cytoscape software was utilized for the further analyses of the top 10 candidate hub genes including OGN,ABI3BP,MAMDC2,FGF7,FAM107A,SPARCL1,DCN,COL14A1,and MFAP4 and CHRDL1,which showed significant downregulation in LUSC.Two LUSC cell lines were used to validate the functions of CHRDL1 and FAM107A through overexpression experiment.Together,our data revealed novel candidate tumor-suppressor genes in LUSC,suggesting previously unappreciated mechanisms in the progression of LUSC.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11861045 and 62162040)。
文摘Predicting essential proteins is crucial for discovering the process of cellular organization and viability.We propose biased random walk with restart algorithm for essential proteins prediction,called BRWR.Firstly,the common process of practice walk often sets the probability of particles transferring to adjacent nodes to be equal,neglecting the influence of the similarity structure on the transition probability.To address this problem,we redefine a novel transition probability matrix by integrating the gene express similarity and subcellular location similarity.The particles can obtain biased transferring probabilities to perform random walk so as to further exploit biological properties embedded in the network structure.Secondly,we use gene ontology(GO)terms score and subcellular score to calculate the initial probability vector of the random walk with restart.Finally,when the biased random walk with restart process reaches steady state,the protein importance score is obtained.In order to demonstrate superiority of BRWR,we conduct experiments on the YHQ,BioGRID,Krogan and Gavin PPI networks.The results show that the method BRWR is superior to other state-of-the-art methods in essential proteins recognition performance.Especially,compared with the contrast methods,the improvements of BRWR in terms of the ACC results range in 1.4%–5.7%,1.3%–11.9%,2.4%–8.8%,and 0.8%–14.2%,respectively.Therefore,BRWR is effective and reasonable.
基金Project supported by the Gansu Province Industrial Support Plan (Grant No.2023CYZC-25)the Natural Science Foundation of Gansu Province (Grant No.23JRRA770)the National Natural Science Foundation of China (Grant No.62162040)。
文摘Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.
文摘Tuberculosis(TB)disease has become one of the major public health concerns globally,especially in developing countries.Numerous research studies have already been carried out for TB,but we are still struggling for a complete and quick cure for it.The progress of Mycobacterium tuberculosis(MTB)strains resistant to existing drugs makes its cure and control very complicated.Therefore,it is the need of the hour to search for newer and effective drugs that can inhibit an increasing number of putative drug targets.We applied the drug repurposing concept to identify promising FDAapproved drugs against five key-regulatory genes(FurB,IdeR,KstR,MosR,and RegX3)of the MTB.The FDA drugs were virtually screened using a structure-based approach by GOLD versions 5.2,and subjected to rigid docking followed by an induced-fit docking algorithm to enhance the accuracy and prioritize drugs for repurposing.We found 11 candidate drugs(including ZINC03871613,ZINC03871614,ZINC03871615 as top scorer candidate drugs)that were frequently present within the top 20 GoldScore ranks and showed promising results.Furthermore,molecular dynamics simulation was performed to monitor the effect of the top scorer drugs on the structural stability of all the five targets,indicating that inhibitors preferentially bind to the active site of the targets.This work suggests that these known FDA-approved drugs open new application domains in the form of anti-tuberculosis agents.
文摘Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mechanisms of TCM compounds.Therefore,network pharmacology and pharmacophore screening were adopted with molecular docking to identify the bioactive compounds possessing excellent oral bioavailability and drug-likeness.The method of pharmacophore screening was then employed to examine molecular interactions occurred between the compounds and targets.The gene-disease associations were collected from the DisGeNET database.The STRING database was utilized to cluster overlapping targets.The key targets were identified,and molecular docking with quercetin and kaempferol was performed against these targets to further characterize drug binding affinities,which verified strong binding affinities comparable with the known anticancer drugs.The multitarget inhibitor was identified and exerted a powerful inhibitory effect on tumor cells,as demonstrated by the CCK-8 assay.Quercetin and kaempferol components derived from TCM with good oral bioavailability and drug-likeness held promise for effective antitumor treatment,especially for tumors resistant to other treatment.
基金supported by the National Natural Science Foundation of China(31972357,31901574,and 31772254)the National Key R&D Program of China(2019YFD1000104)。
文摘Protein–protein interaction(PPI)network analysis is an effective method to identify key proteins during plant development,especially in species for which basic molecular research is lacking,such as apple(Malus domestica).Here,an MdPPI network containing 30806 PPIs was inferred in apple and its quality and reliability were rigorously verified.Subsequently,a rootgrowth subnetwork was extracted to screen for critical proteins in root growth.Because hormone-related proteins occupied the largest proportion of critical proteins,a hormonerelated sub-subnetwork was further extracted from the root-growth subnetwork.Among these proteins,auxin-related M.domestica TRANSPORT INHIBITOR RESISTANT 1(MdTIR1)served as the central,high-degree node,implying that this protein exerts essential roles in root growth.Furthermore,transgenic apple roots overexpressing an MdTIR1 transgene displayed increased primary root elongation.Expression analysis showed that MdTIR1 significantly upregulated auxin-responsive genes in apple roots,indicating that it mediates root growth in an auxin-dependent manner.Further experimental validation revealed that MdTIR1 interacted with and accelerated the degradation of MdIAA28,MdIAA43,andMdIAA46.Thus,MdTIR1-mediated degradation of MdIAAs is critical in auxin signal transduction and root growth regulation in apple.Moreover,our network analysis and high-degree node screening provide a novel research technique for more generally characterizing molecular mechanisms.
基金supported by the Shenzhen KQTD Project(No.KQTD20200820113106007)China Scholarship Council(No.201906725017)+2 种基金the Collaborative Education Project of Industry-University cooperation of the Chinese Ministry of Education(No.201902098015)the Teaching Reform Project of Hunan Normal University(No.82)the National Undergraduate Training Program for Innovation(No.202110542004).
文摘Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.
