Development of a water-in-oil-in-water multiple emulsion system integrating biomimetic aqueous-core lipid nanodroplets for protein entity stabilization. Part II: process and product characterization

The aqueous-core enclosed in lipid nanoballoons integrating multiple emulsions of the type water-in-oil-in-water mimic, at least in theory, the environment within viable cells, thus being suitable for housing hydrophilic protein entities such as bioactive proteins, peptides and bacteriophage particles. This study reports a complete physicochemical characterization of optimized biomimetic aqueous-core lipid nanoballoons housing hydrophilic (BSA) protein entities, evolved from a statistical 2³×3¹ factorial design study (three variables at two levels and one variable at three levels) that was the subject of the first paper of a series of three, aiming at complete stabilization of the three-dimensional structure of protein entities attempted via housing the said molecular entities within biomimetic aqueous-core lipid nanoballoons integrating a multiple (W/O/W) emulsion. The statistical factorial design followed led to the production of an optimum W/O/W multiple emulsion possessing quite homogeneous particles with an average hydrodynamic size of (186.2?±?2.6) nm and average Zeta potential of (?36.5?±?0.9) mV, and exhibiting a polydispersity index of 0.206?±?0.014. Additionally, the results obtained for the diffusion coefficient of the lipid nanoballoons integrating the optimized W/O/W multiple emulsion were comparable and of the same order of magnitude (10^?12 m² s^?1) as those published by other authors since, typically, diffusion coefficients for molecules range from 10^?10 to 10^?7 m² s^?1, but diffusion coefficients for nanoparticles are typically of the order of magnitude of 10^?12 m² s^?1.     

大豆分离蛋白改性的研究(二)

用海藻酸钠对大豆分离蛋白进行了改性,对一些环境因素如温度、ｐＨ值、离子浓度等对反应的影响作了研究。研究表明,海藻酸钠能显著提高大豆分离蛋白的粘度,在０．５％海藻酸钠和０．０１ｍｏｌ／Ｌ氯化钙作用下,大豆分离蛋白的粘度能提高１０００多倍。确定了海藻酸钠与大豆蛋白的最佳浓度比,其数学模型为ｙ＝７．３４１０－４．４０２１ｘ。     

Main-chain complementarity in protein-protein recognition

We present a statistical analysis of protein structures basedon interatomic C_a distances. The overall distance distributionsreflect in detail the contents of sequence-specific substructuresmaintained by local interactions (such as -helixes) and longerrange interactions (such as disulfide bridges and ß-sheets).We also show that a volume scaling of the distances makes distancedistributions for protein chains of different length superimposable.Distance distributions were also calculated specifically foramino acids separated by a given number of residues. Specificfeatures in these distributions are visible for sequence separationsof up to 20 amino acid residues. A simple representation, whichpreserves most of the information in the distance distributions,was obtained using six parameters only. The parameters giverise to canonical distance intervals and when predicting coarse-graineddistance constraints by methods such as data-driven artificialneural networks, these should preferably be selected from theseintervals. We discuss the use of the six parameters for determiningor reconstructing 3-D protein structures.     

以序列特征值预测酶和非酶蛋白及内含肽

利用生物信息学快速准确鉴别酶、非酶蛋白及内含肽能极大提高实验效率,而测序数量的指数型增长使酶、非酶蛋白及内含肽的自动分类尤显重要。本文获取了同一性小于25%的序列共计3853条,采用Z标度的伪氨基酸组成和氨基酸组成分布提取序列特征值识别酶、非酶蛋白及内含肽。结果表明,该特征值提取方法经参数优化后,即当λ=5,w=0.15时,以支持向量机为分类器,其10倍交叉验证的精度可达81.3%,ROC曲线下面积为0.83;其精度高于其它方法0.5%到12.9%不等;独立样本测试的预测精度可达71.2%,ROC曲线下面积为0.782,其精度高于其它方法0.4%到6.4%不等,效果均优于其它常见的序列特征值方法。本文结果说明从序列出发判断其归属是可行的,3种不同功能的分子在序列特征上存在一定的差异,所建立的Z标度的伪氨基酸组成和氨基酸组成分布法可用于其它类似的生物信息学问题。建立了从序列出发预测酶、非酶蛋白及内含肽的新方法。     

An integrated microfluidic system for fast, automatic detection of C-reactive protein

C-reactive protein (CRP) is a well-known inflammation marker in human beings. This study reports a new microfluidic system for fast, automatic detection of CRP. It contains pneumatic micropumps, a vortex-type micromixer, a pneumatic micro-injector and several microvalves to automatically perform the entire protocol for CRP detection. This includes sample/reagent transportation, incubation between the target CRP and a CRP-specific aptamer, washing processes, and the chemiluminescence development process. In addition, the chemiluminescence signal is measured by using a custom-made optical system which consists of a photomultiplier tube, a portable air compressor and eight electronic magnet valves to quantify the concentration of CRP. When compared to previous works, not only can this new microfluidic system automatically perform the entire process via a new integrated micro-injector and new micropumps, but a new CRP-specific DNA aptamer with a higher affinity and specificity is also used for CRP measurement. Experimental data show that the developed system can automatically complete the entire protocol within 30 min with a detection limit of 0.0125 mg/L, which is superior to previous published results. Moreover, this study also measures CRP concentration from clinical samples to verify the performance of the developed microfluidic system. The results indicate that the measured CRP concentrations from human serums are consistent with those using a benchtop system. The developed system can also detect CRP concentrations from human whole blood without any external sample pretreatment process. This microfluidic system may be promising for point-of-care applications for CRP detection in the future.     

Exploiting knowledge ontology and software agents for PPI network analysis

One major goal of functional genomics has been to identify and analyze molecular interactions in a cellular context to better understand the underlying design principles and mechanisms. To investigate into a PPI network from both topological and functional points of view, this work proposes a methodology that exploits ontology-based biological knowledge for network analysis. To speed up the procedure, an agent-based framework is also presented for supporting distributed computing. The preliminary results show that through the knowledge obtained from gene ontology, our work in analyzing building blocks of PPI networks can give a higher resolution than that of previous ones. Also our agent-based framework can successfully speed up the task of network analysis in an adaptive manner.     

基于模式驱动和模糊查询的模式发现方法

在蛋白质序列的比对研究中,拥有相似模式的蛋白质常常具有相似的功能.通过已知的蛋白质序列模式可以很方便地对新蛋白质序列的功能结构进行研究和确认.蛋白质序列的发现已成为一个很有意义的题目.对基于模式驱动Pratt算法进行改进以提高其效率,在原来基础上引入模糊查询方法,能够更为快捷地从互不相关的蛋白质序列集合中找出最具代表性的蛋白质模式.     

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.