MIMLRBF预测谷物蛋白质功能方法的改进

随着人们对营养与保健功能的关注,谷物蛋白质功能预测已经成为当前研究热点.面对大量已完成测序的谷物蛋白质基因组数据,利用计算方法来预测谷物蛋白质功能已经成为主流.从谷物蛋白质结构域序列出发,首次将MIMLRBF算法运用到蛋白质功能预测,并在此算法基础上,提出了多种改进后的谷物蛋白质功能预测模型.其中,针对平均Hausdo...     

基于图聚类的蛋白质相互作用网络功能模块探测

作者采用了基于模块性的图聚类算法来探测蛋白质相互作用网络中的集团,在具有2 617个节点11855个相互作用的酵母蛋白质相互作用网络中探测出177个集团,同时采用慕尼黑信息中心(Munich Information Center,MIPS)的层次功能注释对其进行了注释,并且验证得到的集团的确是内部连接紧密的子图。     

谷物中淀粉-蛋白质相互作用研究进展

作为谷物中含量最丰富的两种生物大分子,淀粉与蛋白质之间的相互作用对谷物制品的品质与营养等具有重要影响。文中概述了淀粉与蛋白质相互作用的类型及影响因素,以及谷物中淀粉与蛋白质相互作用对淀粉糊化、消化、流变学特性的影响,最后综述了相互作用的研究方法,主要包括光谱学方法和结构解析技术。通过探索淀粉-蛋白质的相互作用规律以期提高人们对谷物多组分结构和性质的认识,从而为谷物深加工提供一定的理论依据,促进新型谷物制品的研发。     

基于Halcon多层感知机的织物色差检测研究

探讨基于Halcon中多层感知机织物色差检测研究。首先将数据库中部分样本特征数据输入神经网络,然后采用均方误差最小化训练优化神经网络,构建样本特征与色差类别的多层感知机映射函数;最后将面积更大、上色情况更复杂的织物输入多层感知机,通过形态学算法分割出织物中不同类别的色差区域,并统计三种颜色织物的色差检测平均准确率,最低可达到89.7%。认为:织物图像中的不同色差区域均能准确判别并分割出来,能满足工业色差检测的准确率与灵敏度要求。     

谷物蛋白的组成特点及分离方法研究现状

谷物是优质蛋白和生物活性物质的来源, 其水解产物可以作为植物蛋白提供体和有益活性成分替代物。谷物蛋白在蛋白领域一直是国内外学者的研究热点及重点, 尤其是小麦、水稻、玉米等大宗谷物提供了大量优质的植物蛋白, 其他广泛的小宗谷物品种也提供了优质并具有不同功能特性的蛋白质。本文对几种常见的谷物蛋白的分级及分离方法、常见谷物蛋白的结构与功能特性、谷物蛋白的使用价值及开发前景进行了综合阐述, 以期为谷类蛋白质的进一步研究提供参考。     

大豆蛋白质-植物多酚互作的研究进展

大豆蛋白质与植物多酚是食品基质中两种重要的组成成分,常因其相互作用而被广泛研究。大豆蛋白质与植物多酚的相互作用能够影响两者的结构、功能特性以及生物利用度。本文总结了近几年来植物多酚与大豆蛋白质的相互作用对大豆蛋白的结构、功能特性和生物利用度的研究进展,同时着重总结了相互作用对多酚的影响,以期为大豆蛋白质和植物多酚的高值化利用、产品开发以及在食品领域的应用提供理论基础。     

最优加权融合SO_2转化率预测模型

以冶炼烟气制酸SO2转化率为研究对象,针对单一BP或RBF神经网络,预测SO2转化率存在的过学习或网络速度收敛慢的问题,利用最优均方误差加权融合算法对两种单一神经网络进行融合,从而构建更优的SO2转化率预测模型.仿真结果表明:最优均方误差加权融合模型避免了单一模型信息的缺失,实现了信息互补,从而提高了SO2转化率预测精度.     

基于联合残基力场的蛋白质结构预测的研究进展

讨论了作为后基因组时代的重大课题的蛋白质结构预测的简单分类和基于能量预测方法的难点,系统论述了在进行蛋白质结构预测中的一种模型即联合残基力场的形式和参数的确定方法以及相关项的作用,讨论了在力场中势能函数全局能量最优化方法即构象空间退火算法的基本原理,分析了利用联合残基力场和构象空间退火算法预测蛋白质结构的例子,预测了对联合残基力场模型今后的研究方向和预期研究结果.     

De novo测序技术在啤酒易感乳杆菌全基因组研究中的应用

本文应用De novo测序技术对一种未经测序的啤酒易感乳杆菌进行全基因组研究,包括全基因组遗传信息的首次获取以及基因功能的生物信息学分析与注释。通过对1株从啤酒中分离获得的乳杆菌基因组DNA进行提取与纯化,构建了符合质量要求的测序文库,并进行了De novo测序;通过对De novo测序数据进行筛选与质量评价,进一步对筛选后的数据进行了De novo组装与结果评价,最后获得乳杆菌全基因组序列;在获得全基因组序列的基础上,对全基因组序列进行基因预测,并对预测基因进行GO基因功能注释、COG基因功能注释与KEGG生物通路注释,获取了该乳杆菌的基因序列与基因功能信息。该乳杆菌全基因组测序数据与组装结果良好,获得的序列中共有1,824个预测基因,其中分别有545、1,303、120条预测基因有相应的GO、COG、KEGG注释。本研究为该啤酒易感乳杆菌功能基因的研究与生物信息学分析提供了基础数据和技术参考。     

肺炎克雷伯菌CRP蛋白表达与纯化及生物学特征分析

目的:原核表达获得肺炎克雷伯菌KP1_RS23625基因（crp）编码的CRP蛋白,并解析CRP蛋白具体的生物学功能。方法:首先克隆肺炎克雷伯菌crp基因,并亚克隆至pET-28a（+）质粒构建重组蛋白表达载体;然后体外原核诱导表达、纯化目的蛋白并进行SDS-PAGE电泳分析。进一步通过生物学信息方法分析CRP蛋白的生物学功能:利用ProtParam、Protscale、SignalP4.1 Service、TMHMM Server v.2.0、SOPMA、SWISS-MODEL软件分别分析目的蛋白理化性质、疏水性、信号肽、跨膜区域、二级与三级结构等一般生物学特征;并借助CDD数据库、Net Phos 3.1 Sever在线软件预测蛋白的功能结构域、磷酸化位点等功能特征;进一步利用ATRING 11.0交互式数据库进行蛋白质交联互作分析。结果:成功构建了重组目的蛋白表达载体pET-28a-crp,并通过原核诱导表达与镍柱亲和吸附法表达、纯化获得CRP目的蛋白;蛋白电泳结果显示CRP蛋白为水溶性蛋白,包括标签蛋白（约4 kDa）的重组目的蛋白分子质量约27 kDa。对CRP蛋白的生物信息学分析结果显示:CRP蛋白大小23.65 kDa、为亲水性蛋白;无跨膜结构域、无信号肽;蛋白二级结构主要由α螺旋与不规则卷曲构成,其中α-螺旋占比40%以上,结构较松散;同时成功构建了三级结构模型;CRP蛋白结构域分析表明含有1个功能结构域,属PRK11753超级家族;修饰位点及蛋白注释分析显示目的蛋白含20个磷酸化位点,与多个蛋白具有交互作用。结论:本研究利用基因工程方法成功获得了较高纯度肺炎克雷伯菌CRP蛋白,并通过生物信息学手段解析了其生物学特征,为肺炎克雷伯菌感染的临床治疗提供理论基础。     

Identification of Bioactive Peptides from Cereal Storage Proteins and Their Potential Role in Prevention of Chronic Diseases

Cereal grains, such as wheat, barley, rice, rye, oat, millet, sorghum, and corn, have been staples in human diets since ancient times. At present, there is a significant body of scientific evidence showing the health benefits of consuming whole grains in chronic disease prevention, particularly in regards to diabetes, cardiovascular disease, and cancer. The objective was to determine bioactive peptides in cereal grains that may prevent cardiovascular disease, cancer, inflammation, and diabetes. Bioactive peptides that may be obtained from cereal grains, particularly wheat, oat, barley, and rice, were identified. Bioactive peptides that play a role in chronic disease prevention have been found primarily in legumes and dairy products; although research connecting cereal grains with potential bioactive peptide activity is limited. In this review, 4 cereal grains, wheat, oat, barley, and rice, were evaluated for bioactive peptide potential using the BIOPEP database. In addition, research information was compiled for each grain regarding evidence about the effect of their proteins in prevention of chronic diseases. All 4 grains showed high occurrence frequencies of angiotensin‐converting enzyme‐inhibitor peptides (A = 0.239 to 0.511), as well as of dipeptidyl peptidase‐inhibitor and antithrombotic, antioxidant, hypotensive, and opioid activity. Wheat and rice proteins had anticancer sequences present. Wheat and barley showed the greatest diversity and abundance of potential biological activity among the cereal proteins. Further research needs to be conducted to learn how these biologically active peptide sequences are released from cereal grains. This study supports the notion that cereal grains are a nutritious part of a healthy diet by preventing chronic diseases.     

A Deep Convolutional Neural Network Architecture for Boosting Image Discrimination Accuracy of Rice Species

In this paper, we proposed a machine vision system based on deep convolutional neural network (DCNN) architecture for improving the accuracy of classifying three distinct groups of rice kernel images compared with the traditional approaches. The main advantage of the presented method was able to avoid many heuristics as well as manual labor to tune complex parameters according to the domain to reach a modest level of accuracy in the classical feature extraction algorithms. We trained our models using stochastic gradient descent with momentum of 0.9 and weight decay of 0.0005 to optimize the network parameters and minimize the back-propagation error on the training dataset. We used a batch size between 15 and 150 and epochs time configured between 10 and 25. The experiment results showed that the highest accuracy of 99.4% obtained in the training process with batch size of 15 and epoch time of 20. We also compared the DCNN method with the traditional hand-engineered approaches of PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM for rice kernel classification. The results showed that DCNN routinely outperforms other methods in similar machine vision tasks. The prediction accuracy results for test datasets by PHOG-KNN, PHOG-SVM, GIST-KNN, and GIST-SVM models were 89.1, 76.9, 90.6, and 92.1%, respectively. The highest prediction accuracy of DCNN is 95.5%, which showed the effectiveness of our proposed method for rice kernel classification. The aim of this study is to set up an automatic and accurate intelligent detection system and offering much value to current rice processing industry. With the comparably high classification accuracy, developed neural network could be used as a tool to achieve better and more objective rice quality evaluation at trading points within the rice marketing system.     

Isolation and mapping of self-assembling protein domains encoded by the Saccharomyces cerevisiae genome using lambda repressor fusions

Understanding how proteins are able to form stable complexes is of fundamental interest from the perspective of protein structure and function. Here we show that lambda repressor fusions can be used to identify and characterize homotypic interaction domains encoded by the genome of Saccharomyces cerevisiae, using a selection for polypeptides that can drive the assembly of the DNA binding domain of bacteriophage lambda repressor. Three high complexity libraries were constructed by cloning random fragments of S. cerevisiae DNA as lambda repressor fusions. Repressor fusions encoding homotypic interactions were recovered, identifying oligomerization units in 35 yeast proteins. Seventeen of these interaction domains have not been previously reported, while the other 18 represent homotypic interactions that have been characterized at varying levels of detail. The novel interactions include several predicted coiled-coils as well as domains of unknown structure. With the availability of genomic sequences it should be possible to apply this approach, which provides information about protein-protein interactions that is complementary to that obtained from yeast two-hybrid screens, on a genome-wide scale in yeast or other organisms where large-scale protein-protein interaction data is not available.     

电子鼻结合统计学分析对牛肉中猪肉掺假的识别

利用电子鼻结合统计学分析对掺入猪肉的掺假牛肉进行定性和定量研究。采用平均值法和K均值聚类分析法提取特征值;通过主成分分析、判别分析进行分析并使用多层感知神经网络进行模式识别;通过偏最小二乘、多元线性回归和BP神经网络建立定量模型来预测掺假物含量。结果表明：K均值聚类分析法提取的特征值能更全面地反映电子鼻的响应信号,同时判别分析能更好地对掺假牛肉进行定性检测。多层感知神经网络分析中训练集正确分类率达98.8%,验证集正确分类率达97.4%,说明分类结果较好。BP神经网络的决定系数R2（0.9993、0.9930）和均方根误差（0.90%、2.50%）明显优于其他两种方法,故BP神经网络建模分析能更好地预测掺假牛肉中猪肉的含量。说明应用电子鼻技术检测掺入猪肉的掺假牛肉具有一定的可行性。     

Intelligent Estimation of the Canola Oil Stability Using Artificial Neural Networks

In the present study, a multi-layer perceptron neural network and radial basis function (RBF) network were used to estimate the oxidative stability of canola oil during storage. Artificial neural networks (ANNs) were used to model oxidative stability of canola oil during storage, and comparison was also made with the results obtained from a regression analysis. The oxidative stability of canola oils was considered as dependent variable, and independent variables were selected as time (in week), variety, C14:0, C16:0, C18:0, C20:0, C18:1, C18:2, C18:3, and C22:1 fatty acid content. The results were compared with experimental data and it was found that the estimated oxidative stability by RBF neural network is more accurate than multi-layer perceptron network and regression model. It was also found that the oxidative stability of canola oil decreased with increase in storage time and C18:3 fatty acid content.     

基于iTRAQ技术分析牛初乳与常乳乳清差异蛋白质组

为阐明牛初乳、牛常乳乳清蛋白的差异,利用同位素标记相对和绝对定量(isobaric tags for relative and absolute quantitation,iTRAQ)蛋白质组学技术对二者进行蛋白质组差异分析,在得到的599 种具有定量信息的乳清蛋白中,鉴定出60 种差异蛋白.将牛初乳与牛常乳乳清丰度差...     

基于近红外光谱技术的小米产地溯源研究

采用便携式近红外光谱仪结合主成分分析(PCA)、费舍尔线性判别(FLDA)及多层感知器神经网络(MLPNN)模型,探讨近红外光谱技术应用于小米产地溯源的可行性。PCA分析显示,除山西、河南、黑龙江3省的样品差异较小难以区分外,其余8个省份的样品均能清晰区分产地。FLDA和MLP-NN分析均能识别出小米样品产地,但MLP-NN识别效果优于FLDA,两个模型对预测集的识别正确率分别为92.3%,84.6%。以上结果表明,近红外光谱技术可有效应用于小米的产地溯源。     

Disulphide interchange in cereal proteins

A method of measuring the extent of disulphide interchange between a model compound, cystamine, and cereal flour proteins has been applied to wheat, steamed wheat, rye, barley and maize. The results indicate that, if the extent of protein-protein S.S interchange is of the same order as that found in the model reaction, it is several times greater in wheat than the calculated minimum necessary to create a continuous protein network. In the case of rye the extent seems to be sufficient to just comply with this minimum condition. Steamed wheat, barley and maize (none of which can form doughs) fall below the limiting extent of interchange required. It has been assumed that insoluble proteins are precluded from taking part in protein-protein S.S interchange reactions, and evidence from the literature has been cited to support this.