Objective To construct green fluorescent protein (GFP)-labeled pSELECT-GFP zeohBMP2 eukaryotic expression vector.Methods The encoding fragment of hBMP2 gene was obtained from a recombinant plasmid pcDNA3.1/CT-hBMP2 by...Objective To construct green fluorescent protein (GFP)-labeled pSELECT-GFP zeohBMP2 eukaryotic expression vector.Methods The encoding fragment of hBMP2 gene was obtained from a recombinant plasmid pcDNA3.1/CT-hBMP2 by using polymerase展开更多
N-succinimidyl 4-[18F](fluoromethyl) benzoate for protein labeling was prepared (57%, EOB) in about 30min. Reaction conditions of S18FMB with IgG including pH of solutions, protein concentration, reaction temperature ...N-succinimidyl 4-[18F](fluoromethyl) benzoate for protein labeling was prepared (57%, EOB) in about 30min. Reaction conditions of S18FMB with IgG including pH of solutions, protein concentration, reaction temperature and time were studied. The optimal labeling conditions were: 0.2mg/mL IgG, pH = 7.8-8.5, 25℃, and reaction time 5min.Under these conditions the yield was about 80%. The 18F-labeled protein was purified by size exclusion chromatography.展开更多
Specific bioconjugation for native primary amines is highly valuable for both chemistry and biomedical research.Despite all the efforts,scientists lack a proper strategy to achieve high selectivity for primary amines,...Specific bioconjugation for native primary amines is highly valuable for both chemistry and biomedical research.Despite all the efforts,scientists lack a proper strategy to achieve high selectivity for primary amines,not to mention the requirement of fast response in real applications.Herein,we report a chromone-based aggregation-induced emission(AIE)fluorogen called CMVMN as a self-reporting bioconjugation reagent for selective primary amine identification,and its applications for monitoring bioprocesses of amination and protein labeling.CMVMN is AIE-active and capable of solid-state sensing.Thus,its electrospun films are manufactured for visualization of amine diffusion and leakage process.CMVMN also shows good biocompatibility and potential mitochondria-staining ability,which provides new insight for organelle-staining probe design.Combined with its facile synthesis and good reversibility,CMVMN would not only show wide potential applications in biology,but also offer new possibilities for molecular engineering.展开更多
The study of the neuron has always been a fundamental aspect when it came to studying mental illnesses such as autism and depression. The protein protocadherin-9 (PCDH9) is an important transmembrane protein in the de...The study of the neuron has always been a fundamental aspect when it came to studying mental illnesses such as autism and depression. The protein protocadherin-9 (PCDH9) is an important transmembrane protein in the development of the neuron synapse. Hence, research on its protein interactome is key to understanding its functionality and specific properties. A newly discovered biotin ligase, TurboID, is a proximity labeler that is designed to be able to label and observe transmembrane proteins, something that previous methods struggled with. The TurboID method is verified in HEK293T cells and primary cultured mouse cortical neurons. Results have proven the validity of the TurboID method in observing PCDH9-interacting proteins.展开更多
Protein labeling by using a protein tag and tag-specific fluorescent probes is increasingly becoming a useful technique for the real-time imaging of proteins in living cells. SNAP-tag as one of the most prominent fusi...Protein labeling by using a protein tag and tag-specific fluorescent probes is increasingly becoming a useful technique for the real-time imaging of proteins in living cells. SNAP-tag as one of the most prominent fusion tags has been widely used and already commercially available. Recently, various fluorogenic probes for SNAP-tag based protein labeling were reported. Owing to turn-on fluorescence response, fluorogenic probes for SNAP-tag minimize the fluorescence background caused by unreacted or nonspecifically bound probes and allow for direct imaging in living cells without wash-out steps. Thus,real-time analysis of protein localization, dynamics and interactions has been made possible by SNAP-tag fluorogenic probes. In this review,we describe the design strategies of fluorogenic probes for SNAP-tag and their applications in cellular protein labeling.展开更多
We describe for the first time the synthesis and the optimal conditions for protein labeling with a new fluorescent probe,5-chlorosulfoyl-2-thenoyltrifluoroacetone(CTTA),whicb forms a highly fluorescent conplex with E...We describe for the first time the synthesis and the optimal conditions for protein labeling with a new fluorescent probe,5-chlorosulfoyl-2-thenoyltrifluoroacetone(CTTA),whicb forms a highly fluorescent conplex with Eu^(3+) when conjugated to protein.The labeled proteins were characterized by absorbance and fluorescence measurements and the effect of labeling on the biological activity of sone proteins was also studied.It is shown that the new label is suitable for applications in time-resolved fluoroimmunoassay.展开更多
As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of sing...As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.展开更多
酶功能的识别对理解生命活动的机制、推进生命科学的发展有重要作用。然而现有的酶EC编号预测方法,并未充分利用蛋白质序列信息,在识别精度上仍有所不足。针对上述问题,本研究提出一种基于层级特征和全局特征的EC编号预测网络(EC number...酶功能的识别对理解生命活动的机制、推进生命科学的发展有重要作用。然而现有的酶EC编号预测方法,并未充分利用蛋白质序列信息,在识别精度上仍有所不足。针对上述问题,本研究提出一种基于层级特征和全局特征的EC编号预测网络(EC number prediction network using hierarchical features and global features,ECPN-HFGF)。该方法首先通过残差网络提取蛋白质序列通用特征,并通过层级特征提取模块和全局特征提取模块进一步提取蛋白质序列的层级特征和全局特征,之后结合两种特征信息的预测结果,采用多任务学习框架,实现酶EC编号的精确预测。计算实验结果表明,ECPN-HFGF方法在蛋白质序列EC编号预测任务上性能最佳,宏观F1值和微观F1值分别达到95.5%和99.0%。ECPN-HFGF方法能有效结合蛋白质序列的层级特征和全局特征,快速准确预测蛋白质序列EC编号,比当前常用方法预测精确度更高,能够为酶学研究和酶工程应用的发展提供一种高效的思路和方法。展开更多
<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span>...<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>展开更多
文摘Objective To construct green fluorescent protein (GFP)-labeled pSELECT-GFP zeohBMP2 eukaryotic expression vector.Methods The encoding fragment of hBMP2 gene was obtained from a recombinant plasmid pcDNA3.1/CT-hBMP2 by using polymerase
文摘N-succinimidyl 4-[18F](fluoromethyl) benzoate for protein labeling was prepared (57%, EOB) in about 30min. Reaction conditions of S18FMB with IgG including pH of solutions, protein concentration, reaction temperature and time were studied. The optimal labeling conditions were: 0.2mg/mL IgG, pH = 7.8-8.5, 25℃, and reaction time 5min.Under these conditions the yield was about 80%. The 18F-labeled protein was purified by size exclusion chromatography.
基金National Natural Science Foundation of China,Grant/Award Number:21788102Research Grants Council of Hong Kong,Grant/Award Numbers:16307020,16306620,16305518,N_HKUST609/19,C6009-17G,C6014-20w+1 种基金Innovation and Technology Commission,Grant/Award Numbers:ITC-CNERC14SC01,ITCPD/17-9Natural Science Foundation of Guangdong Province,Grant/Award Number:201913121205002。
文摘Specific bioconjugation for native primary amines is highly valuable for both chemistry and biomedical research.Despite all the efforts,scientists lack a proper strategy to achieve high selectivity for primary amines,not to mention the requirement of fast response in real applications.Herein,we report a chromone-based aggregation-induced emission(AIE)fluorogen called CMVMN as a self-reporting bioconjugation reagent for selective primary amine identification,and its applications for monitoring bioprocesses of amination and protein labeling.CMVMN is AIE-active and capable of solid-state sensing.Thus,its electrospun films are manufactured for visualization of amine diffusion and leakage process.CMVMN also shows good biocompatibility and potential mitochondria-staining ability,which provides new insight for organelle-staining probe design.Combined with its facile synthesis and good reversibility,CMVMN would not only show wide potential applications in biology,but also offer new possibilities for molecular engineering.
文摘The study of the neuron has always been a fundamental aspect when it came to studying mental illnesses such as autism and depression. The protein protocadherin-9 (PCDH9) is an important transmembrane protein in the development of the neuron synapse. Hence, research on its protein interactome is key to understanding its functionality and specific properties. A newly discovered biotin ligase, TurboID, is a proximity labeler that is designed to be able to label and observe transmembrane proteins, something that previous methods struggled with. The TurboID method is verified in HEK293T cells and primary cultured mouse cortical neurons. Results have proven the validity of the TurboID method in observing PCDH9-interacting proteins.
基金supports from the National Natural Science Foundation of China (Nos. 21422606 and 21502189)Dalian Cultivation Fund for Distinguished Young Scholars (Nos. 2014J11JH130 and 2015J12JH205)
文摘Protein labeling by using a protein tag and tag-specific fluorescent probes is increasingly becoming a useful technique for the real-time imaging of proteins in living cells. SNAP-tag as one of the most prominent fusion tags has been widely used and already commercially available. Recently, various fluorogenic probes for SNAP-tag based protein labeling were reported. Owing to turn-on fluorescence response, fluorogenic probes for SNAP-tag minimize the fluorescence background caused by unreacted or nonspecifically bound probes and allow for direct imaging in living cells without wash-out steps. Thus,real-time analysis of protein localization, dynamics and interactions has been made possible by SNAP-tag fluorogenic probes. In this review,we describe the design strategies of fluorogenic probes for SNAP-tag and their applications in cellular protein labeling.
基金Supported by National Natural Science Foundation of China.
文摘We describe for the first time the synthesis and the optimal conditions for protein labeling with a new fluorescent probe,5-chlorosulfoyl-2-thenoyltrifluoroacetone(CTTA),whicb forms a highly fluorescent conplex with Eu^(3+) when conjugated to protein.The labeled proteins were characterized by absorbance and fluorescence measurements and the effect of labeling on the biological activity of sone proteins was also studied.It is shown that the new label is suitable for applications in time-resolved fluoroimmunoassay.
文摘As one of the essential topics in proteomics and molecular biology, protein subcellular localization has been extensively studied in previous decades. However, most of the methods are limited to the prediction of single-location proteins. In many studies, multi-location proteins are either not considered or assumed not existing. This paper proposes a novel multi-label subcellular-localization predictor based on the semantic similarity between Gene Ontology (GO) terms. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the homologous accession numbers of the protein are used as keys to search against the gene ontology annotation database to obtain a set of GO terms. The semantic similarity between GO terms is used to formulate semantic similarity vectors for classification. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO semantic similarity vectors. Experimental results show that the proposed multi-label predictor significantly outperforms the state-of-the-art predictors such as iLoc-Plant and Plant-mPLoc.
文摘酶功能的识别对理解生命活动的机制、推进生命科学的发展有重要作用。然而现有的酶EC编号预测方法,并未充分利用蛋白质序列信息,在识别精度上仍有所不足。针对上述问题,本研究提出一种基于层级特征和全局特征的EC编号预测网络(EC number prediction network using hierarchical features and global features,ECPN-HFGF)。该方法首先通过残差网络提取蛋白质序列通用特征,并通过层级特征提取模块和全局特征提取模块进一步提取蛋白质序列的层级特征和全局特征,之后结合两种特征信息的预测结果,采用多任务学习框架,实现酶EC编号的精确预测。计算实验结果表明,ECPN-HFGF方法在蛋白质序列EC编号预测任务上性能最佳,宏观F1值和微观F1值分别达到95.5%和99.0%。ECPN-HFGF方法能有效结合蛋白质序列的层级特征和全局特征,快速准确预测蛋白质序列EC编号,比当前常用方法预测精确度更高,能够为酶学研究和酶工程应用的发展提供一种高效的思路和方法。
文摘<p class="MsoNormal"> <span lang="EN-US" style="" color:black;"="">The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, </span><span "="" style="font-variant-ligatures:normal;font-variant-caps:normal;orphans:2;text-align:start;widows:2;-webkit-text-stroke-width:0px;text-decoration-style:initial;text-decoration-color:initial;word-spacing:0px;">COVID-19, and H1N1, has been endangering the life of human beings all around the world. In order to really understand the biological process within a cell level and provide useful clues to develop antiviral drugs, information of virus protein subcellular localization is vitally important. In view of this, a CNN based virus protein subcellular localization predictor called “pLoc_Deep-mVirus” was developed. The predictor is particularly useful in dealing with the multi-sites systems in which some proteins may simultaneously occur in two or more different organelles that are the current focus of pharmaceutical industry. The global absolute true rate achieved by the new predictor is over 97% and its local accuracy is over 98%. Both are transcending other existing state-of-the-art predictors significantly. It has not escaped our notice that the deep-learning treatment can be used to deal with many other biological systems as well. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at <a href="http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/">http://www.jci-bioinfo.cn/pLoc_Deep-mVirus/</a>.</span> </p>
