Structure-based functional identification of a novel heme-binding protein from <Emphasis Type=Italic>Thermus thermophilus</Emphasis> HB8

The TT1485 gene from Thermus thermophilus HB8 encodes a hypothetical protein of unknown function with about 20 sequence homologs of bacterial or archaeal origin. Together they form a family of uncharacterized proteins, the cluster of orthologous group COG3253. Using a combination of amino acid sequence analysis, three-dimensional structural studies and biochemical assays, we identified TT1485 as a novel heme-binding protein. The crystal structure reveals that this protein is a pentamer and each monomer exhibits a β-barrel fold. TT1485 is structurally similar to muconolactone isomerase, but this provided no functional clues. Amino acid sequence analysis revealed remote homology to a heme enzyme, chlorite dismutase. Strikingly, amino acid residues that are highly conserved in the homologous hypothetical proteins and chlorite dismutase cluster around a deep cavity on the surface of each monomer. Molecular modeling shows that the cavity can accommodate a heme group with a strictly conserved His as a heme ligand. TT1485 reconstituted with iron protoporphyrin IX chloride gave a low chlorite dismutase activity, indicating that TT1485 catalyzes a reaction other than chlorite degradation. The presence of a possible Fe–His–Asp triad in the heme proximal site suggests that TT1485 functions as a novel heme peroxidase to detoxify hydrogen peroxide within the cell.     

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.     

From protein structure to biochemical function?

Here we describe various methods currently under development aimed at identifying a proteins function from its three-dimensional structure. We are combining a number of these methods to create a pipeline of applications, called ProFunc, which will take a given 3D structure, run all the applications on it and compile and summarise the results obtained. The aim is to provide a best guess as to the proteins function from the evidence provided by the different methods. Here we present three examples, using structures solved by the Midwest Center for Structural Genomics consortium, illustrating the strengths and weaknesses of current approaches.     

结构基因组学研究与核磁共振

各种生物的基因组DNA测序计划的完成,将结构生物学带入了结构基因组学时代.结构基因组学是对所有基因组产物结构的系统性测定,它运用高通量的选择、表达、纯化以及结构测定和计算分析手段,为基因组的每个蛋白质产物提供实验测定的结构或较好的理论模型,这将加速生命科学各个领域的研究.生物信息学、基因工程、结构测定技术等的发展为结构基因组学研究提供了保证.近年来核磁共振在技术方法上的进展,使其成为结构基因组学高通量结构分析中的一个关键方法.     

Protein function prediction using local 3D templates

The prediction of a protein's function from its 3D structure is becoming more and more important as the worldwide structural genomics initiatives gather pace and continue to solve 3D structures, many of which are of proteins of unknown function. Here, we present a methodology for predicting function from structure that shows great promise. It is based on 3D templates that are defined as specific 3D conformations of small numbers of residues. We use four types of template, covering enzyme active sites, ligand-binding residues, DNA-binding residues and reverse templates. The latter are templates generated from the target structure itself and scanned against a representative subset of all known protein structures. Together, the templates provide a fairly thorough coverage of the known structures and ensure that if there is a match to a known structure it is unlikely to be missed. A new scoring scheme provides a highly sensitive means of discriminating between true positive and false positive template matches. In all, the methodology provides a powerful new tool for function prediction to complement those already in use.     

Distinguishing enzyme structures from non-enzymes without alignments

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.     

Crystal structure of a conserved hypothetical protein from Escherichia coli

The crystal structure of a conserved hypothetical protein from Escherichia coli has been determined using X-ray crystallography. The protein belongs to the Cluster of Orthologous Group COG1553 (National Center for Biotechnology Information database, NLM, NIH), for which there was no structural information available until now. Structural homology search with DALI algorism indicated that this protein has a new fold with no obvious similarity to those of other proteins with known three-dimensional structures. The protein quaternary structure consists of a dimer of trimers, which makes a characteristic cylinder shape. There is a large closed cavity with approximate dimensions of 16 Å × 16 Å × 20 Å in the center of the hexameric structure. Six putative active sites are positioned along the equatorial surface of the hexamer. There are several highly conserved residues including two possible functional cysteines in the putative active site. The possible molecular function of the protein is discussed.     

Predicting enzyme class from protein structure without alignments

Methods for predicting protein function from structure are becoming more important as the rate at which structures are solved increases more rapidly than experimental knowledge. As a result, protein structures now frequently lack functional annotations. The majority of methods for predicting protein function are reliant upon identifying a similar protein and transferring its annotations to the query protein. This method fails when a similar protein cannot be identified, or when any similar proteins identified also lack reliable annotations. Here, we describe a method that can assign function from structure without the use of algorithms reliant upon alignments. Using simple attributes that can be calculated from any crystal structure, such as secondary structure content, amino acid propensities, surface properties and ligands, we describe each enzyme in a non-redundant set. The set is split according to Enzyme Classification (EC) number. We combine the predictions of one-class versus one-class support vector machine models to make overall assignments of EC number to an accuracy of 35% with the top-ranked prediction, rising to 60% accuracy with the top two ranks. In doing so we demonstrate the utility of simple structural attributes in protein function prediction and shed light on the link between structure and function. We apply our methods to predict the function of every currently unclassified protein in the Protein Data Bank.