Locating the active sites of enzymes using mechanical properties

We have applied the calculation of mechanical properties to a dataset of almost 100 enzymes to determine the extent to which catalytic residues have distinct properties. Specifically, we have calculated force constants describing the ease of moving any given amino acid residue with respect to the other residues in the protein. The results show that catalytic residues are invariably associated with high force constants. Choosing an appropriate cutoff enables the detection of roughly 80% of catalytic residues with only 25% of false positives. It is shown that neither multidomain structures, nor the presence or absence of bound ligands hinder successful detections. It is however noted that active sites near the protein surface are more difficult to detect and that non-catalytic, but structurally key residues may also exhibit high force constants.     

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Theoretical microscopic titration curves (THEMATICS) is a computational method for the identification of active sites in proteins through deviations in computed titration behavior of ionizable residues. While the sensitivity to catalytic sites is high, the previously reported sensitivity to catalytic residues was not as high, about 50%. Here THEMATICS is combined with support vector machines (SVM) to improve sensitivity for catalytic residue prediction from protein 3D structure alone. For a test set of 64 proteins taken from the Catalytic Site Atlas (CSA), the average recall rate for annotated catalytic residues is 61%; good precision is maintained selecting only 4% of all residues. The average false positive rate, using the CSA annotations is only 3.2%, far lower than other 3D-structure-based methods. THEMATICS-SVM returns higher precision, lower false positive rate, and better overall performance, compared with other 3D-structure-based methods. Comparison is also made with the latest machine learning methods that are based on both sequence alignments and 3D structures. For annotated sets of well-characterized enzymes, THEMATICS-SVM performance compares very favorably with methods that utilize sequence homology. However, since THEMATICS depends only on the 3D structure of the query protein, no decline in performance is expected when applied to novel folds, proteins with few sequence homologues, or even orphan sequences. An extension of the method to predict non-ionizable catalytic residues is also presented. THEMATICS-SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.     

Properties and active center of the thermostable branching enzyme from Bacillus stearothermophilus.

Although the branching enzyme (EC 2.4.1.18) is a member of the alpha-amylase family, the characteristics are not understood. The thermostable branching enzyme gene from Bacillus stearothermophilus TRBE14 was cloned and expressed in Escherichia coli. The branching enzyme was purified to homogeneity, and various enzymatic properties were analyzed by our improved assay method. About 80% of activity was retained when the enzyme was heated at 60 degrees C for 30 min, and the optimum temperature for activity was around 50 degrees C. The enzyme was stable in the range of pH 7.5 to 9.5, and the optimum pH was 7.5. The nucleotide sequence of the gene was determined, and the active center of the enzyme was analyzed by means of site-directed mutagenesis. The catalytic residues were tentatively identified as two Asp residues and a Glu residue by comparison of the amino acid sequences of various branching enzymes from different sources and enzymes of the alpha-amylase family. When the Asp residues and Glu were replaced by Asn and Gln, respectively, the branching enzyme activities disappeared. The results suggested that these three residues are the catalytic residues and that the catalytic mechanism of the branching enzyme is basically identical to that of alpha-amylase. On the basis of these results, four conserved regions including catalytic residues and most of the substrate-binding residues of various branching enzymes are proposed.     

Structure-based identification of catalytic residues

The identification of catalytic residues is an essential step in functional characterization of enzymes. We present a purely structural approach to this problem, which is motivated by the difficulty of evolution-based methods to annotate structural genomics targets that have few or no homologs in the databases. Our approach combines a state-of-the-art support vector machine (SVM) classifier with novel structural features that augment structural clues by spatial averaging and Z scoring. Special attention is paid to the class imbalance problem that stems from the overwhelming number of non-catalytic residues in enzymes compared to catalytic residues. This problem is tackled by: (1) optimizing the classifier to maximize a performance criterion that considers both Type I and Type II errors in the classification of catalytic and non-catalytic residues; (2) under-sampling non-catalytic residues before SVM training; and (3) during SVM training, penalizing errors in learning catalytic residues more than errors in learning non-catalytic residues. Tested on four enzyme datasets, one specifically designed by us to mimic the structural genomics scenario and three previously evaluated datasets, our structure-based classifier is never inferior to similar structure-based classifiers and comparable to classifiers that use both structural and evolutionary features. In addition to the evaluation of the performance of catalytic residue identification, we also present detailed case studies on three proteins. This analysis suggests that many false positive predictions may correspond to binding sites and other functional residues. A web server that implements the method, our own-designed database, and the source code of the programs are publicly available at http://www.cs.bgu.ac.il/～meshi/functionPrediction.     

Evaluation of features for catalytic residue prediction in novel folds

Structural genomics projects are determining the three-dimensional structure of proteins without full characterization of their function. A critical part of the annotation process involves appropriate knowledge representation and prediction of functionally important residue environments. We have developed a method to extract features from sequence, sequence alignments, three-dimensional structure, and structural environment conservation, and used support vector machines to annotate homologous and nonhomologous residue positions based on a specific training set of residue functions. In order to evaluate this pipeline for automated protein annotation, we applied it to the challenging problem of prediction of catalytic residues in enzymes. We also ranked the features based on their ability to discriminate catalytic from noncatalytic residues. When applying our method to a well-annotated set of protein structures, we found that top-ranked features were a measure of sequence conservation, a measure of structural conservation, a degree of uniqueness of a residue's structural environment, solvent accessibility, and residue hydrophobicity. We also found that features based on structural conservation were complementary to those based on sequence conservation and that they were capable of increasing predictor performance. Using a family nonredundant version of the ASTRAL 40 v1.65 data set, we estimated that the true catalytic residues were correctly predicted in 57.0% of the cases, with a precision of 18.5%. When testing on proteins containing novel folds not used in training, the best features were highly correlated with the training on families, thus validating the approach to nonhomologous catalytic residue prediction in general. We then applied the method to 2781 coordinate files from the structural genomics target pipeline and identified both highly ranked and highly clustered groups of predicted catalytic residues.     

The geometry of interactions between catalytic residues and their substrates

The process of deducing the catalytic mechanism of an enzyme from its structure is highly complex and requires extensive experimental work to validate a proposed mechanism. As one step towards improving the reliability of this process, we have gathered statistics describing the typical geometry of catalytic residues with regard to the substrate and one another. In order to analyse residue-substrate interactions, we have assembled a dataset of structures of enzymes of known mechanism bound to substrate, product, or a substrate analogue. Despite the challenges presented in obtaining such experimental data, we were able to include 42 enzyme structures. We have also assembled a separate dataset of catalytic residues which act upon other catalytic residues, using a set of 60 enzyme structures. For both datasets, we have extracted the distances between residues with a given catalytic function and their target moieties. The geometry of residues whose function involves the transfer or sharing of hydrogens (either with substrate or another residue) was analysed more closely. The results showed that the geometry for such productive interactions (prior to the transition state) closely resembles that seen in non-catalytic hydrogen bonds, with distances and angles in the normal expected range. Such statistics provide limits on "expected geometries" for catalytic residues, which will help to identify these residues and elucidate enzyme mechanisms.     

Automatic prediction of catalytic residues by modeling residue structural neighborhood

Background  

Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues.     

Understanding the Functional Roles of Amino Acid Residues in Enzyme Catalysis

The MACiE database contains 223 distinct step-wise enzyme reaction mechanisms and holds representatives from each EC sub-subclass where there is a crystal structure and sufficient evidence in the literature to support a mechanism. Each catalytic step of every reaction sequence in MACiE is fully annotated so that it includes the function of the catalytic residues involved in the reaction and the mechanism by which substrates are transformed into products. Using MACiE as a knowledge base, we have seen that the top 10 most catalytic residues are histidine, aspartate, glutamate, lysine, cysteine, arginine, serine, threonine, tyrosine and tryptophan. Of these only seven (cysteine, histidine, aspartate, lysine, serine, threonine and tyrosine) dominate catalysis and provide essentially five functional roles that are essential. Stabilisation is the most common and essential role for all classes of enzyme, followed by general acid/base (proton acceptor and proton donor) functionality, with nucleophilic addition following closely behind (nucleophile and nucleofuge). We investigated the occurrence of these residues in MACiE and the Catalytic Site Atlas and found that, as expected, certain residue types are associated with each functional role, with some residue types able to perform diverse roles. In addition, it was seen that different EC classes of enzyme have a tendency to employ different residues for catalysis. Further, we show that whilst the differences between EC classes in catalytic residue composition are not immediately obvious from the general classes of Ingold mechanisms, there is some weak correlation between the mechanisms involved in a given EC class and the functions that the catalytic amino acid residues are performing. The analysis presented here provides a valuable insight into the functional roles of catalytic amino acid residues, which may have applications in many aspects of enzymology, from the design of novel enzymes to the prediction and validation of enzyme reaction mechanisms.     

Identification of the principal catalytically important acidic residue of 3-hydroxy-3-methylglutaryl coenzyme A reductase

Kinetic analysis of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase has implicated a glutamate or aspartate residue in (i) formation of mevaldate thiohemiacetal by proton transfer to the carbonyl oxygen of mevaldate and (ii) enhanced ionization of CoASH by the resulting enzyme carboxylate anion, facilitating attack by CoAS- on the carbonyl carbon of mevaldate (Veloso, D., Cleland, W. W., and Porter, J. W. (1981) Biochemistry 81, 887-894). Although neither the identity of this acidic residue nor its location is known, the catalytic domains of 11 sequenced HMG-CoA reductases contain only 3 conserved acidic residues. For HMG-CoA reductase of Pseudomonas mevalonii, these residues are Glu52, Glu83, and Asp183. To identify the acidic residue that functions in catalysis, we generated mutants having alterations in these residues. The mutant proteins were expressed, purified, and characterized. Mutational alteration of residues Glu52 or Asp183 of P. mevalonii HMG-CoA reductase yielded enzymes with significant, but in some cases reduced, activity (Vmax = 100% Asp183----Ala, 65% Asp183----Asn, and 15% Glu52----Gln of wild-type activity, respectively). Although the activity of mutant enzymes Glu52----Gln and Asp183----Ala was undetectable under standard assay conditions, their Km values for substrates were 4-300-fold higher than those for wild-type enzyme. Km values for wild-type enzyme and for mutant enzymes Glu52----Gln and Asp183----Ala were, respectively: 0.41, 73, and 120 mM [R,S)-mevalonate); 0.080, 4.4, and 2.0 mM (coenzyme A); and 0.26, 4.4, and 1.0 mM (NAD+). By these criteria, neither Glu52 nor Asp183 is the acidic catalytic residue although each may function in substrate recognition. During chromatography on coenzyme A agarose or HMG-CoA agarose, mutant enzymes Asp183----Asn and Glu83----Gln behaved like wild-type enzyme. By contrast, and in support of a role for these residues in substrate recognition, mutant enzymes Glu52----Gln and Asp183----Ala exhibited impaired ability to bind to either support. Despite displaying Km values for substrates and chromatographic behavior on substrate affinity supports comparable to wild-type enzyme, only mutant enzyme Glu83----Gln was essentially inactive under all conditions studied (Vmax = 0.2% that of wild-type enzyme). Glutamate residue 83 of P. mevalonii HMG-CoA reductase, and consequently the glutamate of the consensus Pro-Met-Ala-Thr-Thr-Glu-Gly-Cys-Leu-Val-Ala motif of the catalytic domains of eukaryotic HMG-CoA reductases, is judged to be the acidic residue functional in catalysis.     

Rationally selected single-site mutants of the Thermoascus aurantiacus endoglucanase increase hydrolytic activity on cellulosic substrates

Variants of the Thermoascus aurantiacus Eg1 enzyme with higher catalytic efficiency than wild-type were obtained via site-directed mutagenesis. Using a rational mutagenesis approach based on structural bioinformatics and evolutionary analysis, two positions (F16S and Y95F) were identified as priority sites for mutagenesis. The mutant and parent enzymes were expressed and secreted from Pichia pastoris and the single site mutants F16S and Y95F showed 1.7- and 4.0-fold increases in k(cat) and 1.5- and 2.5-fold improvements in hydrolytic activity on cellulosic substrates, respectively, while maintaining thermostability. Similar to the parent enzyme, the two variants were active between pH 4.0 and 8.0 and showed optimal activity at temperature 70°C at pH 5.0. The purified enzymes were active at 50°C for over 12 h and retained at least 80% of initial activity for 2 h at 70°C. In contrast to the improved hydrolysis seen with the single mutation enzymes, no improvement was observed with a third variant carrying a combination of both mutations, which instead showed a 60% reduction in catalytic efficiency. This work further demonstrates that non-catalytic amino acid residues can be engineered to enhance catalytic efficiency in pretreatment enzymes of interest.     

Predicting and annotating catalytic residues: an information theoretic approach.

We introduce a computational method to predict and annotate the catalytic residues of a protein using only its sequence information, so that we describe both the residues' sequence locations (prediction) and their specific biochemical roles in the catalyzed reaction (annotation). While knowing the chemistry of an enzyme's catalytic residues is essential to understanding its function, the challenges of prediction and annotation have remained difficult, especially when only the enzyme's sequence and no homologous structures are available. Our sequence-based approach follows the guiding principle that catalytic residues performing the same biochemical function should have similar chemical environments; it detects specific conservation patterns near in sequence to known catalytic residues and accordingly constrains what combination of amino acids can be present near a predicted catalytic residue. We associate with each catalytic residue a short sequence profile and define a Kullback-Leibler (KL) distance measure between these profiles, which, as we show, effectively captures even subtle biochemical variations. We apply the method to the class of glycohydrolase enzymes. This class includes proteins from 96 families with very different sequences and folds, many of which perform important functions. In a cross-validation test, our approach correctly predicts the location of the enzymes' catalytic residues with a sensitivity of 80% at a specificity of 99.4%, and in a separate cross-validation we also correctly annotate the biochemical role of 80% of the catalytic residues. Our results compare favorably to existing methods. Moreover, our method is more broadly applicable because it relies on sequence and not structure information; it may, furthermore, be used in conjunction with structure-based methods.     

Identification of a mouse short-chain dehydrogenase/reductase gene,retinol dehydrogenase-similar. Function of non-catalytic amino acid residues in enzyme activity

We report a mouse short-chain dehydrogenase/reductase (SDR), retinol dehydrogenase-similar (RDH-S), with intense mRNA expression in liver and kidney. The RDH-S gene localizes to chromosome 10D3 with the SDR subfamily that catalyzes metabolism of retinoids and 3 alpha-hydroxysteroids. RDH-S has no activity with prototypical retinoid/steroid substrates, despite 92% amino acid similarity to mouse RDH1. This afforded the opportunity to analyze for functions of non-catalytic SDR residues. We produced RDH-S Delta 3 by mutating RDH-S to remove an "additional" Asn residue relative to RDH1 in its center, to convert three residues into RDH1 residues (L121P, S122N, and Q123E), and to substitute RDH1 sequence G208FKTCVTSSD for RDH-S sequence F208-FLTGMASSA. RDH-S Delta 3 catalyzed all-trans-retinol and 5 alpha-androstane-3 alpha,17 alpha-diol (3 alpha-adiol) metabolism 60-70% as efficiently (Vm/Km) as RDH1. Conversely, substituting RDH-S sequence F208FLTGMASSA into RDH1 produced a chimera (viz. C3) that was inactive with all-trans-retinol, but was 4-fold more efficient with 3 alpha-adiol. A single RDH1 mutation in the C3 region (K210L) reduced efficiency for all-trans-retinol by >1250-fold. In contrast, the C3 area mutation C212G enhanced efficiency with all-trans-retinol by approximately 2.4-fold. This represents a >6000-fold difference in catalytic efficiency for two enzymes that differ by a single non-catalytic amino acid residue. Another chimera (viz. C5) retained efficiency with all-trans-retinol, but was not saturated and was weakly active with 3 alpha-adiol, stemming from three residue differences (K224Q, K229Q, and A230T). The residues studied contribute to the substrate-binding pocket: molecular modeling indicated that they would affect orientation of substrates with the catalytic residues. These data report a new member of the SDR gene family, provide insight into the function of non-catalytic SDR residues, and illustrate that limited changes in the multifunctional SDR yield major alterations in substrate specificity and/or catalytic efficiency.     

Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods

Calculations of charge interactions complement analysis of a characterised active site, rationalising pH-dependence of activity and transition state stabilisation. Prediction of active site location through large DeltapK(a)s or electrostatic strain is relevant for structural genomics. We report a study of ionisable groups in a set of 20 enzymes, finding that false positives obscure predictive potential. In a larger set of 156 enzymes, peaks in solvent-space electrostatic properties are calculated. Both electric field and potential match well to active site location. The best correlation is found with electrostatic potential calculated from uniform charge density over enzyme volume, rather than from assignment of a standard atom-specific charge set. Studying a shell around each molecule, for 77% of enzymes the potential peak is within that 5% of the shell closest to the active site centre, and 86% within 10%. Active site identification by largest cleft, also with projection onto a shell, gives 58% of enzymes for which the centre of the largest cleft lies within 5% of the active site, and 70% within 10%. Dielectric boundary conditions emphasise clefts in the uniform charge density method, which is suited to recognition of binding pockets embedded within larger clefts. The variation of peak potential with distance from active site, and comparison between enzyme and non-enzyme sets, gives an optimal threshold distinguishing enzyme from non-enzyme. We find that 87% of the enzyme set exceeds the threshold as compared to 29% of the non-enzyme set. Enzyme/non-enzyme homologues, "structural genomics" annotated proteins and catalytic/non-catalytic RNAs are studied in this context.     

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.     

Prediction of catalytic residues in enzymes based on known tertiary structure,stability profile,and sequence conservation

The catalytic or functionally important residues of a protein are known to exist in evolutionarily constrained regions. However, the patterns of residue conservation alone are sometimes not very informative, depending on the homologous sequences available for a given query protein. Here, we present an integrated method to locate the catalytic residues in an enzyme from its sequence and structure. Mutations of functional residues usually decrease the activity, but concurrently often increase stability. Also, catalytic residues tend to occupy partially buried sites in holes or clefts on the molecular surface. After confirming these general tendencies by carrying out statistical analyses on 49 representative enzymes, these data together with amino acid conservation were evaluated. This novel method exhibited better sensitivity in the prediction accuracy than traditional methods that consider only the residue conservation. We applied it to some so-called "hypothetical" proteins, with known structures but undefined functions. The relationships among the catalytic, conserved, and destabilizing residues in enzymatic proteins are discussed.     

Catalysing new reactions during evolution: economy of residues and mechanism

The diversity of function in some enzyme superfamilies shows that during evolution, enzymes have evolved to catalyse different reactions on the same structure scaffold. In this analysis, we examine in detail how enzymes can modify their chemistry, through a comparison of the catalytic residues and mechanisms in 27 pairs of homologous enzymes of totally different functions. We find that evolution is very economical. Enzymes retain structurally conserved residues to aid catalysis, including residues that bind catalytic metal ions and modulate cofactor chemistry. We examine the conservation of residue type and residue function in these structurally conserved residue pairs. Additionally, enzymes often retain common mechanistic steps catalyzed by structurally conserved residues. We have examined these steps in the context of their overall reactions.     

Achieving High Accuracy Prediction of Minimotifs

The low complexity of minimotif patterns results in a high false-positive prediction rate, hampering protein function prediction. A multi-filter algorithm, trained and tested on a linear regression model, support vector machine model, and neural network model, using a large dataset of verified minimotifs, vastly improves minimotif prediction accuracy while generating few false positives. An optimal threshold for the best accuracy reaches an overall accuracy above 90%, while a stringent threshold for the best specificity generates less than 1% false positives or even no false positives and still produces more than 90% true positives for the linear regression and neural network models. The minimotif multi-filter with its excellent accuracy represents the state-of-the-art in minimotif prediction and is expected to be very useful to biologists investigating protein function and how missense mutations cause disease.     

Catalysis and binding in L-ribulose-5-phosphate 4-epimerase: a comparison with L-fuculose-1-phosphate aldolase.

L-Ribulose-5-phosphate (L-Ru5P) 4-epimerase and L-fuculose-1-phosphate (L-Fuc1P) aldolase are evolutionarily related enzymes that display 26% sequence identity and a very high degree of structural similarity. They both employ a divalent cation in the formation and stabilization of an enolate during catalysis, and both are able to deprotonate the C-4 hydroxyl group of a phosphoketose substrate. Despite these many similarities, subtle distinctions must be present which allow the enzymes to catalyze two seemingly different reactions and to accommodate substrates differing greatly in the position of the phosphate (C-5 vs C-1). Asp76 of the epimerase corresponds to the key catalytic acid/base residue Glu73 of the aldolase. The D76N mutant of the epimerase retained considerable activity, indicating it is not a key catalytic residue in this enzyme. In addition, the D76E mutant did not show enhanced levels of background aldolase activity. Mutations of residues in the putative phosphate-binding pocket of the epimerase (N28A and K42M) showed dramatically higher values of K(M) for L-Ru5P. This indicates that both enzymes utilize the same phosphate recognition pocket, and since the phosphates are positioned at opposite ends of the respective substrates, the two enzymes must bind their substrates in a reversed or "flipped" orientation. The epimerase mutant D120N displays a 3000-fold decrease in the value of k(cat), suggesting that Asp120' provides a key catalytic acid/base residue in this enzyme. Analysis of the D120N mutant by X-ray crystallography shows that its structure is indistinguishable from that of the wild-type enzyme and that the decrease in activity was not simply due to a structural perturbation of the active site. Previous work [Lee, L. V., Poyner, R. R., Vu, M. V., and Cleland, W. W. (2000) Biochemistry 39, 4821-4830] has indicated that Tyr229' likely provides the other catalytic acid/base residue. Both of these residues are supplied by an adjacent subunit. Modeling of L-Ru5P into the active site of the epimerase structure suggests that Tyr229' is responsible for deprotonating L-Ru5P and Asp120' is responsible for deprotonating its epimer, D-Xu5P.     

Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces

Analysis of the distances of the exposed residues in 175 enzymes from the centroids of the molecules indicates that catalytic residues are very often found among the 5% of residues closest to the enzyme centroid. This property of catalytic residues is implemented in a new prediction algorithm (named EnSite) for locating the active sites of enzymes and in a new scheme for re-ranking enzyme-ligand docking solutions. EnSite examines only 5% of the molecular surface (represented by surface dots) that is closest to the centroid, identifying continuous surface segments and ranking them by their area size. EnSite ranks the correct prediction 1-4 in 97% of the cases in a dataset of 65 monomeric enzymes (rank 1 for 89% of the cases) and in 86% of the cases in a dataset of 176 monomeric and multimeric enzymes from all six top-level enzyme classifications (rank 1 in 74% of the cases). Importantly, identification of buried or flat active sites is straightforward because EnSite "looks" at the molecular surface from the inside out. Detailed examination of the results indicates that the proximity of the catalytic residues to the centroid is a property of the functional unit, defined as the assembly of domains or chains that form the active site (in most cases the functional unit corresponds to a single whole polypeptide chain). Using the functional unit in the prediction further improves the results. The new property of active sites is also used for re-evaluating enzyme-inhibitor unbound docking results. Sorting the docking solutions by the distance of the interface to the centroid of the enzyme improves remarkably the ranks of nearly correct solutions compared to ranks based on geometric-electrostatic-hydrophobic complementarity scores.     

Accurate sequence-based prediction of catalytic residues

MOTIVATION: Prediction of catalytic residues provides useful information for the research on function of enzymes. Most of the existing prediction methods are based on structural information, which limits their use. We propose a sequence-based catalytic residue predictor that provides predictions with quality comparable to modern structure-based methods and that exceeds quality of state-of-the-art sequence-based methods. RESULTS: Our method (CRpred) uses sequence-based features and the sequence-derived PSI-BLAST profile. We used feature selection to reduce the dimensionality of the input (and explain the input) to support vector machine (SVM) classifier that provides predictions. Tests on eight datasets and side-by-side comparison with six modern structure- and sequence-based predictors show that CRpred provides predictions with quality comparable to current structure-based methods and better than sequence-based methods. The proposed method obtains 15-19% precision and 48-58% TP (true positive) rate, depending on the dataset used. CRpred also provides confidence values that allow selecting a subset of predictions with higher precision. The improved quality is due to newly designed features and careful parameterization of the SVM. The features incorporate amino acids characterized by the highest and the lowest propensities to constitute catalytic residues, Gly that provides flexibility for catalytic sites and sequence motifs characteristic to certain catalytic reactions. Our features indicate that catalytic residues are on average more conserved when compared with the general population of residues and that highly conserved amino acids characterized by high catalytic propensity are likely to form catalytic sites. We also show that local (with respect to the sequence) hydrophobicity contributes towards the prediction.