目的对1株在九江地区分离的柯萨奇A6病毒(CoxA6)VP1区域进行克隆,并对其编码蛋白的结构、功能及B细胞表位进行分析和预测,为CoxA6疫苗制备和诊断方法的研究提供理论基础。方法采用RT-PCR法对CoxA6分离株VP1区进行扩增和克隆、序列分析,...目的对1株在九江地区分离的柯萨奇A6病毒(CoxA6)VP1区域进行克隆,并对其编码蛋白的结构、功能及B细胞表位进行分析和预测,为CoxA6疫苗制备和诊断方法的研究提供理论基础。方法采用RT-PCR法对CoxA6分离株VP1区进行扩增和克隆、序列分析,应用SigaIP、TMPRED、TMHMM、Big-PI Predictor、Cell-Ploc、PSORT、NetNES、Netphos、SOPMA、NetNGlyc、MotifScan、InterProscan、SMART、PROSITE、GOR4、Bepipred Linear Epitope Prediction等生物信息学方法预测其VP1基因编码蛋白特性和潜在的B细胞表位。结果该CoxA6分离株VP1基因编码305aa的多肽,分子量为33.5kDa。该蛋白无信号肽、跨膜区,是亲水性蛋白;二级结构主要以无规则卷曲为主,其次为α-螺旋及β-片层。该蛋白共有7个可能的B细胞抗原表位,位于151~173aa区域内的表位分值最高为2.774。结论成功克隆了1株CoxA6的VP1基因,并对其进行序列分析、蛋白质结构和B细胞表位预测,为制备CoxA6疫苗和开发诊断方法提供了分子生物学基础。展开更多
The phase behavior of a semirigid polymer manifested by the polypeptide such as poly(βphenethyl Laspartate)(PPLA) in a mixed solvent system containing a denaturant acid was studied. The obtained results suggest that ...The phase behavior of a semirigid polymer manifested by the polypeptide such as poly(βphenethyl Laspartate)(PPLA) in a mixed solvent system containing a denaturant acid was studied. The obtained results suggest that there is a marked enhancement of the propensity for the semirigid polymer chain to adopt a rigid conformation upon increasing polymer concentration before the critical value for the incipience of a liquid crystal phase, which has been firmly confirmed both theoretically and experimentally. In consideration of the first order estimation of polymerpolymer interaction, a theoretical approach was attempted within the framework of a helixcoil transition model proposed by Rajan and Woo. According to the analysis, the semirigid chains could become rather rigid before reaching liquid crystal phase. Such a kind of chain stiffening effect was verified by 1H NMR and 13 C NMR measurements. The experimental results show that the PPLA molecules convert gradually from the random coil to the rigid rod conformation as polymer concentration turns higher in the isotropic phase.展开更多
ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><s...ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><span style="font-size:12px;font-family:Verdana;">silico</span></i><span style="font-size:12px;font-family:Verdana;"> analysis, earlier we have identified 22 putative ERK interacting proteins namely;ephrin type-B receptor 2 isoform 2 precursor (EPHB2), mitogen-activated protein kinase 1</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">(MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), </span><span style="font-size:12px;font-family:Verdana;">tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4),</span><span style="font-size:12px;font-family:Verdana;"> kinase suppressor of Ras2 (KSR2), mitogen-activated protein kinase kinase 6 (MAP3K6), UL16 binding protein 2 (ULBP2), UL16 binding protein 1 (ULBP1), dual specificity phosphatase 14 (DUSP14), dual specificity phosphatase 6 (DUSP6), hyaluronan-mediated motility receptor (RHAMM), kinase D interacting substrate of 220</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T1"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 1</span></b></a></span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i></span><i><span style="font-size:10.0pt;font-family:;" "=""> </span><span style="font-size:12px;font-family:Verdana;" "="">silico</span></i><span "="" style="font-size:12px;font-family:Verdana;"> prediction of secondary structure of ERK interacting proteins was done by SOPMA and motif/domain identification using motif search. Briefly, SOPMA predicted higher random coil and alpha helix percentage in these proteins (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T2"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 2</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">)</span><span "="" style="font-size:12px;font-family:Verdana;"> and</span><span "="" style="font-size:12px;font-family:Verdana;"> motif scan predicted serine/threonine kinases active site signature and protein kinase ATP binding region in majority of ERK interacting proteins. Moreover, few have commonly dual specificity protein phosphatase family and tyrosine specific protein phosphatase domains (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T3"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 3</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.展开更多
Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate predi...Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.展开更多
文摘目的对1株在九江地区分离的柯萨奇A6病毒(CoxA6)VP1区域进行克隆,并对其编码蛋白的结构、功能及B细胞表位进行分析和预测,为CoxA6疫苗制备和诊断方法的研究提供理论基础。方法采用RT-PCR法对CoxA6分离株VP1区进行扩增和克隆、序列分析,应用SigaIP、TMPRED、TMHMM、Big-PI Predictor、Cell-Ploc、PSORT、NetNES、Netphos、SOPMA、NetNGlyc、MotifScan、InterProscan、SMART、PROSITE、GOR4、Bepipred Linear Epitope Prediction等生物信息学方法预测其VP1基因编码蛋白特性和潜在的B细胞表位。结果该CoxA6分离株VP1基因编码305aa的多肽,分子量为33.5kDa。该蛋白无信号肽、跨膜区,是亲水性蛋白;二级结构主要以无规则卷曲为主,其次为α-螺旋及β-片层。该蛋白共有7个可能的B细胞抗原表位,位于151~173aa区域内的表位分值最高为2.774。结论成功克隆了1株CoxA6的VP1基因,并对其进行序列分析、蛋白质结构和B细胞表位预测,为制备CoxA6疫苗和开发诊断方法提供了分子生物学基础。
基金Supported by the National Natural Science Foundation of China(Grant No.59803002)
文摘The phase behavior of a semirigid polymer manifested by the polypeptide such as poly(βphenethyl Laspartate)(PPLA) in a mixed solvent system containing a denaturant acid was studied. The obtained results suggest that there is a marked enhancement of the propensity for the semirigid polymer chain to adopt a rigid conformation upon increasing polymer concentration before the critical value for the incipience of a liquid crystal phase, which has been firmly confirmed both theoretically and experimentally. In consideration of the first order estimation of polymerpolymer interaction, a theoretical approach was attempted within the framework of a helixcoil transition model proposed by Rajan and Woo. According to the analysis, the semirigid chains could become rather rigid before reaching liquid crystal phase. Such a kind of chain stiffening effect was verified by 1H NMR and 13 C NMR measurements. The experimental results show that the PPLA molecules convert gradually from the random coil to the rigid rod conformation as polymer concentration turns higher in the isotropic phase.
文摘ERK is involved in multiple cell signaling pathways through its interacting proteins. By </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i> <i><span style="font-size:12px;font-family:Verdana;">silico</span></i><span style="font-size:12px;font-family:Verdana;"> analysis, earlier we have identified 22 putative ERK interacting proteins namely;ephrin type-B receptor 2 isoform 2 precursor (EPHB2), mitogen-activated protein kinase 1</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">(MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), </span><span style="font-size:12px;font-family:Verdana;">tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4),</span><span style="font-size:12px;font-family:Verdana;"> kinase suppressor of Ras2 (KSR2), mitogen-activated protein kinase kinase 6 (MAP3K6), UL16 binding protein 2 (ULBP2), UL16 binding protein 1 (ULBP1), dual specificity phosphatase 14 (DUSP14), dual specificity phosphatase 6 (DUSP6), hyaluronan-mediated motility receptor (RHAMM), kinase D interacting substrate of 220</span></span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1</span><span "="" style="font-size:10pt;"> </span><span "="" style="font-size:12px;font-family:Verdana;">(MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T1"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 1</span></b></a></span><span "="" style="font-size:10pt;"><span style="font-size:12px;font-family:Verdana;">). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, </span><i><span style="font-size:12px;font-family:Verdana;">in</span></i></span><i><span style="font-size:10.0pt;font-family:;" "=""> </span><span style="font-size:12px;font-family:Verdana;" "="">silico</span></i><span "="" style="font-size:12px;font-family:Verdana;"> prediction of secondary structure of ERK interacting proteins was done by SOPMA and motif/domain identification using motif search. Briefly, SOPMA predicted higher random coil and alpha helix percentage in these proteins (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T2"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 2</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">)</span><span "="" style="font-size:12px;font-family:Verdana;"> and</span><span "="" style="font-size:12px;font-family:Verdana;"> motif scan predicted serine/threonine kinases active site signature and protein kinase ATP binding region in majority of ERK interacting proteins. Moreover, few have commonly dual specificity protein phosphatase family and tyrosine specific protein phosphatase domains (</span><span "="" style="font-size:10pt;"><a href="file:///E:/%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2021/0225-wqs-%E5%B7%A5%E4%BD%9C%E8%AE%B0%E5%BD%95/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89(1)/2%E6%9C%88%20WJNS11.1%20%E6%8F%92%E9%A1%B5%E7%A0%81%20%E4%BB%98%E5%96%9C%E4%BB%81%20%EF%BC%887%EF%BC%89/7-1390595.docx#T3"><b><span color:#943634;"="" style="font-size: 12px;font-family: Verdana;">Table 3</span></b></a></span><span "="" style="font-size:12px;font-family:Verdana;">). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.
文摘Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.