基因组信息的整合与植物功能基因组学研究的策略

近年来,随着许多植物基因组测序和可利用序列的增加,相继建立了一些基于靶基因诱变的“反向”遗传学研究策略,如T—DNA诱变、基因敲除、基因沉默和超表达分析等。同时,DNA微阵列和基因芯片技术的发展使得快速、定量检测植物发育不同时期和不同组织器官的基因转录时空变化成为现实。作图技术的改进和来自不同物种基因组信息的整合也正在加速图谱克隆程序的简化和发展。因此,随着生物基因组测序工作日益增多,整合不同类群植物基因组的信息和资源,在植物功能基因组学研究中的重要性日趋显著。     

从结构基因组学到功能基因组学

基因组学研究随着模式生物基因组全序列测定的完成由结构基因组学阶段发展到功能基因组学阶段,基因组学成为当今最为活跃、最有影响的前沿学科.以结构基因组学的研究成果为基础,功能基因组学中各学科因其原理不同及其关键技术的特点和优势,具有各自的应用范畴和发展趋势.功能基因组学不断渗透入现代科学的各领域,促成了适用于不同研究目的新兴学科的诞生.     

林木基因组学研究进展

林木基因组学研究进展迅速。结构基因组学方面,已构建了近40个主要造林树种的遗传连锁图谱,在不同树种中定位了30余个重要的数量性状位点,在部分树种中开展了基因组比较和综合图谱构建研究,杨树的全基因组测序已经完成,桉树的全基因组测序正在进行。功能基因组学方面,已分析了主要造林树种多种组织的转录组EST序列,对林木次生生长与木材形成、开花和抗寒性的形成等过程开展了功能基因组学研究。另外,探讨了林木基因组学研究的发展趋势,以期为我国林木基因组学研究提供有益的参考。     

植物抗性基因研究新趋势

多种生物基因组的大规模测序结果表明,抗性基因在基因组上成簇存在,从结构基因组、比较基因组、功能基因组与生物信息学等方面论述了植物抗性基因研究的新趋势。     

Using a neural network and spatial clustering to predict the location of active sites in enzymes

Structural genomics projects aim to provide a sharp increase in the number of structures of functionally unannotated, and largely unstudied, proteins. Algorithms and tools capable of deriving information about the nature, and location, of functional sites within a structure are increasingly useful therefore. Here, a neural network is trained to identify the catalytic residues found in enzymes, based on an analysis of the structure and sequence. The neural network output, and spatial clustering of the highly scoring residues are then used to predict the location of the active site.A comparison of the performance of differently trained neural networks is presented that shows how information from sequence and structure come together to improve the prediction accuracy of the network. Spatial clustering of the network results provides a reliable way of finding likely active sites. In over 69% of the test cases the active site is correctly predicted, and a further 25% are partially correctly predicted. The failures are generally due to the poor quality of the automatically generated sequence alignments.We also present predictions identifying the active site, and potential functional residues in five recently solved enzyme structures, not used in developing the method. The method correctly identifies the putative active site in each case. In most cases the likely functional residues are identified correctly, as well as some potentially novel functional groups.     

Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods

Calculations of charge interactions complement analysis of a characterised active site, rationalising pH-dependence of activity and transition state stabilisation. Prediction of active site location through large DeltapK(a)s or electrostatic strain is relevant for structural genomics. We report a study of ionisable groups in a set of 20 enzymes, finding that false positives obscure predictive potential. In a larger set of 156 enzymes, peaks in solvent-space electrostatic properties are calculated. Both electric field and potential match well to active site location. The best correlation is found with electrostatic potential calculated from uniform charge density over enzyme volume, rather than from assignment of a standard atom-specific charge set. Studying a shell around each molecule, for 77% of enzymes the potential peak is within that 5% of the shell closest to the active site centre, and 86% within 10%. Active site identification by largest cleft, also with projection onto a shell, gives 58% of enzymes for which the centre of the largest cleft lies within 5% of the active site, and 70% within 10%. Dielectric boundary conditions emphasise clefts in the uniform charge density method, which is suited to recognition of binding pockets embedded within larger clefts. The variation of peak potential with distance from active site, and comparison between enzyme and non-enzyme sets, gives an optimal threshold distinguishing enzyme from non-enzyme. We find that 87% of the enzyme set exceeds the threshold as compared to 29% of the non-enzyme set. Enzyme/non-enzyme homologues, structural genomics annotated proteins and catalytic/non-catalytic RNAs are studied in this context.     

Prediction of functional sites in proteins using conserved functional group analysis

A detailed knowledge of a protein's functional site is an absolute prerequisite for understanding its mode of action at the molecular level. However, the rapid pace at which sequence and structural information is being accumulated for proteins greatly exceeds our ability to determine their biochemical roles experimentally. As a result, computational methods are required which allow for the efficient processing of the evolutionary information contained in this wealth of data, in particular that related to the nature and location of functionally important sites and residues. The method presented here, referred to as conserved functional group (CFG) analysis, relies on a simplified representation of the chemical groups found in amino acid side-chains to identify functional sites from a single protein structure and a number of its sequence homologues. We show that CFG analysis can fully or partially predict the location of functional sites in approximately 96% of the 470 cases tested and that, unlike other methods available, it is able to tolerate wide variations in sequence identity. In addition, we discuss its potential in a structural genomics context, where automation, scalability and efficiency are critical, and an increasing number of protein structures are determined with no prior knowledge of function. This is exemplified by our analysis of the hypothetical protein Ydde_Ecoli, whose structure was recently solved by members of the North East Structural Genomics consortium. Although the proposed active site for this protein needs to be validated experimentally, this example illustrates the scope of CFG analysis as a general tool for the identification of residues likely to play an important role in a protein's biochemical function. Thus, our method offers a convenient solution to rapidly and automatically process the vast amounts of data that are beginning to emerge from structural genomics projects.     

Active site identification through geometry-based and sequence profile-based calculations: burial of catalytic clefts

Electrostatics calculations with proteins that are uniformly charged over volume can aid enzyme/non-enzyme discrimination. For known enzymes, such methods locate active sites to within 5% on the enzyme surface, in 77% of a test set. We now report that removing the dielectric boundary improves active site location to 80%, with optimal discrimination between enzymes and non-enzymes of around 80% specificity and 80% sensitivity. This calculation quantifies burial of solvent-accessible regions. Many of the true enzymes incorrectly assigned as non-enzymes have active sites at subunit boundaries. These are missed in monomer-based calculations. Catalytic and non-catalytic antibodies are studied in this context of active/binding site burial. Whilst catalytic antibodies, on average, have marginally higher active site burial than non-catalytic antibodies, these values are generally smaller than for non-antibody enzymes, possibly contributing to their relatively low turnover. Prediction of active site location improves further when sequence profile-based weights replace the uniform charge distribution, so that a combination of burial and amino acid conservation is assessed. Accuracy rises to 93% of active sites to within 5%, in the test set, for the optimal profile weights scheme. The equivalent value in a separate validation set is 89% to within 5%. Enzyme/non-enzyme and enzyme functional site predictions are made for structural genomics proteins, suggesting that a substantial majority of these are non-enzymes.     

Chemogenomics approaches to novel target discovery

Chemogenomics involves the combination of a compound’s effect on biological targets together with modern genomics technologies. The merger of these two methodologies is creating a new way to screen for compound–target interactions, as well as map chemical and biological space in a parallel fashion. The challenge associated with mining complex databases has initiated the development of many novel in silico tools to profile and analyze data in a systematic way. The ability to analyze the combinatorial effects of chemical libraries on biological systems will aid the discovery of new therapeutic entities. Chemogenomics provides a tool for the rapid validation of novel targeted therapeutics, where a specific molecular target is modulated by a small molecule. Along with targeted therapies comes the ability to discovery pathway nodes where a single molecular target might be an essential component of more than one disease. Several disease areas will benefit directly from the chemogenomics approach, the most advanced being cancer. A genetic loss-of-function screen can be modulated in the presence of a compound to search for genes or pathways involved in the compound’s activity. Several recent papers highlight how chemogenomics is changing with RNA interference-based screening and shaping the discovery of new targeted therapies. Together, chemical and RNA interference-based screens open the door for a new way to discovery disease-associated genes and novel targeted therapies.     

Computational method for predicting protein functional sites with the use of specificity determinants

The currently available body of decoded amino acid sequences of various proteins exceeds manifold the experimental capabilities of their functional annotation. Therefore, in silico annotation using bioinformatics methods becomes increasingly important. Such annotation is actually a prediction; however, this can be an important starting point for further laboratory research. This work describes a new method for predicting functionally important protein sites, SDPsite, on the basis of identification of specificity determinants. The algorithm proposed utilizes a protein family aglinment and a phylogenetic tree to predict the conserved positions and specificity determinants, map them onto the protein structure, and search for clusters of the predicted positions. Comparison of the resulting predictions with experimental data and published predictions of functional sites by other methods demonstrates that the results of SDPsite agree well with experimental data and exceed the results obtained with the majority of previous methods. SDPsite is publicly available at http://bioinf.fbb.msu.ru/SDPsite.     

Structures of randomly generated mutants of T4 lysozyme show that protein stability can be enhanced by relaxation of strain and by improved hydrogen bonding via bound solvent.

The structures of three mutants of bacteriophage T4 lysozyme selected using a screen designed to identify thermostable variants are described. Each of the mutants has a substitution involving threonine. Two of the variants, Thr 26-->Ser (T26S) and Thr 151-->Ser (T151S), have increased reversible melting temperatures with respect to the wild-type protein. The third, Ala 93-->Thr (A93T), has essentially the same stability as wild type. Thr 26 is in the wall of the active-site cleft. Its replacement with serine results in the rearrangement of nearby residues, most notably Tyr 18, suggesting that the increase in stability may result from the removal of strain. Thr 151 in the wild-type structure is far from the active site and appears to sterically prevent the access of solvent to a preformed binding site. In the mutant, the removal of the methyl group allows access to the solvent binding site and, in addition, the Ser 151 hydroxyl rotates to a new position so that it also contributes to solvent binding. Residue 93 is in a highly exposed site on the surface of the molecule, and presumably is equally solvent exposed in the unfolded protein. It is, therefore, not surprising that the substitution Ala 93-->Thr does not change stability. The mutant structures show how chemically similar mutations can have different effects on both the structure and stability of the protein, depending on the structural context. The results also illustrate the power of random mutagenesis in obtaining variants with a desired phenotype.(ABSTRACT TRUNCATED AT 250 WORDS)     

Protein structure prediction in genomics

As the number of completely sequenced genomes rapidly increases, including now the complete Human Genome sequence, the post-genomic problems of genome-scale protein structure determination and the issue of gene function identification become ever more pressing. In fact, these problems can be seen as interrelated in that experimentally determining or predicting or the structure of proteins encoded by genes of interest is one possible means to glean subtle hints as to the functions of these genes. The applicability of this approach to gene characterisation is reviewed, along with a brief survey of the reliability of large-scale protein structure prediction methods and the prospects for the development of new prediction methods.