PREX: PeroxiRedoxin classification indEX, a database of subfamily assignments across the diverse peroxiredoxin family.

PREX (http://www.csb.wfu.edu/prex/) is a database of currently 3516 peroxiredoxin (Prx or PRDX) protein sequences unambiguously classified into one of six distinct subfamilies. Peroxiredoxins are a diverse and ubiquitous family of highly expressed, cysteine-dependent peroxidases that are important for antioxidant defense and for the regulation of cell signaling pathways in eukaryotes. Subfamily members were identified using the Deacon Active Site Profiler (DASP) bioinformatics tool to focus in on functionally relevant sequence fragments surrounding key residues required for protein activity. Searches of this database can be conducted by protein annotation, accession number, PDB ID, organism name or protein sequence. Output includes the subfamily to which each classified Prx belongs, accession and GI numbers, genus and species and the functional site signature used for classification. The query sequence is also presented aligned with a select group of Prxs for manual evaluation and interpretation by the user. A synopsis of the characteristics of members of each subfamily is also provided along with pertinent references.

Analysis of the peroxiredoxin family: using active-site structure and sequence information for global classification and residue analysis.

Peroxiredoxins (Prxs) are a widespread and highly expressed family of cysteine-based peroxidases that react very rapidly with H2O2, organic peroxides, and peroxynitrite. Correct subfamily classification has been problematic because Prx subfamilies are frequently not correlated with phylogenetic distribution and diverge in their preferred reductant, oligomerization state, and tendency toward overoxidation. We have developed a method that uses the Deacon Active Site Profiler (DASP) tool to extract functional-site profiles from structurally characterized proteins to computationally define subfamilies and to identify new Prx subfamily members from GenBank(nr). For the 58 literature-defined Prx test proteins, 57 were correctly assigned, and none were assigned to the incorrect subfamily. The >3500 putative Prx sequences identified were then used to analyze residue conservation in the active site of each Prx subfamily. Our results indicate that the existence and location of the resolving cysteine vary in some subfamilies (e.g., Prx5) to a greater degree than previously appreciated and that interactions at the A interface (common to Prx5, Tpx, and higher order AhpC/Prx1 structures) are important for stabilization of the correct active-site geometry. Interestingly, this method also allows us to further divide the AhpC/Prx1 into four groups that are correlated with functional characteristics. The DASP method provides more accurate subfamily classification than PSI-BLAST for members of the Prx family and can now readily be applied to other large protein families.

Functional site profiling and electrostatic analysis of cysteines modifiable to cysteine sulfenic acid.

Cysteine sulfenic acid (Cys-SOH), a reversible modification, is a catalytic intermediate at enzyme active sites, a sensor for oxidative stress, a regulator of some transcription factors, and a redox-signaling intermediate. This post-translational modification is not random: specific features near the cysteine control its reactivity. To identify features responsible for the propensity of cysteines to be modified to sulfenic acid, a list of 47 proteins (containing 49 known Cys-SOH sites) was compiled. Modifiable cysteines are found in proteins from most structural classes and many functional classes, but have no propensity for any one type of protein secondary structure. To identify features affecting cysteine reactivity, these sites were analyzed using both functional site profiling and electrostatic analysis. Overall, the solvent exposure of modifiable cysteines is not different from the average cysteine. The combined sequence, structure, and electrostatic approaches reveal mechanistic determinants not obvious from overall sequence comparison, including: (1) pKaS of some modifiable cysteines are affected by backbone features only; (2) charged residues are underrepresented in the structure near modifiable sites; (3) threonine and other polar residues can exert a large influence on the cysteine pKa; and (4) hydrogen bonding patterns are suggested to be important. This compilation of Cys-SOH modification sites and their features provides a quantitative assessment of previous observations and a basis for further analysis and prediction of these sites. Agreement with known experimental data indicates the utility of this combined approach for identifying mechanistic determinants at protein functional sites.

An approach to functionally relevant clustering of the protein universe: Active site profile-based clustering of protein structures and sequences.

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results.

Mutations in alpha-helical solvent-exposed sites of eglin c have long-range effects: evidence from molecular dynamics simulations.

Eglin c is a small protease inhibitor whose structural and thermodynamic properties have been well studied. Previous thermodynamic measurements on mutants at solvent-accessible positions in the protein's helix have shown the unexpected result that the data could be best fit by the inclusion of residue- and position-specific parameters to the model. To explore the origins of this surprising result, long molecular dynamics simulations in explicit solvent have been performed. These simulations indicate specific long-range interactions between the solvent-exposed residues in the eglin c alpha-helix and binding loop, an unexpected observation for such a small protein. The residues involved in the interaction are on opposite sides of the protein, about 25 A apart. Simulations of alanine substitutions at the solvent-exposed helix positions, arginine 22, glutamic acid 23, threonine 26, and leucine 27, show both small and large perturbations of eglin c dynamics. Two mutations exhibit large impacts on the long-range helix-loop interactions. Previous stability measurements (Yi et al., Biochemistry 2003;42:7594-7603) had indicated that an alanine substitution at position 27 was less stabilizing than at other solvent-exposed positions in the helix. The L27A mutation effects observed in these simulations suggest that the position-dependent loss of stability measured in wet bench experiments is derived from changes in dynamics that involve long-range interactions; thus, these simulations support the hypothesis that solvent-exposed positions in helices are not always equivalent.

Structure-based active site profiles for genome analysis and functional family subclassification.

In previous work, structure-based functional site descriptors, fuzzy functional forms (FFFs), were developed to recognize structurally conserved active sites in proteins. These descriptors identify members of protein families according to active-site structural similarity, rather than overall sequence or structure similarity. FFFs are defined by a minimal number of highly conserved residues and their three-dimensional arrangement. This approach is advantageous for function assignment across broad families, but is limited when applied to detailed subclassification within these families. In the work described here, we developed a method of three-dimensional, or structure-based, active-site profiling that utilizes FFFs to identify residues located in the spatial environment around the active site. Three-dimensional active-site profiling reveals similarities and differences among active sites across protein families. Using this approach, active-site profiles were constructed from known structures for 193 functional families, and these profiles were verified as distinct and characteristic. To achieve this result, a scoring function was developed that discriminates between true functional sites and those that are geometrically most similar, but do not perform the same function. In a large-scale retrospective analysis of human genome sequences, this profile score was shown to identify specific functional families correctly. The method is effective at recognizing the likely subtype of structurally uncharacterized members of the diverse family of protein kinases, categorizing sequences correctly that were misclassified by global sequence alignment methods. Subfamily information provided by this three-dimensional active-site profiling method yields key information for specific and selective inhibitor design for use in the pharmaceutical industry.

Chemical and structural diversity in cyclooxygenase protein active sites.

A major pharmaceutical problem is designing diverse and selective lead compounds. The human genome sequence provides opportunities to discover compounds that are protein selective if we can develop methods to identify specificity determinants from sequence alone. We have analyzed sequence and structural diversity of sheep COX-1 and mouse COX-2 proteins by Active Site Profiling (ASP). Eleven residues that should serve as specificity determinants between COX-1 and COX-2 were identified; however, the literature suggests that only one has been utilized in structure-based discovery. ASP was used to create a position-specific scoring matrix, which was used to identify possible cross-reacting proteins from the human sequences. This method proved selective for cyclooxygenases, comparing well with results using BLAST. The methods identify a probable misannotation of a cyclooxygenase in which there is high sequence similarity scores using BLAST, but ASP shows it does not contain the residues necessary for cyclooxygenase function. ASP Analysis of human COX proteins suggests that some specificity determinants that distinguish COX-1 and COX-2 proteins are similar between sheep COX-1/mouse COX-2 and human COX-1/COX2; however, residue identities at those positions are not necessarily conserved. Our results lay groundwork for development of family-specific pattern recognition methods to selectively match compounds with proteins.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.

Prediction of deleterious functional effects of amino acid mutations using a library of structure-based function descriptors.

An automated, active site-focused, computational method is described for use in predicting the effects of engineered amino acid mutations on enzyme catalytic activity. The method uses structure-based function descriptors (Fuzzy Functional Forms trade mark or FFFs trade mark ) to automatically identify enzyme functional sites in proteins. Three-dimensional sequence profiles are created from the surrounding active site structure. The computationally derived active site profile is used to analyze the effect of each amino acid change by defining three key features: proximity of the change to the active site, degree of amino acid conservation at the position in related proteins, and compatibility of the change with residues observed at that position in similar proteins. The features were analyzed using a data set of individual amino acid mutations occurring at 128 residue positions in 14 different enzymes. The results show that changes at key active site residues and at highly conserved positions are likely to have deleterious effects on the catalytic activity, and that non-conservative mutations at highly conserved residues are even more likely to be deleterious. Interestingly, the study revealed that amino acid substitutions at residues in close contact with the key active site residues are not more likely to have deleterious effects than mutations more distant from the active site. Utilization of the FFF-derived structural information yields a prediction method that is accurate in 79-83% of the test cases. The success of this method across all six EC classes suggests that it can be used generally to predict the effects of mutations and nsSNPs for enzymes. Future applications of the approach include automated, large-scale identification of deleterious nsSNPs in clinical populations and in large sets of disease-associated nsSNPs, and identification of deleterious nsSNPs in drug targets and drug metabolizing enzymes.

Synergistic computational and experimental proteomics approaches for more accurate detection of active serine hydrolases in yeast.

An analysis of the structurally and catalytically diverse serine hydrolase protein family in the Saccharomyces cerevisiae proteome was undertaken using two independent but complementary, large-scale approaches. The first approach is based on computational analysis of serine hydrolase active site structures; the second utilizes the chemical reactivity of the serine hydrolase active site in complex mixtures. These proteomics approaches share the ability to fractionate the complex proteome into functional subsets. Each method identified a significant number of sequences, but 15 proteins were identified by both methods. Eight of these were unannotated in the Saccharomyces Genome Database at the time of this study and are thus novel serine hydrolase identifications. Three of the previously uncharacterized proteins are members of a eukaryotic serine hydrolase family, designated as Fsh (family of serine hydrolase), identified here for the first time. OVCA2, a potential human tumor suppressor, and DYR-SCHPO, a dihydrofolate reductase from Schizosaccharomyces pombe, are members of this family. Comparing the combined results to results of other proteomic methods showed that only four of the 15 proteins were identified in a recent large-scale, "shotgun" proteomic analysis and eight were identified using a related, but similar, approach (neither identifies function). Only 10 of the 15 were annotated using alternate motif-based computational tools. The results demonstrate the precision derived from combining complementary, function-based approaches to extract biological information from complex proteomes. The chemical proteomics technology indicates that a functional protein is being expressed in the cell, while the computational proteomics technology adds details about the specific type of function and residue that is likely being labeled. The combination of synergistic methods facilitates analysis, enriches true positive results, and increases confidence in novel identifications. This work also highlights the risks inherent in annotation transfer and the use of scoring functions for determination of correct annotations.