Bridging genomics technology and biology

A report on the UK Genome Science Meeting, held at the University of Nottingham, UK, 2–4 September 2013.This year’s newly named UK Genome Science Meeting was the fourth edition of what was previously known as the UK Next Generation Sequencing Meeting. The renaming reflects technological developments that continue to redefine the meaning of next generation sequencing, and the fact that high-throughput sequencing is now a common tool in the scientific community. Indeed, an enormous diversity of topics was presented at this compact 3-day meeting, ranging from evolving technologies and bioinformatics, through to evolutionary genomics, metagenomics and clinical applications, amongst others. The meeting succeeded in portraying how, in relatively few years, new sequencing technologies have revolutionized the way we do basic research in just about every subject area, and are quickly making their way through translational research, and into the clinic and field.     

蛋白质磁共振——从结构生物学到结构基因组学

在后基因组时代,随着大量物种全基因组序列的获得,结构生物学家面临着结构基因组学的新机遇和挑战。与传统的结构生物学不同的是,结构基因组学的研究主要集中在结构和功能未知并且与从前研究的蛋白质相似性很小的蛋白质。准确的来讲,结构基因组学通过高通量蛋白质表达、结构解析来完成所有蛋白质家族的结构表征,从而能够通过结构预测功能。加州结构基因组学联合实验室发展了高度自动化的蛋白质合成、结晶、结构解析生产线。然而由于一些蛋白质不能被结晶,要想覆盖所有蛋白质结构域还有很大困难。Wuthrich的研究小组通过一些高通量的目的蛋白质筛选和NMR结构解析的方法解决了这一难题。与X射线晶体学解析蛋白质结构相比,NMR技术由于能够解析更接近生理状态的溶液结构而具有互补性。通过获得溶液中的蛋白质稳定性、动力学特征和相互作用信息,正如在朊蛋白和SARS相关蛋白的研究中所表现的那样,NMR技术从扩大已知的蛋白质结构数据库、新的蛋白质功能到化学生物学研究中都扮演着激动人心的角色。     

Structural genomics and structural biology: compare and contrast

A report on the Keystone Symposium 'Structural Genomics', held concurrently with the 'Frontiers in Structural Biology' symposium, Snowbird, USA, 13-19 April 2004.     

bioWidgets: data interaction components for genomics

MOTIVATION: The presentation of genomics data in a perspicuous visual format is critical for its rapid interpretation and validation. Relatively few public database developers have the resources to implement sophisticated front-end user interfaces themselves. Accordingly, these developers would benefit from a reusable toolkit of user interface and data visualization components. RESULTS: We have designed the bioWidget toolkit as a set of JavaBean components. It includes a wide array of user interface components and defines an architecture for assembling applications. The toolkit is founded on established software engineering design patterns and principles, including componentry, Model-View-Controller, factored models and schema neutrality. As a proof of concept, we have used the bioWidget toolkit to create three extendible applications: AnnotView, BlastView and AlignView.     

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.     

Focusing in on structural genomics: the University of Queensland structural biology pipeline

The flood of new genomic sequence information together with technological innovations in protein structure determination have led to worldwide structural genomics (SG) initiatives. The goals of SG initiatives are to accelerate the process of protein structure determination, to fill in protein fold space and to provide information about the function of uncharacterized proteins. In the long-term, these outcomes are likely to impact on medical biotechnology and drug discovery, leading to a better understanding of disease as well as the development of new therapeutics. Here we describe the high throughput pipeline established at the University of Queensland in Australia. In this focused pipeline, the targets for structure determination are proteins that are expressed in mouse macrophage cells and that are inferred to have a role in innate immunity. The aim is to characterize the molecular structure and the biochemical and cellular function of these targets by using a parallel processing pipeline. The pipeline is designed to work with tens to hundreds of target gene products and comprises target selection, cloning, expression, purification, crystallization and structure determination. The structures from this pipeline will provide insights into the function of previously uncharacterized macrophage proteins and could lead to the validation of new drug targets for chronic obstructive pulmonary disease and arthritis.     

From pull-down data to protein interaction networks and complexes with biological relevance

Motivation: Recent improvements in high-throughput Mass Spectrometry(MS) technology have expedited genome-wide discovery of protein–proteininteractions by providing a capability of detecting proteincomplexes in a physiological setting. Computational inferenceof protein interaction networks and protein complexes from MSdata are challenging. Advances are required in developing robustand seamlessly integrated procedures for assessment of protein–proteininteraction affinities, mathematical representation of proteininteraction networks, discovery of protein complexes and evaluationof their biological relevance. Results: A multi-step but easy-to-follow framework for identifyingprotein complexes from MS pull-down data is introduced. It assessesinteraction affinity between two proteins based on similarityof their co-purification patterns derived from MS data. It constructsa protein interaction network by adopting a knowledge-guidedthreshold selection method. Based on the network, it identifiesprotein complexes and infers their core components using a graph-theoreticalapproach. It deploys a statistical evaluation procedure to assessbiological relevance of each found complex. On Saccharomycescerevisiae pull-down data, the framework outperformed othermore complicated schemes by at least 10% in F₁-measure and identified610 protein complexes with high-functional homogeneity basedon the enrichment in Gene Ontology (GO) annotation. Manual examinationof the complexes brought forward the hypotheses on cause offalse identifications. Namely, co-purification of differentprotein complexes as mediated by a common non-protein molecule,such as DNA, might be a source of false positives. Protein identificationbias in pull-down technology, such as the hydrophilic bias couldresult in false negatives. Contact: samatovan{at}ornl.gov Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: Jonathan Wren Present address: Department of Biomedical Informatics, VanderbiltUniversity, Nashville, TN 37232. The authors wish it to be known that, in their opinion, thefirst two authors should be regarded as joint First Authors.     

A data management system for structural genomics

Background  

Structural genomics (SG) projects aim to determine thousands of protein structures by the development of high-throughput techniques for all steps of the experimental structure determination pipeline. Crucial to the success of such endeavours is the careful tracking and archiving of experimental and external data on protein targets.     

Simplivariate models: uncovering the underlying biology in functional genomics data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.     

Crystallization data mining in structural genomics: using positive and negative results to optimize protein crystallization screens

Recent efforts to collect and mine crystallization data from structural genomics (SG) consortia have led to the identification of minimal screens and novel screening strategies that can be used to streamline the crystallization process. Two groups, the Joint Center for Structural Genomics and the University of Toronto, carried out large-scale crystallization trials on different sets of bacterial targets (539, JCSG and 755, Toronto), using different sample processing and crystallization methods, and then analyzed their results to identify the smallest subset of conditions that would have crystallized the maximum number of protein targets. The JCSG Core Screen contains 67 conditions (from 480) while the Toronto Minimal Screen contains 6 (from 48). While the exact conditions included in the two screens do not overlap, the major precipitants of the conditions are similar and thus both screens can be used to determine if a protein has a natural propensity to crystallize. In addition, studies from other groups including the University of Queensland, the Mycobacterium tuberculosis SG group, the Southeast Collaboratory for SG, and the York Structural Biology Laboratory indicate that alternative crystallization strategies may be more successful at identifying initial crystallization conditions than typical sparse matrix screens. These minimal screens and alternative screening strategies are already being used to optimize the crystallization processes within large SG efforts. The differences between these results, however, demonstrate that additional studies which examine the influence of protein biophysical properties and sample preparation methods on crystal formation must also be carried out before more robust screens can be identified.     

Modeling of protein binary complexes using structural mass spectrometry data

In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology modeling approaches are used to define the structures of the individual proteins if footprinting detects significant conformational reorganization as a function of complex formation. A three-dimensional model of the complex is constructed from these binary partners using the ClusPro program, which is composed of docking, energy filtering, and clustering steps. Footprinting data are used to incorporate constraints-positive and/or negative-in the docking step and are also used to decide the type of energy filter-electrostatics or desolvation-in the successive energy-filtering step. By using this approach, we examine the structure of a number of binary complexes of monomeric actin and compare the results to crystallographic data. Based on docking alone, a number of competing models with widely varying structures are observed, one of which is likely to agree with crystallographic data. When the docking steps are guided by footprinting data, accurate models emerge as top scoring. We demonstrate this method with the actin/gelsolin segment-1 complex. We also provide a structural model for the actin/cofilin complex using this approach which does not have a crystal or NMR structure.     

Comparative genomics and structural biology of the molecular innovations of eukaryotes

Eukaryotes encode numerous proteins that either have no detectable homologs in prokaryotes or have only distant homologs. These molecular innovations of eukaryotes may be classified into three categories: proteins and domains inherited from prokaryotic precursors without drastic changes in biochemical function, but often recruited for novel roles in eukaryotes; new superfamilies or distinct biochemical functions emerging within pre-existing protein folds; and domains with genuinely new folds, apparently 'invented' at the outset of eukaryotic evolution. Most new folds emerging in eukaryotes are either alpha-helical or stabilized by metal chelation. Comparative genomics analyses point to an early phase of rapid evolution, and dramatic changes between the origin of the eukaryotic cell and the advent of the last common ancestor of extant eukaryotes. Extensive duplication of numerous genes, with subsequent functional diversification, is a distinctive feature of this turbulent era. Evolutionary analysis of ancient eukaryotic proteins is generally compatible with a two-symbiont scenario for eukaryotic origin, involving an alpha-proteobacterium (the ancestor of the mitochondria) and an archaeon, as well as key contributions from their selfish elements.     

Functional maps of protein complexes from quantitative genetic interaction data

Recently, a number of advanced screening technologies have allowed for the comprehensive quantification of aggravating and alleviating genetic interactions among gene pairs. In parallel, TAP-MS studies (tandem affinity purification followed by mass spectroscopy) have been successful at identifying physical protein interactions that can indicate proteins participating in the same molecular complex. Here, we propose a method for the joint learning of protein complexes and their functional relationships by integration of quantitative genetic interactions and TAP-MS data. Using 3 independent benchmark datasets, we demonstrate that this method is >50% more accurate at identifying functionally related protein pairs than previous approaches. Application to genes involved in yeast chromosome organization identifies a functional map of 91 multimeric complexes, a number of which are novel or have been substantially expanded by addition of new subunits. Interestingly, we find that complexes that are enriched for aggravating genetic interactions (i.e., synthetic lethality) are more likely to contain essential genes, linking each of these interactions to an underlying mechanism. These results demonstrate the importance of both large-scale genetic and physical interaction data in mapping pathway architecture and function.     

Integrating systems biology data to yield functional genomics insights

A report of the recent EMBO Conference 'From Functional Genomics to Systems Biology' held at the EMBL Advanced Training Centre, Heidelberg, Germany, 13-16 November 2010.     

Prospects for ab initio protein structural genomics

We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all alpha and alpha/beta proteins 50 to 150 residues in length, the method produced at least one model within 7 A rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.     

Automation of protein purification for structural genomics

A critical issue in structural genomics, and in structural biology in general, is the availability of high-quality samples. The additional challenge in structural genomics is the need to produce high numbers of proteins with low sequence similarities and poorly characterized or unknown properties. 'Structural-biology-grade' proteins must be generated in a quantity and quality suitable for structure determination experiments using X-ray crystallography or nuclear magnetic resonance (NMR). The choice of protein purification and handling procedures plays a critical role in obtaining high-quality protein samples. The purification procedure must yield a homogeneous protein and must be highly reproducible in order to supply milligram quantities of protein and/or its derivative containing marker atom(s). At the Midwest Center for Structural Genomics we have developed protocols for high-throughput protein purification. These protocols have been implemented on AKTA EXPLORER 3D and AKTA FPLC 3D workstations capable of performing multidimensional chromatography. The automated chromatography has been successfully applied to many soluble proteins of microbial origin. Various MCSG purification strategies, their implementation, and their success rates are discussed in this paper.