Formins： Bringing new insights to the organization of actin cytoskeleton

The actin cytoskeleton is an important component of eukaryotic cell cytoskeleton and is temporally and spatially controlled by a series of actin binding proteins (ABPs). Among ABPs, formin family proteins have attracted much attention as they can nucleate unbranched actin filament from the profilin bound actin pool in vivo. In recent years, a number of formin family members from different organisms have been reported, and their characteristics are known more clearly, although some questions are still to be clarified. Here, we summarize the structures, func-tions and nucleation mechanisms of different formin family proteins, intending to compare them and give some new clues to the study of formins.     

How the “Folding Funnel” Depends on Size and Structure of Proteins？A View from the Scoring Function Perspective

It has been well accepted that the folding energy landscape may resemble a funnel according to the theory of protein folding. This theory of "folding funnel" has been extensively studied and thought to play an important role in guiding the sampling process of the protein folding and refinement in protein structure prediction. Here, we have investigated the relationship between the "funnel likeness" of protein folding and the size/structure of the proteins based on a set of non-homologous proteins we have recently evaluated using a statistical mechanicsbased scoring function ITScorePro. It was found that larger proteins that consist of more helix/sheet structures tend to have a higher score-Root Mean Square Deviation(RMSD) correlation(or a more funnel like energy landscape).Another measurement in protein folding, Z-score, has also shown some correlation with the size of the proteins.As expected, proteins with a better "olding funnel likeness"(or score-RMSD correlation) tend to have a betterpredicted conformation with a lower RMSD from their native structures. These findings can be extremely valuable for the development and improvement of sampling and scoring algorithms for protein structure prediction.     

A complete sequence and comparative analysis of a SARS-associated virus （Isolate BJO1）

The genome sequence of the Severe Acute Respiratory Syndrome (SARS)-assoclated virus provides essential information for the identification of pathogen(s), exploration of etiology and evolution, interpretation of transmission and pathogenesis, development of diagnostics, prevention by future vaccination, and treatment by developing new drugs.We report the complete genome sequence and comparative analysis of an isolate (B J01) of the coronavirus that has been recognized as a pathogen for SARS. The genome is 29725 nt in size and has 11 ORFs (Open Reading Frames). It is composed of a stable region encoding an RNA-dependent RNA polymerase (composed of 20RFs) and a variable region representing 4 CDSs (coding sequences) for viral structural genes (the S, E, M, N proteins) and 5 PUPs (putative uncharacterized proteins). Its gene order is identical to that of other known coronaviruses. The sequence alignment with all known RNA viruses places this virus as a member in the family of Coronaviridae. Thirty putative substitutions have been identified by comparative analysis of the 5 SARS-associated virus genome sequences in GenBank. Fifteen of them lead to possible amino acid changes (non-synonymous mutations) in the proteins. Three amino acid changes, with predicted alteration of physical and chemical features, have been detected in the S protein that is postulated to be involved in the immunoreactions between the virus and its host.Two amino acid changes have been detected in the M protein,which could be related to viral envelope formation. Phylogenetic analysis suggests the possibility of non-human origin of the SARS-associated viruses but provides no evidence that they are man-made. Further efforts should focus on identifying the etiology of the SARS-associated virus and ruling out conclusively the existence of other possible SARS-related pathogen(s).     

The Teacher and The Student in the Art of The Teaching

How could we have a effective teaching?We,as teachers,always think about this question and always hope to have better experiences to supple- ment us.The art of teaching lies in the teaching of the teacher and the studying of the students.During this course, a teacher's quality will be re- garded as an important factor in the art of teaching which we have to think over and discuss.Of course,the factors of students will be undoubtedly tak- en into consideration because of their significance.HereⅠwould like to sunmaarize the following points to improve our teaching effect.     

Bioinformatical study on the proteomics and evolution of SARS-CoV

A novel coronavirus has been identified as the causative agent of the severe acute respiratory syndrome (SARS). For all the SARS-CoV associated proteins derivated from the SARS-CoV genome, the physiochemical properties such as the molecular weight, isoelectric point and extinction coefficient of each protein were calculated. The transmembrane segments and subeellular localization (SubLoeation) prediction and conserved protein motifs search against database were employed to analyze the function of SARS-CoV proteins. Also, the homology protein sequence alignment and evolutionary distance matrix calculation between SARS-CoV associated proteins and the corresponding proteins of other coronaviruses were employed to identify the classification and phylogenetic relationship between SARS-CoV and other coronaviruses. The results showed that SARS-CoV is a novel coronavirus which is different from any of the three previously known groups of coronviruses, but it is closer to BoCoV and MHV than to other coronaviruses. This study is in aid of experimental determination of SARS-CoV proteomics and the development of antiviral vaccine.     

GO molecular function coding based protein subcellular localization prediction

Subcellular localization is an important feature of proteins which is closely correlated to their function. In this work,we tried to develop a new coding method of using those location predictive molecular function terms of protein as the input for the prediction of subcellular localization. Combined with the amino acid pair composition of the sequence,this coding system is proved to be efficient for support vector machine (SVM) and to have satisfied performance when tested on the RH dataset. Meanwhile,the model also shows robustness against N-terminal uncertainties in sequences.     

Design of Smart Polymer-Protein Conjugates and Smart Magnetic Nanoparticles for Use in Microfluidic Diagnostic Assays

1 Results In this talk,I will describe the design,synthesis and application of smart polymers for use in microfluidic diagnostic devices.We are synthesizing a variety of temperature- and pH-responsive polymers using RAFT living free radical polymerization techniques.This allows us to control molecular weight and to achieve a narrow MW distribution of the polymers. Furthermore,RAFT polymers have reactive end groups that are used to conjugate the polymers to proteins.We are also using those groups to bind and coat the smart polymers onto the surfaces of polymeric or magnetic nanoparticles.We have applied UV graft co-polymerization techniques to coat the channel surfaces in the microfluidic devices with the smart polymers.The smart polymers we work with include temperature-responsive polymers such as poly(N-isopropyl acrylamide) or PNIPAAm and pH-responsive polymers such as poly(propylacrylic acid) or PPAA.We are synthesizing random,block and graft copolymers of these compositions as well.     

Prediction of Essential Proteins Using Topological Properties in GO-Pruned PPI Network Based on Machine Learning Methods

The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput technologies and the production of large amounts of data enable the discovery of essential proteins at the system level by analyzing Protein-Protein Interaction (PPI) networks, and replacing biological or chemical experiments. Furthermore, additional gene-level annotation information, such as Gene Ontology (GO) terms, helps to detect essential proteins with higher accuracy. Various centrality algorithms have been used to determine essential proteins in a PPI network, and, recently motif centrality GO, which is based on network motifs and GO terms, works best in detecting essential proteins in a Baker’s yeast Saccharomyces cerevisiae PPI network, compared to other centrality algorithms. However, each centrality algorithm contributes to the detection of essential proteins with different properties, which makes the integration of them a logical next step. In this paper, we construct a new feature space, named CENT-ING-GO consisting of various centrality measures and GO terms, and provide a computational approach to predict essential proteins with various machine learning techniques. The experimental results show that CENT-ING-GO feature space improves performance over the INT-GO feature space in previous work by Acencio and Lemke in 2009. We also demonstrate that pruning a PPI with informative GO terms can improve the prediction performance further.     

A data-mining approach to biomarker identification from protein profiles using discrete stationary wavelet transform

Objective： To develop a new bioinformatic tool based on a data-mining approach for extraction of the most informative proteins that could be used to find the potential biomarkers for the detection of cancer. Methods： Two independent datasets from serum samples of 253 ovarian cancer and 167 breast cancer patients were used. The samples were examined by surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF MS）. The datasets were used to extract the informative proteins using a data-mining method in the discrete stationary wavelet transform domain. As a dimensionality reduction procedure, the hard thresholding method was applied to reduce the number of wavelet coefficients. Also, a distance measure was used to select the most discriminative coefficients. To find the potential biomarkers using the selected wavelet coefficients, we applied the inverse discrete stationary wavelet transform combined with a two-sided t-test. Results： From the ovarian cancer dataset, a set of five proteins were detected as potential biomarkers that could be used to identify the cancer patients from the healthy cases with accuracy, sensitivity, and specificity of 100%. Also, from the breast cancer dataset, a set of eight proteins were found as the potential biomarkers that could separate the healthy cases from the cancer patients with accuracy of 98.26%, sensitivity of 100%, and specificity of 95.6%. Conclusion： The results have shown that the new bioinformatic tool can be used in combination with the high-throughput proteomic data such as SELDI-TOF MS to find the potential biomarkers with high discriminative power.     

Progresses in studies of nuclear actin

Actin is a protein abundant in cells. Recently, it has been proved to be universally existent in the nuclei of many cell types. Actin and actin-binding proteins, as well as aetin-related proteins, are necessary for the mediation of the conformation and function of nuclear actin, including the transformation of actin between unpolymerized and polymerized, chromatin remodeling, regulation of gene expression and RNA processing as well as RNA transportation. In this paper, we summarized the progresses in the research of nuclear actin.     

SARS冠状病毒及相关病毒的分子系统学分析

根据SARS冠状病毒及其相关病毒的基因组核酸序列和3种不同蛋白质序列，应用最大简约法和最小进化法重建系统发育树；并对SARS冠状病毒的11个推测蛋白质(ORF)做BLAST分析。结果表明，SARS冠状病毒和鼠肝炎病毒——牛冠状病毒分支构成姊妹群。其单系群性质得到强有力的统计学支持。这暗示了SARS的爆发可能源自种间屏障的突破事件，该病毒天然宿主可能为猪、牛或鼠。SARS冠状病毒与已知的人冠状病毒分属冠状病毒科的不同分支，因此致病机制可能有很大不同。3个基因的系统树分支格局的一致性表明：SARS冠状病毒这3个主要基因与其他冠状病毒间不存在重组，但全部11个ORF的BLAST分析却认为其基因组上一些小的区段可能与其他病毒存在重组。     

SARS疫情传播的时间序列分析

利用时间序列分析的思想[1～3],对北京市2003年4～6月的累计确诊SARS病例进行研究,获得了日增确诊病例的变化趋势方程,并用自回归模型AR(20)来拟合传播过程,经方差分析知模型效果高度显著,将预测值与实际值比较,结果比较理想.由此推定每个SARS病人可以直接造成他人感染的期限平均在20天左右.     

Control dynamics of severe acute respiratory syndrome transmission

Severe acute respiratory syndrome (SARS) is a serious disease with many puzzling features. We present a simple, dynamic model to assess the epidemic potential of SARS and the effectiveness of control measures. With this model, we analysed the SARS epidemic data in Beijing.The data fitting gives the basic case reproduction number of 2.16 leading to the outbreak, and the variation of the effective reproduction number reflecting the control effect. Noticeably, our study shows that the response time and the strength of control measures have significant effects on the scale of the outbreak and the lasting time of the epidemic.     

Reovirus, isolated from SARS patients

Beijing has been severely affected by SARS, and SARS-associated coronavirus has been confirmed as its cause. However, clinical and experimental evidence implicates the possibility of co-infection. In this report, reovirus was isolated from throat swabs of SARS patients, including the first case in Beijing and her mother. Identification with the electron microscopy revealed the characteristic features of reovirus. 24 of 38 samples from other SARS cases were found to have serologic responses to the reovirus. Primers designed for reovirus have amplified several fragments of DNA, one of which was sequenced (S2 gene fragment), which indicates it as a unique reovirus (orthoreovirus). Preliminary animal experiment showed that inoculation of the reovirus in mice caused death with atypical pneumonia. Nevertheless, the association of reovirus with SARS outbreak requires to be further investigated.     

A complete sequence and comparative analysis of a SARS-associated virus(Isolate BJ01)

Severe Acute Respiratory Syndrome (SARS) is a newly identified infectious disease[1—5]. The global outbreak of SARS has been threatening the health of people worldwide and has killed 353 people and infected more than 5462 in 27 countries, as reported by WHO on April 29, 2003 (http://www.who.int/csr/sarscountry/en). Although it has been recognized that a variant of virus from the family of coronavirus might be the candidate pathogen of SARS[1—5], its identity as the unique pathogen sti…     

“非典”时期贵阳民众社会心理调查报告

为抗击非典型肺炎 ,让各级政府部门领导同志能及时、准确地了解民众的社会心理行为状态 ,我们于 2 0 0 3年 5月 7～ 10日 ,对贵州省贵阳市民众进行了抽样问卷调查 ,内容涉及疫情风险认知 (疫情发布信息源、政府行为信息影响 )、民众认知水平、民众心理行为变化、民众恐惧心理来源以及民众信心的预期等指标。并得出相应结论。     

Bioinformatics analysis of SARS-Cov M protein provides information for vaccine development

Severalmonthsago ,anoutbreakofsevereacuterespiratorysyndrome (SARS )startedtospreadaroundtheworld .Asofthiswriting (May 14 ,2 0 0 3) ,morethan 75 0 0 personshavebeeninfectedinasmanyas 2 9countries[1] .The pathogencausingSARShasalreadybeenidentifiedtobeSARS Covbyanumberoflaboratoriesworldwide[2 ,3] .Severedis easesinanimals ,suchasthediseasescausedbyporcinetransmissiblegastroenteritisvirus (TGEV ) ,murinehepatitisvirus (MHV)andporcinehemagglu tinatingencephalomyelitisvirus (PHEV ) ,ha…     

The outbreak pattern of the SARS cases in Asia

The severe acute respiratory syndrome (SARS) caused tremendous damage to many Asia countries, especially China. The transmission process and outbreak pattern of SARS is still not well understood. This study aims to find a simple model to describe the outbreak pattern of SARS cases by using SARS case data commonly released by governments. The outbreak pattern of cumulative SARS cases is expected to be a logistic type because the infection will be slowed down due to the increasing control effort by people and/or due to depletion of susceptible individuals. The increase rate of SARS cases is expected to decrease with the cumulative SARS cases, as described by the traditional logistical model, which is widely used in population dynamic studies. The instantaneous rate of increases were significantly and negatively correlated with the cumulative SARS cases in mainland of China (including Beijing, Hebei, Tianjin, Shanxi,the Autonomous Region of Inner Mongolia) and Singapore. The basic reproduction number R0 in Asia ranged from 2.0 to 5.6 (except for Taiwan, China). The R0 of Hebei and Tianjin were much higher than that of Singapore, Hongkong, Beijing, Shanxi, Inner Mongolia, indicating SARS virus might have originated differently or new mutations occurred during transmission. We demonstrated that the outbreaks of SARS in many regions of Asia were wall described by the logistic model, and the control measures implemented by governments are effective. The maximum instantaneous rate of increase, basic reproductive number, and maximum cumulative SARS cases were also calculated by using the logistic model.     

SARS传播模型的探讨

首先以传统的Logistic模型和SEIR模型分析了SARS传播的一般规律,并以北京地区2003年4月20日到6月23日有关SARS数据为参考资料,对北京地区SARS疫情高峰期和最终感染人数作出估计,由此得到SARS传播服从Logistic模型和SEIR模型,其次,在此基础上分析了两物种间的疾病传播规律,建立了两物种间疾病传播的SEIR模型。