Emulsions Based on the Interactions Between Lactoferrin and Chitosans

In this work, purification of lactoferrin from whey was performed with high recovery rate. Lactoferrin was then exploited in the preparation of food emulsions. Two tertiary emulsions, formed by olive oil, lecithin, chitosan, and lactoferrin, were compared: both the emulsions showed similar turbidity and stability. In the secondary emulsion formed by oil/lecithin/chitosan, the pH was increased to 9 before addition of lactoferrin. Then, lactoferrin was added, and the pH was stabilized above pH 9. Lactoferrin was found in amounts of 1 to 2.5 mg/ml in the multiple experiments. A fraction of the added lactoferrin was also present in a milky layer above the emulsion layer. This was, to our knowledge, the first study of emulsions made exploiting the interactions between lactoferrin and chitosan. It was noted that chitosan droplets remained soluble, although the hydrocolloid solubility occurs at pH lower than 5.9. These results showed the feasibility of manufacturing lactoferrin-based emulsions as functional foods.     

基于支持向量机的蛋白质相互作用识别

支持向量机（SVM）是广泛应用于各个领域的分类算法,包括生物信息学。本研究应用SVM作为蛋白质相互作用的分类算法,所用蛋白质相互作用数据下载于墨尼黑生物信息学中心的酿酒酵母数据集,包含有6736条蛋白质,其中相互作用的有4837对,不相互作用的有9674对。提取蛋白质主要结构的电荷和等电位点特征,并应用SVM分类算法对此进行了分类。结果显示,分类的正确率在60％左右,但是较系统发育谱法还是获得了较高的分类正确率。     

水稻生长模拟模型的组建与验证

在模型L1D和TIL的基础上,利用田间试验和文献资料,组建了水稻生长过程的动态模拟模型．验证结果显示,模型运行后生育期的模拟值与实测值吻合良好,干物重、叶面积系数和分蘖数的模拟值与实测值也基本一致,表明该模型能较好地模拟水稻生长过程的动态变化．     

Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks

The main finding of this paper is a novel avalanche-size exponent τ ≈ 1.87 when the generalised sandpile dynamics evolves on the real-world Japanese inter-firm network. The topology of this network is non-layered and directed, displaying the typical bow tie structure found in real-world directed networks, with cycles and triangles. We show that one can move from a strictly layered regular lattice to a more fluid structure of the inter-firm network in a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distribution for the interactions, forces the scaling exponent of the avalanche-size probability density function τ out of the two-dimensional directed sandpile universality class τ = 4/3, into the mean field universality class τ = 3/2. Numerical investigation shows that these two classes are the only that exist on the directed sandpile, regardless of the underlying topology, as long as it is strictly layered. Randomly adding a small proportion of links connecting non adjacent layers in an otherwise layered network takes the system out of the mean field regime to produce non-trivial avalanche-size probability density function. Although these do not display proper scaling, they closely reproduce the behaviour observed on the Japanese inter-firm network.     

Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology

We describe a sequence of methods to produce a partial differential equation model of the electrical activation of the ventricles. In our framework, we incorporate the anatomy and cardiac microstructure obtained from magnetic resonance imaging and diffusion tensor imaging of a New Zealand White rabbit, the Purkinje structure and the Purkinje-muscle junctions, and an electrophysiologically accurate model of the ventricular myocytes and tissue, which includes transmural and apex-to-base gradients of action potential characteristics. We solve the electrophysiology governing equations using the finite element method and compute both a 6-lead precordial electrocardiogram (ECG) and the activation wavefronts over time. We are particularly concerned with the validation of the various methods used in our model and, in this regard, propose a series of validation criteria that we consider essential. These include producing a physiologically accurate ECG, a correct ventricular activation sequence, and the inducibility of ventricular fibrillation. Among other components, we conclude that a Purkinje geometry with a high density of Purkinje muscle junctions covering the right and left ventricular endocardial surfaces as well as transmural and apex-to-base gradients in action potential characteristics are necessary to produce ECGs and time activation plots that agree with physiological observations.     

Partial Agonism: Mechanisms Based on Ligand-Receptor Interactions and on Stimulus-Response Coupling

Abstract

Substances eliciting, at very high concentrations, a lower maximal response of a particular biological system than a defined standard, are defined as partial agonists. The convention rests on the definition of a standard substance that achieves a ‘full’ maximal response; partial agonism being, therefore, relative. Various mechanisms lie behind this phenomenon: 1. Receptor-related mechanisms: the agonist-receptor complex exists in several conformational states from which only one, or only a few, activate the cell signaling pathway. This may occur when the receptor itself, or the agonist, exists in multiple states (e.g., in the form of enantiomers or stereoisomers), or when the agonist-receptor complex changes its conformation (receptor switch: two-state model of receptor activation). Furthermore, a steric hindrance by a ‘wrong-way binding’ of a part of the agonist's molecules may prevent the full ‘correct’ occupancy of receptors. 2. Mechanisms based on the efficacy of the stimulus-response coupling. The efficacy is then proportional to the sum of probabilities that receptors in individual states activate the cell-signaling pathway. Doses (concentrations) eliciting the half maximal response (EC₅₀), or similar response sensitivity parameters, are not included in the definition of partial agonism. However, tight correlations exist between maximal response and EC₅₀ in many, but not all, generic groups of agonistically acting substances. These relationships are frequently linear; intercepts and slopes of these ‘E, K_E plots’ are characteristic for individual, putative mechanisms. Dose-response curves of partial agonists are akin to those obtained for a response to a full agonist after a stepwise partial inactivation of receptors by an irreversible inhibitor. Also, the E, K_E plots obtained in these instances are similar to those of partial agonists. The receptor reserve, rather vaguely defined in early reports, is therefore closely linked to the phenomenon of partial agonism.     

利用荧光寿命变化定量分子内和分子间相互作用的方法

荧光寿命是指荧光分子在回到基态前在激发态停留的平均时间.本文发展了基于荧光寿命测量来定量分子内和分子间相互作用的方法:通过G碱基猝灭对于荧光寿命的影响定量DNA二级结构的形成;通过荧光共振能量传递(FRET)中荧光寿命的变化来定量分子间的相互作用.第一种方法巧妙利用了G碱基会猝灭临近的染料分子的性质,结合荧光寿命的变化...     

基于结构信息的蛋白质相互作用规律的研究

蛋白质-蛋白质相互作用是蛋白质发挥其功能的重要途径之一。作者基于相互作用蛋白质数据库、基因本体数据库和蛋白质结构分类数据库（structural classification of proteins, SCOP）,结合SWISS-PROT等相关数据库中蛋白质功能注释信息,定义描述蛋白质相互作用倾向性的参数,对酿酒酵母定位于不同细胞器蛋白、部分膜蛋白,以及酿酒酵母、线虫和大肠杆菌按SCOP分类的不同结构类蛋白质之间相互作用规律进行研究。结果表明各种细胞器、膜和结构类蛋白质之间相互作用确实存在明显的偏向性。     

InCeP: Intracellular Pathway Based on mKIAA Protein-Protein Interactions

Since December 2001, we have been conducting a project to isolateand determine entire sequences of mouse KIAA cDNA clones, whichencode polypeptides corresponding to human KIAA proteins. Theultimate goal of this project has been elucidation of the functionsof KIAA proteins. To address this issue, we have been generating‘libraries’ of antibodies against mKIAA proteins.We have, to date, already generated >800 antibodies. Usingour ‘libraries’ of antibodies, we are now identifyingendogenous mKIAA protein–protein interactions. In thepresent study, novel interactions were identified by MS/MS analysisfollowing immunoprecipitation with anti-mKIAA antibodies. Theinteractions with biologically known molecules should enableus to predict the function of mKIAA/KIAA proteins, includinghypothetical proteins identified in our cDNA project. Theseinteractions are subsequently used for construction of an intracellularpathway related to the mKIAA protein, and the pathway is distributedthrough the InCeP (IntraCellular Pathway based on mKIAA protein–proteininteractions) database. Users can freely access the InCeP throughthe internet and download the graphical display as well as thecurated information.     

基于物理化学性质优化的蛋白质相互作用预测研究

蛋白质相互作用预测是生物信息学研究的重要问题之一,提出了一种基于物理化学性质优化的蛋白质相互作用预测方法,与现有方法的显著不同就是,并未使用已知的氨基酸残基的物理化学性质,而是通过粒子群算法优化得到有益于相互作用预测的物理化学性质数值.对真实的数据集测试表明,优化得到的物理化学性质比现有的物理化学性质更有益于提高蛋白质相互作用的预测性能,与其它方法相比,也具有一定的优势,说明该方法是一种有效的蛋白质相互作用预测方法.     

基于蛋白序列的IDQD算法预测蛋白质相互作用

基于蛋白质序列组分信息,提出一个离散增量结合二次判别分析法(IDQD)预测蛋白质相互作用的模型,对人类蛋白质相互作用进行预测.自洽检验的识别精度达到75.89%,3-fold交叉检验的敏感性和特异性分别为64.22%和64.68%.结果表明IDQD算法可以用于蛋白质相互作用的预测.     

基于质谱技术的蛋白质互作研究进展

蛋白质作为生命活动的执行者,其功能往往体现在与其他蛋白质的相互作用中,研究蛋白-蛋白相互作用对于人们深入了解和预防传染病、靶向治疗多基因疾病、阐明蛋白质的分子作用机制及各种复杂的生命现象具有重要意义。目前,有多种技术被用来研究蛋白间的相互作用,研究难点在于实时捕获瞬时或弱蛋白质间的相互作用,质谱技术（mass spectrometry, MS）可在某种程度上解决该难点。由于质谱技术可研究简单的蛋白质复合物再到大规模的蛋白质组实验,基于质谱技术研究蛋白质间相互作用被越来越多地应用于科学研究中。综述了蛋白质间相互作用检测方法的研究进展,重点介绍了氢氘交换质谱法和化学交联质谱法研究蛋白质间相互作用的优缺点及其应用,最后对基于质谱技术研究蛋白质间相互作用进行了总结与展望,以期为深入开展相关研究提供借鉴。     

Predicting Kinase Activity in Angiotensin Receptor Phosphoproteomes Based on Sequence-Motifs and Interactions

Recent progress in the understanding of seven-transmembrane receptor (7TMR) signalling has promoted the development of a new generation of pathway selective ligands. The angiotensin II type I receptor (AT_1aR) is one of the most studied 7TMRs with respect to selective activation of the β-arrestin dependent signalling. Two complimentary global phosphoproteomics studies have analyzed the complex signalling induced by the AT_1aR. Here we integrate the data sets from these studies and perform a joint analysis using a novel method for prediction of differential kinase activity from phosphoproteomics data. The method builds upon NetworKIN, which applies sophisticated linear motif analysis in combination with contextual network modelling to predict kinase-substrate associations with high accuracy and sensitivity. These predictions form the basis for subsequently nonparametric statistical analysis to identify likely activated kinases. This suggested that AT_1aR-dependent signalling activates 48 of the 285 kinases detected in HEK293 cells. Of these, Aurora B, CLK3 and PKG1 have not previously been described in the pathway whereas others, such as PKA, PKB and PKC, are well known. In summary, we have developed a new method for kinase-centric analysis of phosphoproteomes to pinpoint differential kinase activity in large-scale data sets.     

Finding Candidate Drugs for Hepatitis C Based on Chemical-Chemical and Chemical-Protein Interactions

Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.     

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.     

基于GEO数据库的肝癌相关基因的筛选及验证

肝细胞癌(hepatocellular carcinoma,HCC)是中国高发的恶性肿瘤之一,识别肝细胞癌发生发展相关基因,对于深入研究肝癌发病机制和开发诊疗靶点均具有重要意义.本研究利用GEO2R工具从基因表达汇编数据库(Gene Expression Omnibus Database,GEO)筛选5个数据集中共有的差异表达基因作为潜在的肝癌相关基因.利用Metascape网站,对差异表达基因进行功能富集及信号通路分析.结合GEPIA(Gene Expres-sion Profiling Interaction Analysis)网站筛选具有临床意义的基因.利用荧光定量PCR技术验证与肝癌预后相关的差异表达基因,候选肝癌相关基因,为后续的深入研究奠定扎实的基础.结果显示,从5个数据集中共发现94个共有的差异表达基因.文献检索后发现24个基因与肝癌发生发展的关系少见文献报道,属于肝癌中未知功能基因.利用GEPIA分析癌症基因组图谱(the cancer genome atlas,TCGA)中数据后发现,GINS1在肝癌组织中高表达,与肝癌患者生存期呈负相关;CFHR4和DNASE1L3在肝癌组织中显著低表达,与肝癌患者生存期呈正相关.荧光定量PCR技术证实GINS1在81.3％的肝癌组织中呈现高表达,CFHR4和DNAS-E1L3分别在71.9％和93.8％肝癌组织中低表达.因此,本研究发现GINS1、CFHR4和DNASE 1L3在肝癌组织中显著差异表达,与肝癌患者的预后密切相关,可能作为潜在的判断肝癌患者预后的分子标志物和研发肝癌治疗的潜在靶标.     

基于雷帕霉素与FRB结合的蛋白质间相互作用的相关技术

FKBP12-rapamycin复合物的结合位点(FKBP1 2-rapamycin binding,FRB)为雷帕霉素(rapamycin,RAP)与哺乳动物雷帕霉素靶蛋白(mammalian target of rapamycin,mTOR)结合的结构城,基于RAP介导FK506结合蛋白12(12 kD FK506-binging protein,FKBP12)与FRB蛋白质相互作用相关研究技术的发展与应用,使人们对小分子介导的蛋白质相互作用有了更多的认识.就研究RAP作用于FRB域及研究FKBP12-RAP-FRB三元复合物形成的相关技术作一综述,为确认新的mTOR抑制剂的作用机制及认识其他蛋白质间相互作用提供参考.