Protein Structural Genomics Research Articles

Functional annotation of haloacid dehalogenase superfamily structural genomics proteins.

Haloacid dehalogenases (HAD) are members of a large superfamily that includes many Structural Genomics proteins with poorly characterized functionality. This superfamily consists of multiple types of enzymes that can act as sugar phosphatases, haloacid dehalogenases, phosphonoacetaldehyde hydrolases, ATPases, or phosphate monoesterases. Here, we report on predicted functional annotations and experimental testing by direct biochemical assay for Structural Genomics proteins from the HAD superfamily. To characterize the functions of HAD superfamily members, nine representative HAD proteins and 21 structural genomics proteins are analyzed. Using techniques based on computed chemical and electrostatic properties of individual amino acids, the functions of five structural genomics proteins from the HAD superfamily are predicted and validated by biochemical assays. A dehalogenase-like hydrolase, RSc1362 (Uniprot Q8XZN3, PDB 3UMB) is predicted to be a dehalogenase and dehalogenase activity is confirmed experimentally. Four proteins predicted to be sugar phosphatases are characterized as follows: a sugar phosphatase from Thermophilus volcanium (Uniprot Q978Y6) with trehalose-6-phosphate phosphatase and fructose-6-phosphate phosphatase activity; haloacid dehalogenase-like hydrolase from Bacteroides thetaiotaomicron (Uniprot Q8A2F3; PDB 3NIW) with fructose-6-phosphate phosphatase and sucrose-6-phosphate phosphatase activity; putative phosphatase from Eubacterium rectale (Uniprot D0VWU2; PDB 3DAO) as a sucrose-6-phosphate phosphatase; and hypothetical protein from Geobacillus kaustophilus (Uniprot Q5L139; PDB 2PQ0) as a fructose-6-phosphate phosphatase. Most of these sugar phosphatases showed some substrate promiscuity.

Computed chemical properties for predicting protein function

Most of the 15,000+ reported Structural Genomics (SG) protein structures in the PDB are of unknown or uncertain biochemical function. Here we present a powerful approach based on computed chemical properties of the individual residues in a protein structure. Local arrays of predicted active residues for sets of proteins of known function are matched with those of SG proteins. For instance, a superfamily consists of proteins with similar structure but multiple different kinds of biochemical function, including different types of reactivity as well as different substrate specificities.

Functional Characterization of Structural Genomics Proteins in the Crotonase Superfamily.

Members of the Crotonase superfamily, a mechanistically diverse family of proteins that share a conserved quaternary structure, can often catalyze more than one reaction. However, the spectrum of activity for its members has not been well studied. We report on measured crotonase and hydrolase activity for eight structural genomics (SG) proteins from the Crotonase superfamily plus two previously characterized proteins, intended as controls: human enoyl CoA hydratase (ECH) and Anabaena β-diketone hydrolase. Like most of the 15,000+ SG protein structures deposited in the Protein Data Bank (PDB), the eight SG proteins are of unknown or uncertain biochemical function. The functional characterization of the eight SG proteins is guided by the Structurally Aligned Local Sites of Activity (SALSA), a local-structure-based computational approach to functional annotation. For human ECH, the turnover number for hydrolase activity is threefold higher than that for ECH activity, although the catalytic efficiency is 160-fold higher for ECH. Three SG proteins originally annotated as ECHs were predicted by SALSA to be hydrolases and are observed to have higher catalytic efficiencies for hydrolase activity than for ECH activity, on par with the previously characterized hydrolase. Among the five SG proteins predicted by SALSA to be ECHs, all but one also show some hydrolase activity; all five exhibit lower ECH activity than the human ECH with respect to the crotonyl-CoA substrate. Here, we show examples demonstrating that SALSA can correct functional misannotations even within enzyme families that display promiscuous activity.

Making Functional Predictions Using Local Spatial Arrangements in the Haloacid Dehalogenase Superfamily

Beginning with the sequencing of the human genome in the 1990s, major efforts have been underway to expand significantly upon our current working knowledge of proteins. Between the years 2000–2015, the Protein Structure Initiative (PSI) solved 15,000+ protein structures, but most of them have unknown or uncertain biochemical function or have an incorrect assigned putative function. With all these structures, methodology must be developed to functionally annotate these proteins in order to identify potential applications, such as bioremediation and understanding cellular processes. Enzymes in the Haloacid Dehalogenase Superfamily (HADSF) possess a wide range of functions, including phosphatases important in cell membrane biosynthesis and dehalogenases that possesses the ability to detoxify and degrade halogenated compounds. The Ondrechen Research Group (ORG) at Northeastern University has successfully developed methodology that is being used to predict biochemical function for Structural Genomics (SG) proteins in the HADSF. These methods are Partial Order Optimum Likelihood (POOL) and Structurally Aligned Local Sites of Activity (SALSA). POOL is a machine learning method that uses the electrostatic features and metrics from THEoreticalMicroscope Anomalous TItration Curve Shapes (THEMATICS), ligand binding pocket and geometric features from ConCavity, and the evolutionary scores and phylogenetic trees from INformation‐theoretic TREe traversal for Protein functional site IDentification (INTREPID) to make the predictions about which residues are catalytically active or otherwise important for protein function. SALSA uses the functional residue predictions for SG proteins obtained from POOL and aligns them with consensus signatures from known enzyme subfamilies according to the local spatial arrangement of predicted residues at the active site. So far for the HAD superfamily, using SALSA we have made predictions for 20 SG proteins: one dehalogenase, eight sugar phosphatases, three NagD‐like phosphatases, four P‐Type ATPases, and four soluble epoxide hydrolases. These predictions will be experimentally validated by biochemical assays to establish the function of each protein and to verify our computational approach to protein function prediction. The ability to predict computationally the biochemical function of protein structures of unknown or uncertain function adds tremendous value to genomics data.Support or Funding InformationThis project is funded by NSF CHE‐1305655, CHE‐1905214, and CHE‐1757078.

Thinking Outside the Informatics Box: Computed Chemical Properties for Protein Function Annotation

There are now over 14,000 Structural Genomics (SG) protein structures deposited in the Protein Data Bank (PDB) and most of these are of unknown or uncertain biochemical function. Reliable computational methods for the prediction of the function of protein structures is an important current need. Typically, functions are assigned using informatics‐based approaches. The annotation of protein function by automated means has led to high rates of misannotations in some databases. Here we present a complementary and powerful approach based on computed chemical properties of the individual residues in a protein structure. Partial Order Optimum Likelihood (POOL) is used to predict the residues in the query protein structure that are important for catalysis. Typically these include the residues in the first layer that make direct contact with the substrate molecule(s) and also some residues in the second and third layers that play supporting roles in the catalytic process. Then, for proteins of known biochemical function, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func) establishes local arrays of POOL‐predicted residues that are common to proteins of the same function. Then local arrays of POOL‐predicted residues of the query (SG) protein are aligned with those of the proteins of known function for the different functional types. These alignments, each SG protein against each functional family, are scored in order to predict the most likely function of the SG proteins. Results are reported for the SG members of the Ribulose Phosphate Binding Barrel (RPBB), Clp‐Crotonase, and Haloacid Dehalogenase superfamilies. While we find the SG proteins in the RPBB superfamily to be well annotated, we predict very high annotation error rates (about 70%) in the Clp‐Crotonase superfamily. Of particular interest are cases of predicted misannotation, where our prediction differs from that of the assigned function. Experimental testing of our predictions is performed by direct biochemical assays to verify our predictions.Support or Funding InformationNational Science Foundation under grant number CHE‐1305655, MathWorks, Inc. and a PhRMA Foundation Fellowship awarded to CLM.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Functional classification of protein structures by local structure matching in graph representation.

As a result of high‐throughput protein structure initiatives, over 14,400 protein structures have been solved by Structural Genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP‐Func method to our previously reported method, Structurally Aligned Local Sites of Activity (SALSA), using the Ribulose Phosphate Binding Barrel (RPBB), 6‐Hairpin Glycosidase (6‐HG), and Concanavalin A‐like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP‐Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP‐Func methods to predict function. Forty‐one SG proteins in the RPBB superfamily, nine SG proteins in the 6‐HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community.

Functional assignment of Structural Genomics proteins through computed chemical properties, graph representation of active sites, and biochemical validation

There are currently over 14,300 Structural Genomics (SG) protein structures deposited in the PDB by protein structure initiatives. However, most of these SG proteins have unknown or putative function annotations. This accumulated structural information represents a tremendous contribution to structural biology and genomics. Still, the addition of accurate functional annotations for these SG proteins would add substantial value to this information. Our approach to functional annotation and validation incorporates predicting functional assignments through structure‐based computed chemical properties and local structure matching followed by biochemical validation. This research focuses on four superfamilies: Crotonase, Ribulose Phosphate Binding Barrel, 6‐Hairpin Glycosidase, and Concanavlin A‐like Lectins and Glucanases. First, Partial Order Optimum Likelihood (POOL) is used to predict computationally the catalytically important residues in each protein structure. Next, Structurally Aligned Local Sites of Activity (SALSA) develops spatially‐localized consensus signatures for the proteins of known function in each functional family within each superfamily based on POOL‐predicted residues and functionally characterized residues of importance. Then, the POOL‐predicted residues for each SG protein are compared to each consensus signature and scored to determine their degree of similarity at the local active site. Finally, we introduce a new, computationally faster method for sorting protein superfamilies and annotating protein function using local structure matching in graph representation: Graph Representation of Active Sites for Prediction of Function (GRASP‐Func). Sets of tetrahedra are generated through Delaunay triangulation for each protein structure using the alpha carbon atoms of each residue. Then, sets of proteins with matched tetrahedra are grouped together and images are generated showing the relationship of each protein (node) and its neighbors (edges) with similar active sites. We compare SALSA and GRASP‐Func and show that both methods correctly sort the superfamilies into their respective functional families. Both methods also make similar functional predictions for the SG proteins, with GRASP‐Func performing in far less time. Thus GRASP‐Func enables large‐scale comparisons and functional assignments within and across superfamilies. Finally, we are able to test these predictions biochemically to confirm function. Biochemical data for the Crotonase Superfamily show that while proteins have some promiscuous functionality, our methods predict the correct dominant function for each protein tested. The goal of this project is to provide a validated approach to functional annotation to enable applications from drug target identification to green chemistry and biofuel production.Support or Funding InformationSupport from NSF‐CHE‐1305655, NSF‐MCB‐1158176, NSF‐MCB‐1517290, PhRMA Foundations (Predoctoral Fellowship in Informatics awarded to CLM), NSF‐GRFP (JSL), MathWorks, Inc., and American Cancer Society Research Scholar Grant RSG‐12‐161‐01‐DMC (PJB).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Characterizing the Functions of Structural Genomics Proteins through Computed Chemical Properties and Experimental Chemistry

As of November 2016, the Protein Structure Initiative resulted in nearly 14,000 Structural Genomics (SG) protein structures deposited in the PDB. Initially it was believed that structural information would reveal the function of these proteins, but this proved to be more difficult than expected. Now we are left with proteins of unknown or uncertain functions or putative functions that are often incorrect. This accumulated structural information constitutes a tremendous contribution to structural biology and genomics. However, the addition of better functional predictions for SG proteins would add substantial value to the information already available. Our approach incorporates structure‐based computed chemical properties through local site prediction followed by local structural matching. First, Partial Order Optimum Likelihood (POOL) is a machine learning method used to predict computationally the residues important for catalysis in a given protein structure. Then, Structurally Aligned Local Sites of Activity (SALSA) analyzes a superfamily and develops spatially‐localized consensus signatures for the proteins of known function in each functional family based on POOL‐predicted residues, functionally characterized residues of importance, and multiple structure alignments. The POOL‐predicted residues for the SG proteins are then compared to the consensus signatures and the alignments are scored to determine the degree of similarity between the local structures.This research focuses on two enzyme superfamilies: the Crotonase Superfamily and the 6‐Hairpin Glycosidase Superfamily. For the Crotonase Superfamily, we have computationally determined that many SG proteins are misannotated. In some cases, we are able to provide a better putative functional annotation for the SG proteins. We have also acquired experimental data supporting our predictions as well as proof of promiscuity within the superfamily. For example, we have assayed proteins that exhibit both hydrolase and hydratase activity, albeit higher activity for one function over the other. SG protein probable enoyl‐CoA hydratase from Thermus thermophilus (PDB 1WZ8) is predicted by our methods to be a hydrolase and we have demonstrated this activity as well as a lower hydratase activity in biochemical assays. For the 6‐Hairpin Glycosidase Superfamily, we have computationally determined a number of functional assignments for SG proteins in this superfamily, including substrate specificity. For example, SG protein putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY) is shown through general bioinformatics analysis to be a fucosidase. However, due to the difference of a key substrate recognition residue in the local active site, our method predicts galactosidase activity instead. The chemical reactions are the same in both cases, but with different substrates. The goal of this research project is to provide a validated approach to functional annotation for wider application by the community, including drug discovery, controlling antibiotic‐resistant bacteria, alternative energy technology, and biofuel production.Support or Funding InformationSupport from NSF‐CHE‐1305655 and the PhRMA Foundation (predoctoral fellowship in informatics awarded to CLM).

Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases

Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional information for limited sets of SG proteins, the biochemical functions for most SG proteins are still unknown or uncertain. Therefore, computational methods for the reliable prediction of protein function from structure can add tremendous value to the existing SG data. In this article, we show how computational methods may be used to predict the function of SG proteins, using examples from the six-hairpin glycosidase (6-HG) and the concanavalin A-like lectin/glucanase (CAL/G) superfamilies. Using a set of predicted functional residues, obtained from computed electrostatic and chemical properties for each protein structure, it is shown that these superfamilies may be sorted into functional families according to biochemical function. Within these superfamilies, a total of 18 SG proteins were analyzed according to their predicted, local functional sites: 13 from the 6-HG superfamily, five from the CAL/G superfamily. Within the 6-HG superfamily, an uncharacterized protein BACOVA_03626 from Bacteroides ovatus (PDB 3ON6) and a hypothetical protein BT3781 from Bacteroides thetaiotaomicron (PDB 2P0V) are shown to have very strong active site matches with exo-α-1,6-mannosidases, thus likely possessing this function. Also in this superfamily, it is shown that protein BH0842, a putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY), has a predicted active site that matches well with a known α-l-galactosidase. In the CAL/G superfamily, an uncharacterized glycosyl hydrolase family 16 protein from Mycobacterium smegmatis (PDB 3RQ0) is shown to have local structural similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given.

Functional Characterization of Structural Genomics Proteins in the Crotonase Superfamily

There are over 13,000 Structural Genomics (SG) proteins deposited in the PDB. However, most of these SG proteins are of unknown or uncertain biochemical function. Although many SG proteins have putative functional assignments, they are often incorrect. The Crotonase Superfamily consists of five diverse functional subgroups that are well characterized structurally and functionally, representing different types of reactivity: hydratase, isomerase, and dehalogenase activities. This superfamily also contains at least 70 SG proteins, so it is ideal to test predictions of protein function. Our approach is based on local structure matching at the computationally predicted active site. First, Partial Order Optimum Likelihood (POOL) is used to predict the functionally important residues of each SG protein and of the proteins of known function in the superfamily. Next, Structurally Aligned Local Site of Activity (SALSA) is used to align the predicted catalytic residues of the well characterized members in the superfamily. From this analysis we generate chemical signatures for each functional subgroup and compare them to the sets of catalytic residues predicted for the SG proteins. We demonstrate based on these computational methods that the majority of the putative annotations in this superfamily are likely incorrect. Currently, biochemical assays are being used to test these predictions. Preliminary biochemical results show that one SG protein classified as a probable enoyl-CoA hydratase possesses hydrolase activity as predicted by our methods. The outcome of this project will be to successfully classify the biochemical functions of SG proteins based on their local structure at the predicted active sites. This work is supported by NSF-CHE-1305655.

A Critical Evaluation of in silico Methods for Detection of Membrane Protein Intrinsic Disorder

Intrinsically disordered regions in proteins possess important biological roles including transcriptional regulation, molecular recognition, and provision of sites for posttranslational modification. In three-dimensional crystallization of both soluble and membrane proteins, identification and removal of disordered regions is often necessary for obtaining crystals possessing sufficient long-range order for structure determination. Disordered regions can be identified experimentally, with techniques such as limited proteolysis coupled with mass spectrometry, or computationally, by using disorder prediction programs, of which many are available. Although these programs use various methods to predict disorder from a protein’s primary sequence, they all were developed using information derived from soluble protein structures. Therefore, their performance and accuracy when applied to integral membrane proteins remained an open question. We evaluated the performance of 13 disorder prediction programs on a dataset containing 343 membrane proteins, and upon subdatasets containing only α-helical or β-barrel proteins. These programs were ranked using multiple metrics, including metrics specifically created for membrane proteins. Analysis of these data shows a clear distinction between programs that accurately predict disordered regions in membrane proteins and programs which perform poorly, and allows for the robust integration of in silico disorder prediction into our PSI:Biology membrane protein structural genomics pipeline.

Using the Structurally Aligned Local Sites of Activity (SALSAs) computational method to determine biochemical function of structural genomics proteins

In the Protein Data Bank, there are over 84,600 deposited structures and 11,800 of those are structural genomics (SG) proteins. The functional annotations of large number of solved SG structures are uncertain or incorrect, due to assignments based on sequence or overall structure comparison. The present method to predict function, Structurally Aligned Local Sites of Activity (SALSAs), uses functional active site predictors, Theoretical Microscopic Anomalous Titration Curve Shapes (THEMATICS) and Partial Order Optimum Likelihood (POOL), to first determine the important active site residues from the 3D structure. Second, these local active site structures are aligned to compare proteins of known function with SG proteins. Third, those matches are assigned scores with a similarity scoring matrix to assign correct annotation. We apply this method to the ribulose‐phosphate binding barrel (RPBB) superfamily and annotate twenty‐eight SG proteins. The superfamily was divided into eight functional subclasses and their active sites were differentiated along the eight beta‐sheets. The location of the active site and residues were correctly predicted for all protein structures.

Protein function annotation with Structurally Aligned Local Sites of Activity (SALSAs)

BackgroundThe prediction of biochemical function from the 3D structure of a protein has proved to be much more difficult than was originally foreseen. A reliable method to test the likelihood of putative annotations and to predict function from structure would add tremendous value to structural genomics data. We report on a new method, Structurally Aligned Local Sites of Activity (SALSA), for the prediction of biochemical function based on a local structural match at the predicted catalytic or binding site.ResultsImplementation of the SALSA method is described. For the structural genomics protein PY01515 (PDB ID 2aqw) from Plasmodium yoelii, it is shown that the putative annotation, Orotidine 5'-monophosphate decarboxylase (OMPDC), is most likely correct. SALSA analysis of YP_001304206.1 (PDB ID 3h3l), a putative sugar hydrolase from Parabacteroides distasonis, shows that its active site does not bear close resemblance to any previously characterized member of its superfamily, the Concanavalin A-like lectins/glucanases. It is noted that three residues in the active site of the thermophilic beta-1,4-xylanase from Nonomuraea flexuosa (PDB ID 1m4w), Y78, E87, and E176, overlap with POOL-predicted residues of similar type, Y168, D153, and E232, in YP_001304206.1. The substrate recognition regions of the two proteins are rather different, suggesting that YP_001304206.1 is a new functional type within the superfamily. A structural genomics protein from Mycobacterium avium (PDB ID 3q1t) has been reported to be an enoyl-CoA hydratase (ECH), but SALSA analysis shows a poor match between the predicted residues for the SG protein and those of known ECHs. A better local structural match is obtained with Anabaena beta-diketone hydrolase (ABDH), a known β-diketone hydrolase from Cyanobacterium anabaena (PDB ID 2j5s). This suggests that the reported ECH function of the SG protein is incorrect and that it is more likely a β-diketone hydrolase.ConclusionsA local site match provides a more compelling function prediction than that obtainable from a simple 3D structure match. The present method can confirm putative annotations, identify misannotation, and in some cases suggest a more probable annotation.

The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods

The Protein Structure Initiative’s Structural Biology Knowledgebase (SBKB, URL: http://sbkb.org) is an open web resource designed to turn the products of the structural genomics and structural biology efforts into knowledge that can be used by the biological community to understand living systems and disease. Here we will present examples on how to use the SBKB to enable biological research. For example, a protein sequence or Protein Data Bank (PDB) structure ID search will provide a list of related protein structures in the PDB, associated biological descriptions (annotations), homology models, structural genomics protein target status, experimental protocols, and the ability to order available DNA clones from the PSI:Biology-Materials Repository. A text search will find publication and technology reports resulting from the PSI’s high-throughput research efforts. Web tools that aid in research, including a system that accepts protein structure requests from the community, will also be described. Created in collaboration with the Nature Publishing Group, the Structural Biology Knowledgebase monthly update also provides a research library, editorials about new research advances, news, and an events calendar to present a broader view of structural genomics and structural biology.

The Structural Biology Knowledgebase - search Online for Protein Sequences, Structures, Functions, Methods and More

The Structural Biology Knowledgebase (SBKB, URL: http://sbkb.org) is a free online resource designed to combine all protocols and results of the structural genomics and structural biology efforts with information from the biological community in order to have a better understanding living systems and disease. We will present examples of how to navigate the SBKB and how to use it to enable biological research. For example, a protein sequence or PDB ID search will provide a list of protein structures from the Protein Data Bank, associated biological descriptions (annotations), homology models, structural genomics protein target information, experimental protocols, and the ability to order available DNA clones. A text search will find technology reports and publications that were created by the Protein Structure Initiative's high-throughput research efforts. Web tools that aid in bench top research, such as the Sequence Comparison and Analysis tool for protein construct design, will also be demonstrated. Created in collaboration with the Nature Publishing Group, the Structural Biology Knowledgebase Gateway provides a research library, editorials about new research advances, news, and an events calendar also present a broader view of structural genomics and structural biology.The SBKB is funded by the Protein Structure Initiative/NIGMS.

FUNCTIONAL CLASSIFICATION OF PROTEIN 3D STRUCTURES FROM PREDICTED LOCAL INTERACTION SITES

A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.

Characteristic Peptides of Protein Secondary Structural Motifs

Characteristic peptides of the protein segments having common secondary folds are obtained for the I-sites library using maximal position specific probability scores. The secondary structures of these peptides are predicted deploying two best-known computational methods. These are validated with significant accuracy against the corresponding motifs. The characteristic peptides also match with those computed using a Bayesian modeling approach with Markov Chain Monte Carlo Simulation. Percentage representation of the characteristic peptides in the protein structural and functional families shows some interesting results with potential applications in protein structural genomics.

Structural motif screening reveals a novel, conserved carbohydrate-binding surface in the pathogenesis-related protein PR-5d

BackgroundAromatic amino acids play a critical role in protein-glycan interactions. Clusters of surface aromatic residues and their features may therefore be useful in distinguishing glycan-binding sites as well as predicting novel glycan-binding proteins. In this work, a structural bioinformatics approach was used to screen the Protein Data Bank (PDB) for coplanar aromatic motifs similar to those found in known glycan-binding proteins.ResultsThe proteins identified in the screen were significantly associated with carbohydrate-related functions according to gene ontology (GO) enrichment analysis, and predicted motifs were found frequently within novel folds and glycan-binding sites not included in the training set. In addition to numerous binding sites predicted in structural genomics proteins of unknown function, one novel prediction was a surface motif (W34/W36/W192) in the tobacco pathogenesis-related protein, PR-5d. Phylogenetic analysis revealed that the surface motif is exclusive to a subfamily of PR-5 proteins from the Solanaceae family of plants, and is absent completely in more distant homologs. To confirm PR-5d's insoluble-polysaccharide binding activity, a cellulose-pulldown assay of tobacco proteins was performed and PR-5d was identified in the cellulose-binding fraction by mass spectrometry.ConclusionsBased on the combined results, we propose that the putative binding site in PR-5d may be an evolutionary adaptation of Solanaceae plants including potato, tomato, and tobacco, towards defense against cellulose-containing pathogens such as species of the deadly oomycete genus, Phytophthora. More generally, the results demonstrate that coplanar aromatic clusters on protein surfaces are a structural signature of glycan-binding proteins, and can be used to computationally predict novel glycan-binding proteins from 3 D structure.

High Conservation of Amino Acids with Anomalous Protonation Behavior

The determination of a proteins biochemical function from its 3D structure has proved more difficult than anticipated for structural genomics proteins, most of which are of unknown or uncertain function. Functional annotations typically have been assigned using the closest sequence or structure match, a practice that has resulted in large numbers of misannotated proteins. Recently it was reported that computed protonation properties can be used to predict the residues with catalytic and binding activity, thus providing clues about the function of the protein. We show that residues with anomalous computed protonation behavior constitute a small fraction of the proteins highly conserved residues. Results for a test set of 61 proteins reveal that the average conservation scores are high for residues with unusual protonation behavior, even for many not annotated as functionally important in the literature. Two enzymes, protein tyrosine phosphatase from Yersinia enterocolitica and glucosamine-6-phosphate deaminase from Escherichia coli, are described in detail as examples to illustrate the relationship between anomalous protonation behavior and conservation. We conclude that the residues with anomalous protonation behavior are generally highly conserved, but are fewer in number and more spatially localized than the set of all highly conserved residues in a given protein. Keywords: Protonation states, functional residues, sequence conservation, function annotation, THEMATICS

HOW TO USE THE PSI STRUCTURAL GENOMICS KNOWLEDGEBASE TO ENABLE RESEARCH

The Protein Structure Initiative Structural Genomics Knowledgebase (PSI SGKB, URL: http://kb.psi‐structuralgenomics.org) is a web resource designed to turn the products of the structural genomics and structural biology efforts into knowledge that can be used by the biological community to understand living systems and disease. This workshop will present examples of how to use the PSI SGKB to enable biological research. For example, a protein sequence or PDB ID search will provide a list of protein structures from the Protein Data Bank, associated biological descriptions (annotations), homology models, structural genomics protein target information, experimental protocols, and the ability to order available DNA clones. A text search will find technology reports and publications that were created by the PSI's high‐throughput research efforts. Web tools that aid in bench top research, such as protein construct design, are also available. Created in collaboration with the Nature Publishing Group, the Structural Genomics Knowledgebase Gateway provides a research library, editorials about new research advances, news, and an events calendar also present a broader view of structural genomics and structural biology. The PSI SGKB is funded by the NIGMS.