生物物理学的几个热点领域

物理学与生物科学的交叉由来已久，这不仅解决了自然界许多重大的理论问题，并且在高层次上开辟了新的技术领域，如生物信息学、纳米生物学和脑与认知科学等，文章对当今生物物理学的这几个热点领域进行了介绍。     

物理学和生物学(上)

20世纪物理学研究从微观领域到宏观宇宙都取得很大进展。对生物的研究已经成为新世纪物理学的重要主题。文章简要阐述了分子生物学中的一些基本概念，说明了一些有趣的问题，同时总结了作者最近在生物信息学方面的工作。     

A novel method for similarity/dissimilarity analysis of protein sequences

Sequence comparison is one of the major tasks in bioinformatics, which can be used to study structural and functional conservation, as well as evolutionary relations among the sequences. In this paper, we introduce the concept of distance frequency of amino acid pairs and propose a new numerical characterization of protein sequences, which converts any protein sequence into a distance frequency matrix. Using this distance frequency matrix, we can compare the similarity of protein sequences. In order to confirm the validity of our method, we test it with two experiments. The results show that our method is effective.     

Design and synthesis of novel spirooxindole–indenoquinoxaline derivatives as novel tryptophanyl-tRNA synthetase inhibitors

Molecular Diversity - In the current study, we used an integrated approach combining bioinformatics, rational drug design, one-pot synthesis, and biological experiments in vitro for the potential...     

Reconstruction of protein structures from a vectorial representation

We show that the contact map of the native structure of globular proteins can be reconstructed starting from the sole knowledge of the contact map's principal eigenvector, and present an exact algorithm for this purpose. Our algorithm yields a unique contact map for all 221 globular structures of PDBselect25 of length N相似文献   

Investigation of Commuting Hamiltonian in Quantum Markov Network

Graphical Models have various applications in science and engineering which include physics, bioinformatics, telecommunication and etc. Usage of graphical models needs complex computations in order to evaluation of marginal functions, so there are some powerful methods including mean field approximation, belief propagation algorithm and etc. Quantum graphical models have been recently developed in context of quantum information and computation, and quantum statistical physics, which is possible by generalization of classical probability theory to quantum theory. The main goal of this paper is preparing a primary generalization of Markov network, as a type of graphical models, to quantum case and applying in quantum statistical physics. We have investigated the Markov network and the role of commuting Hamiltonian terms in conditional independence with simple examples of quantum statistical physics.     

Heterologous production of fungal natural products: Reconstitution of biosynthetic gene clusters in model host Aspergillus oryzae

While exploring phytotoxic metabolites from phytopathogenic fungi in the 1970s, we became interested in biosynthetic enzymes that catalyze Diels–Alder reactions involving biosynthesis of several phytotoxins that we isolated. Target enzymes were successfully characterized, and this triggered the identification of various Diels–Alderases in a recent decade. Through our Diels–Alderase project in 1990s, we recognized a highly efficient expression system of various biosynthetic genes with Aspergillus oryzae as a host. With the development of tools such as genomic data and bioinformatics analysis to identify biosynthetic gene clusters for natural products, we developed a highly reliable methodology such as hot spot knock-in to elucidate the biosynthetic pathways of representative fungal metabolites including phytotoxic substances. This methodology allows total biosynthesis of natural products and genome mining using silent biosynthetic gene clusters to obtain novel bioactive metabolites. Further applications of this technology are discussed.     

珙桐热休克蛋白序列分析及功能预测

从本实验室已构建好的珙桐种子cDNA文库中获得热休克蛋白18(HSP18)核苷酸序列,该序列编码蛋白属小热休克蛋白(sHSP).用生物信息学的方法对HSP18从同源性、氨基酸组成、理化性质、疏水性/亲水性、信号肽、跨膜结构域、卷曲螺旋、蛋白质二级及三级结构进行预测和分析.结果表明,珙桐HSP18是不具有信号肽、没有跨膜...     

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel’s concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.     

Future pathways for combinatorial chemistry

Summary Investment in combinatorial chemistry (combichem) in the pharmaceutical industry is being driven by the need for increased efficiency. Results from pioneers in the field have demonstrated where mixture or discrete compound synthesis is useful, and what mixture sizes and compound concentrations are appropriate. To make the techniques of combichem of general utility in drug discovery, a broad range of advances is still required. Conversion of organic chemistry to solid phase conditions is key, as are developments in linkers and resins. Library design methodology requires further development. Combinatorial biosynthesis of focused libraries of natural products holds great promise for capitalising on hardwon natural product leads. Miniaturisation of screens is required to reduce the cost of screening combinatorial libraries. Developments in the processes preceding and following synthesis are required to enable the flow of increased numbers of compounds without new bottlenecks developing. The impact of combinatorial chemistry will be greatly enhanced by synergy with ongoing parallel developments in genetic technologies, screening technologies and bioinformatics.     

牛乳β-乳球蛋白热稳定与免疫原性关系的光谱学研究

利用圆二色性光谱和荧光光谱对牛乳β-乳球蛋白的热变性过程进行光谱学分析,得到其热变性过程的光谱学特征;结合生物信息学方法,进一步分析了热变性过程中牛乳β-乳球蛋白构象变化的特点,探讨了热变性使牛乳β-乳球蛋白蛋白构象变化导致其免疫原性发生变化的关系,建立了一种研究过敏原蛋白热变性与免疫原性关系的光谱实验方法,为探讨热变...     

牛乳β-乳球蛋白热稳定与免疫原性关系的光谱学研究

利用圆二色性光谱和荧光光谱对牛乳β-乳球蛋白的热变性过程进行光谱学分析,得到其热变性过程的光谱学特征;结合生物信息学方法,进一步分析了热变性过程中牛乳β-乳球蛋白构象变化的特点,探讨了热变性使牛乳β-乳球蛋白蛋白构象变化导致其免疫原性发生变化的关系,建立了一种研究过敏原蛋白热变性与免疫原性关系的光谱实验方法,为探讨热变性改变食物中主要过敏原的免疫原性提供了理论依据。     

Divergent estimation error in portfolio optimization and in linear regression

The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.     

椰子花粉过敏原profilin蛋白体外重折叠过程的光谱学研究

在基因工程技术中,采用原核表达系统获得的重组蛋白通常都是以包涵体的形式存在.利用圆二色性光谱、荧光光谱和同步荧光光谱对尿素诱导的重组椰子化粉泛过敏原profilin蛋白包涵体的复性过程进行了系统的光谱学研究,得到了profilin蛋白复性过程的光谱学特征;结合生物信息学方法,通过对profilin蛋白的二级结构和三级结构的预测,进一步分析了复性过程中profilin蛋白构象变化的特点,初步建市了一种新的检测重组变应原蛋白复性程度的光谱学实验方法,为重组蛋白包涵体复性机理的深入研究进行了有益的探索.     

Mass spectrometry in the proteome analysis of mature cereal kernels

In the last decade, the improved performance and versatility of the mass spectrometers together with the increasing availability of gene and genomic sequence database, led the mass spectrometry to become an indispensable tool for either protein and proteome analyses in cereals. Mass spectrometric works on prolamins have rapidly evolved from the determination of the molecular masses of proteins to the proteomic approaches aimed to a large-scale protein identification and study of functional and regulatory aspects of proteins. Mass spectrometry coupled with electrophoresis, chromatographic methods, and bioinformatics tools is currently making significant contributions to a better knowledge of the composition and structure of the cereal proteins and their structure-function relationships. Results obtained using mass spectrometry, including characterization of prolamins, investigation of the gluten toxicity for coeliac patients, identification of proteins responsible of cereal allergies, determination of the protein pattern and its modification under environmental or stress effects, investigation of genetically modified varieties by proteomic approaches, are summarized here, to illustrate current trends, analytical troubles and challenges, and suggest possible future perspectives.     

蛋白质组学及其在重离子治癌应用中的探讨

综述了蛋白质组学的主要方法和技术——双向凝胶电泳、生物质谱技术、蛋白质芯片技术，及其在肿瘤标志物的筛选和鉴定、肿瘤治疗效果的评价及肿瘤发生机制研究等方面的应用状况，并对蛋白质组学在重离子治疗中的应用进行了展望。With the accomplishment of the human genome project, proteomics becomes a new snbject on the buildup of the whole proteins and their dynamic changes in cell emerges. Cancer is a kind of complex disease involved in multi-genes and proteins. Heavy ion therapy is all arising and potential radiation treatment nowadays. This paper reviews on the main methods and technology in proteomics-- two-dimensional gel electrophoresis （2-DE）,biological mass spectrometry, protein biochips, bioinformatics and its application on identification of cancer biomarkers, evaluation of curative effect on tumour and the mechanisms of turnout formation. This paper also presents some prospects on the application of proteomics in heavv ion therapy.     

Clustering gene expression data analysis using an improved EM algorithm based on multivariate elliptical contoured mixture models

Clustering gene expression data is an important research topic in bioinformatics because knowing which genes act similarly can lead to the discovery of important biological information. Many clustering algorithms have been used in the field of gene clustering. The multivariate Gaussian mixture distribution function was frequently used as the component of the finite mixture model for clustering, however the clustering cannot be restricted to the normal distribution in the real dataset. In order to make the cluster algorithm strong adaptability, this paper proposes a new scheme for clustering gene expression data based on the multivariate elliptical contoured mixture models (MECMMs). To solve the problem of over-reliance on the initialization, we propose an improved expectation maximization (EM) algorithm by adding and deleting initial value for the classical EM algorithm, and the number of clusters can be treated as a known parameter and inferred with the QAIC criterion. The improved EM algorithm based on the MECMMs is tested and compared with some other clustering algorithms, the performance of our clustering algorithm has been extensively compared over several simulated and real gene expression datasets. Our results indicated that improved EM clustering algorithm is superior to the classical EM algorithm and the support vector machines (SVMs) algorithm, and can be widely used for gene clustering.     

分子模拟在生物化学中的应用实例

分子模拟是一种描述和模拟分子和分子体系运动状态和性质的方法.随着电子计算机技术的飞速发展,分子模拟进入了一个前所未有的新时代.在此之前,人们只能通过机械模型和纸笔计算进行简单的分子模拟,现在通过利用电子计算机人们可以做更为复杂、更为全面的分子模拟.本文通过两个实例来简单阐述了分子模拟在生物化学中的应用.一则是通过模拟膦酰基氧化腈和丙乙腈的1,3偶极环加成反应过程,用密度泛函理论方法在B3LYP/6-31G(d,p)水平上解释了得到2∶1的加成产物的现象,来解释1,3偶极环加成反应得到2:1加成产物的现象.一则是通过结构生物信息学的方法建立H5N1高致病性禽流感病毒蛋白的三维结构,模拟其与一些药物分子的相互作用,研究H5N1的活性中心.     

Classification of genes based on gene expression analysis

Systems biology and bioinformatics are now major fields for productive research. DNA microarrays and other array technologies and genome sequencing have advanced to the point that it is now possible to monitor gene expression on a genomic scale. Gene expression analysis is discussed and some important clustering techniques are considered. The patterns identified in the data suggest similarities in the gene behavior, which provides useful information for the gene functionalities. We discuss measures for investigating the homogeneity of gene expression data in order to optimize the clustering process. We contribute to the knowledge of functional roles and regulation of E. coli genes by proposing a classification of these genes based on consistently correlated genes in expression data and similarities of gene expression patterns. A new visualization tool for targeted projection pursuit and dimensionality reduction of gene expression data is demonstrated. The text was submitted by the authors in English.