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.     

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.     

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。     

Integration of structural and functional genomics

This paper introduces a special issue of Animal Genetics , which is devoted to the recent symposium held at Iowa State University entitled 'Integration of Structural and Functional Genomics'. We describe issues and needs that confront the animal genomics community, and describe how this symposium was structured to address these issues by improving communication and collaboration across species and disciplines. The session topics and oral presentations are briefly described for all invited speakers.     

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.     

小麦的比较基因组学和功能基因组学

小麦是异源多倍体植物,具有大的染色体组,并且基因组中重复序列所占比例较高,这些特征限制了小麦基因组研究的进展。比较基因组学方法为运用模式植物进行小麦基因组学研究提供了一个操作平台。功能基因组学的研究集中于基因组中转录表达的部分,基因功能的确定是功能基因组学研究的主要内容。对比较基因组学在小麦基因组研究中的应用和小麦功能基因组学的研究内容和方法进行了综述。     

Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.     

Ion mobility–mass spectrometry for structural proteomics

Ion mobility coupled to mass spectrometry has been an important tool in the fields of chemical physics and analytical chemistry for decades, but its potential for interrogating the structure of proteins and multiprotein complexes has only recently begun to be realized. Today, ion mobility–mass spectrometry is often applied to the structural elucidation of protein assemblies that have failed high-throughput crystallization or NMR spectroscopy screens. Here, we highlight the technology, approaches and data that have led to this dramatic shift in use, including emerging trends such as the integration of ion mobility–mass spectrometry data with more classical (e.g., ‘bottom-up’) proteomics approaches for the rapid structural characterization of protein networks.     

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.