Identify catalytic triads of serine hydrolases by support vector machines

The core of an enzyme molecule is its active site from the viewpoints of both academic research and industrial application. To reveal the structural and functional mechanism of an enzyme, one needs to know its active site; to conduct structure-based drug design by regulating the function of an enzyme, one needs to know the active site and its microenvironment as well. Given the atomic coordinates of an enzyme molecule, how can we predict its active site? To tackle such a problem, a distance group approach was proposed and the support vector machine algorithm applied to predict the catalytic triad of serine hydrolase family. The success rate by jackknife test for the 139 serine hydrolases was 85%, implying that the method is quite promising and may become a useful tool in structural bioinformatics.     

Location and volume of the active site of chymotrypsin

The active site of chymotrypsin molecule (approximated by a sphere with radius of 20 A) was taken as the largest cavity on the enzyme surface. The volume inside the approximating sphere is sufficient for placement of 95% of non-hydrogen atoms of the enzyme. The active site cavity is localized in a spherical sector with solid angle of 80 degrees whose axis passes through the CB-atom of the Ser195 residue. The volume of the active site cavity is about 2700 A(3) (8% of the volume of the approximating sphere) as computed by the Monte-Carlo method from known X-ray data. The size and shape of the active site cavity is sufficient for entrance of significantly large fragments (more than 60 non-hydrogen atoms) of the substrate molecule. At the active site cavity bottom, there is a narrow compartment adjacent to an oxy-anion hollow and accessible to water but not to substrate molecules. The water molecules inside this narrow compartment can take part in heat exchange with the external medium during different steps of the enzymatic process.     

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.     

FTSite: high accuracy detection of ligand binding sites on unbound protein structures

MOTIVATION: Binding site identification is a classical problem that is important for a range of applications, including the structure-based prediction of function, the elucidation of functional relationships among proteins, protein engineering and drug design. We describe an accurate method of binding site identification, namely FTSite. This method is based on experimental evidence that ligand binding sites also bind small organic molecules of various shapes and polarity. The FTSite algorithm does not rely on any evolutionary or statistical information, but achieves near experimental accuracy: it is capable of identifying the binding sites in over 94% of apo proteins from established test sets that have been used to evaluate many other binding site prediction methods. AVAILABILITY: FTSite is freely available as a web-based server at http://ftsite.bu.edu.     

Using pseudo amino acid composition to predict protease families by incorporating a series of protein biological features

Proteases are essential to most biological processes though they themselves remain intact during the processes. In this research, a computational approach was developed for predicting the families of proteases based on their sequences. According to the concept of pseudo amino acid composition, in order to catch the essential patterns for the sequences of proteases, the sample of a protein was formulated by a series of its biological features. There were a total of 132 biological features, which were sourced from various biochemical and physicochemical properties of the constituent amino acids. The importance of these features to the prediction is rated by Maximum Relevance Minimum Redundancy algorithm and then the Incremental Feature Selection was applied to select an optimal feature set, which was used to construct a predictor through the nearest neighbor algorithm. As a demonstration, the overall success rate by the jackknife test in identifying proteases among their seven families was 92.74%. It was revealed by further analysis on the optimal feature set that the secondary structure and amino acid composition play the key roles for the classification, which is quite consistent with some previous findings. The promising results imply that the predictor as presented in this paper may become a useful tool for studying proteases.     

The porphobilinogen synthase catalyzed reaction mechanism

Porphobilinogen synthase (PBGS) catalyzes the first common reaction in the biosynthesis of the tetrapyrroles, the asymmetric condensation of two molecules of delta-aminolevulinic acid to form porphobilinogen. There is a variable requirement for an essential active site zinc that necessitates consideration of PBGS as an enzyme that may exhibit phylogenetic diversity in its chemical reaction mechanism. Recent crystal structures suggest reaction mechanisms that involve two covalent Schiff base linkages between adjacent active site lysine residues and each of the two substrate molecules. The reaction appears to stall at a covalently bound almost-product intermediate that is poised for breakdown to product upon binding of a substrate molecule to an adjacent active site and a subsequent conformational change.     

Dihydrolipoamide dehydrogenase moonlighting activity as a DNA chelating agent

Dihydrolipoamide dehydrogenase (DLDH) is a mitochondrial enzyme that comprises an essential component of the pyruvate dehydrogenase complex. Lines of evidence have shown that many dehydrogenases possess unrelated actions known as moonlightings in addition to their oxidoreductase activity. As part of these activities, we have demonstrated that DLDH binds TiO₂ as well as produces reactive oxygen species (ROS). This ROS production capability was harnessed for cancer therapy via integrin‐mediated drug‐delivery of RGD‐modified DLDH (DLDH^RGD), leading to apoptotic cell death. In these experiments, DLDH^RGD not only accumulated in the cytosol but also migrated to the cell nuclei, suggesting a potential DNA‐binding capability of this enzyme. To explore this interaction under cell‐free conditions, we have analyzed DLDH binding to phage lambda (λ) DNA by gel‐shift assays and analytic ultracentrifugation, showing complex formation between the two, which led to full coverage of the DNA molecule with DLDH molecules. DNA binding did not affect DLDH enzymatic activity, indicating that there are neither conformational changes nor active site hindering in DLDH upon DNA‐binding. A Docking algorithm for prediction of protein‐DNA complexes, Paradoc, identified a putative DNA binding site at the C‐terminus of DLDH. Our finding that TiO₂‐bound DLDH failed to form a complex with DNA suggests partial overlapping between the two sites. To conclude, DLDH binding to DNA presents a novel moonlight activity which may be used for DNA alkylating in cancer treatment.     

Molecular dynamics studies of alanine racemase: a structural model for drug design

Alanine racemase (AlaR) is a bacterial enzyme that catalyzes the interconversion of L- and D-alanine, which is an essential constituent of the peptidoglycan layer of the bacterial cell wall and requires pyridoxal 5'-phosphate (PLP) as a cofactor. The enzyme is universal to bacteria, including mycobacteria, making it an attractive target for drug design. To investigate the effects of flexibility on the binding modes of the substrate and an inhibitor and to analyze how the active site is affected by the presence of the substrate versus inhibitor, a molecular dynamics simulation on the full AlaR dimer from Bacillus stearothermophilus (pdb code: 1SFT) with a D-alanine molecule in one active site and the noncovalent inhibitor, propionate, in the second site has been carried out. Within the time scale of the simulation, we show that the active site becomes more stabilized in the presence of substrate versus inhibitor. The results of this simulation are in agreement with the proposed mechanism of alanine racemase reaction in which the substrate carboxyl group directly participates in the catalysis by acting cooperatively with Tyr 265' and Lys 39. A structural water molecule in contact with both substrate and inhibitor (i.e., in both active sites) and bridging residues in both active sites was identified. It shows a remarkably low mobility and does not exchange with bulk water. This water molecule can be taken into account for the design of specific AlaR inhibitors by either utilizing it as a bridging group or displacing it with an inhibitor atom. The results presented here provide insights into the dynamics of the alanine racemase in the presence of substrate/inhibitor, which will be used for the rational design of novel inhibitors.     

Structure-based design of guanosine analogue inhibitors targeting GTP cyclohydrolase IB towards a new class of antibiotics

GTP cyclohydrolase (GCYH-I) is an enzyme in the folate biosynthesis pathway that has not been previously exploited as an antibiotic target, although several pathogens including N. gonorrhoeae use a form of the enzyme GCYH-IB that is structurally distinct from the human homologue GCYH-IA. A comparison of the crystal structures of GCYH-IA and -IB with the nM inhibitor 8-oxo-GTP bound shows that the active site of GCYH-IB is larger and differently shaped. Based on this structural information, we designed and synthesized a small set of 8-oxo-G derivatives with ether linkages at O⁶ and O⁸ expected to displace water molecules from the expanded active site of GCYH-IB. The most potent of these compounds, G3, is selective for GCYH-IB, supporting the premise that potent and selective inhibitors of GCYH-IB could constitute a new class of small molecule antibiotics.     

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.     

Crystal structure of phosphatidylglycerophosphatase (PGPase), a putative membrane-bound lipid phosphatase, reveals a novel binuclear metal binding site and two "proton wires"

Phosphatidylglycerophosphatase (PGPase), an enzyme involved in lipid metabolism, catalyzes formation of phosphatidylglycerol from phosphatidylglycerophosphate. Phosphatidylglycerol is a multifunctional phospholipid, found in the biological membranes of many organisms. Here, we report the crystal structure of Listeria monocytogenes PGPase at 1.8 A resolution. PGPase, an all-helical molecule, forms a homotetramer. Each protomer contains an independent active site with two metal ions, Ca(2+) and Mg(2+), forming a hetero-binuclear center located in a hydrophilic cavity near the surface of the molecule. The binuclear center, conserved ligands, metal-bound water molecules, and an Asp-His dyad form the active site. The catalytic mechanism of this enzyme is likely to proceed via binuclear metal activated nucleophilic water. The binuclear metal-binding active-site environment of this structure should provide insights into substrate binding and metal-dependent catalysis. A long channel with inter-linked linear water chains, termed "proton wires," is observed at the tetramer interface. Comparison of similar water chain structures in photosynthetic reaction centers (RCs), Cytochrome f, gramicidin, and bacteriorhodopsin, suggests that PGPase may conduct protons via proton wires.     

Sequence-based enzyme catalytic domain prediction using clustering and aggregated mutual information content

Characterizing enzyme sequences and identifying their active sites is a very important task. The current experimental methods are too expensive and labor intensive to handle the rapidly accumulating protein sequences and structure data. Thus accurate, high-throughput in silico methods for identifying catalytic residues and enzyme function prediction are much needed. In this paper, we propose a novel sequence-based catalytic domain prediction method using a sequence clustering and an information-theoretic approaches. The first step is to perform the sequence clustering analysis of enzyme sequences from the same functional category (those with the same EC label). The clustering analysis is used to handle the problem of widely varying sequence similarity levels in enzyme sequences. The clustering analysis constructs a sequence graph where nodes are enzyme sequences and edges are a pair of sequences with a certain degree of sequence similarity, and uses graph properties, such as biconnected components and articulation points, to generate sequence segments common to the enzyme sequences. Then amino acid subsequences in the common shared regions are aligned and then an information theoretic approach called aggregated column related scoring scheme is performed to highlight potential active sites in enzyme sequences. The aggregated information content scoring scheme is shown to be effective to highlight residues of active sites effectively. The proposed method of combining the clustering and the aggregated information content scoring methods was successful in highlighting known catalytic sites in enzymes of Escherichia coli K12 in terms of the Catalytic Site Atlas database. Our method is shown to be not only accurate in predicting potential active sites in the enzyme sequences but also computationally efficient since the clustering approach utilizes two graph properties that can be computed in linear to the number of edges in the sequence graph and computation of mutual information does not require much time. We believe that the proposed method can be useful for identifying active sites of enzyme sequences from many genome projects.     

Carbamoyl-phosphate synthetase. Creation of an escape route for ammonia

Carbamoyl-phosphate synthetase catalyzes the production of carbamoyl phosphate through a reaction mechanism requiring one molecule of bicarbonate, two molecules of MgATP, and one molecule of glutamine. The enzyme from Escherichia coli is composed of two polypeptide chains. The smaller of these belongs to the Class I amidotransferase superfamily and contains all of the necessary amino acid side chains required for the hydrolysis of glutamine to glutamate and ammonia. Two homologous domains from the larger subunit adopt conformations that are characteristic for members of the ATP-grasp superfamily. Each of these ATP-grasp domains contains an active site responsible for binding one molecule of MgATP. High resolution x-ray crystallographic analyses have shown that, remarkably, the three active sites in the E. coli enzyme are connected by a molecular tunnel of approximately 100 A in total length. Here we describe the high resolution x-ray crystallographic structure of the G359F (small subunit) mutant protein of carbamoyl phosphate synthetase. This residue was initially targeted for study because it resides within the interior wall of the molecular tunnel leading from the active site of the small subunit to the first active site of the large subunit. It was anticipated that a mutation to the larger residue would "clog" the ammonia tunnel and impede the delivery of ammonia from its site of production to the site of utilization. In fact, the G359F substitution resulted in a complete change in the conformation of the loop delineated by Glu-355 to Ala-364, thereby providing an "escape" route for the ammonia intermediate directly to the bulk solvent. The substitution also effected the disposition of several key catalytic amino acid side chains in the small subunit active site.     

Mapping the energetics of water-protein and water-ligand interactions with the "natural" HINT forcefield: predictive tools for characterizing the roles of water in biomolecules

The energetics and hydrogen bonding pattern of water molecules bound to proteins were mapped by analyzing structural data (resolution better than 2.3A) for sets of uncomplexed and ligand-complexed proteins. Water-protein and water-ligand interactions were evaluated using hydropatic interactions (HINT), a non-Newtonian forcefield based on experimentally determined logP(octanol/water) values. Potential water hydrogen bonding ability was assessed by a new Rank algorithm. The HINT-derived binding energies and Ranks for second shell water molecules were -0.04 kcal mol(-1) and 0.0, respectively, for first shell water molecules -0.38 kcal mol(-1) and 1.6, for active site water molecules -0.45 kcal mol(-1) and 2.3, for cavity water molecules -0.55 kcal mol(-1) and 3.3, and for buried water molecules -0.56 kcal mol(-1) and 4.4. For the last four classes, similar energies indicate that internal and external water molecules interact with protein almost equally, despite different degrees of hydrogen bonding. The binding energies and Ranks for water molecules bridging ligand-protein were -1.13 kcal mol(-1) and 4.5, respectively. This energetic contribution is shared equally between protein and ligand, whereas Rank favors the protein. Lastly, by comparing the uncomplexed and complexed forms of proteins, guidelines were developed for prediction of the roles played by active site water molecules in ligand binding. A water molecule with high Rank and HINT score is unlikely to make further interactions with the ligand and is largely irrelevant to the binding process, while a water molecule with moderate Rank and high HINT score is available for ligand interaction. Water molecule displaced for steric reasons were characterized by lower Rank and HINT score. These guidelines, tested by calculating HINT score and Rank for 50 water molecules bound in the active site of four uncomplexed proteins (for which the structures of the liganded forms were also available), correctly predicted the ultimate roles (in the complex) for 76% of water molecules. Some failures were likely due to ambiguities in the structural data.     

Weighted geometric docking: incorporating external information in the rotation-translation scan

Weighted geometric docking is a prediction algorithm that matches weighted molecular surfaces. Each molecule is represented by a grid of complex numbers, storing information about the shape of the molecule in the real part and weight information in the imaginary part. The weights are based on experimental biochemical and biophysical data or on theoretical analyses of amino acid conservation or correlation patterns in multiple-sequence alignments of homologous proteins. Only a few surface residues on either one or both molecules are weighted. In contrast to methods that use postscan filtering based on biochemical information, our method incorporates the external data in the rotation-translation search, producing a different set of docking solutions biased toward solutions in which the up-weighted residues are at the interface. Similarly, interactions involving specified residues can be impeded. The weighted geometric algorithm was applied to five systems for which regular geometric docking of the unbound molecules gave poor results. We obtained much better ranking of the nearly correct prediction and higher statistical significance when weighted geometric docking was used. The method was successful even when the weighted portion of the surface corresponded only partially and approximately to the binding site.     

Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms.

Two different machine-learning algorithms have been used to predict the blood-brain barrier permeability of different classes of molecules, to develop a method to predict the ability of drug compounds to penetrate the CNS. The first algorithm is based on a multilayer perceptron neural network and the second algorithm uses a support vector machine. Both algorithms are trained on an identical data set consisting of 179 CNS active molecules and 145 CNS inactive molecules. The training parameters include molecular weight, lipophilicity, hydrogen bonding, and other variables that govern the ability of a molecule to diffuse through a membrane. The results show that the support vector machine outperforms the neural network. Based on over 30 different validation sets, the SVM can predict up to 96% of the molecules correctly, averaging 81.5% over 30 test sets, which comprised of equal numbers of CNS positive and negative molecules. This is quite favorable when compared with the neural network's average performance of 75.7% with the same 30 test sets. The results of the SVM algorithm are very encouraging and suggest that a classification tool like this one will prove to be a valuable prediction approach.     

Tyrosyl-tRNA synthetase acts as an asymmetric dimer in charging tRNA. A rationale for half-of-the-sites activity

Tyrosyl-tRNA synthetase from Bacillus stearothermophilus is a classical example of an enzyme with half-of-the-sites activity. The enzyme crystallizes as a symmetrical dimer that is composed of identical subunits, each having a complete active site. In solution, however, tyrosyl-tRNA synthetase binds tightly, and activates rapidly, only 1 mol of Tyr/mol of dimer. It has recently been shown that the half-of-the-sites activity results from an inherent asymmetry of the enzyme. Only one subunit catalyzes formation of Tyr-AMP, and interchange of activity between subunits is not detectable over a long time scale. Paradoxically, however, the kinetics of tRNA charging are biphasic with respect to [Tyr], suggesting that both subunits of the dimer are catalytically active. This paradox has now been resolved by kinetic analysis of heterodimeric enzymes containing different mutations in each subunit. Biphasic kinetics with unchanged values of KM for Tyr are maintained when one of the two tRNA-binding domains is removed and also when the affinity of the "inactive" site for Try is reduced by 2-58-fold. The biphasic kinetics do not result from catalysis at both active sites, but instead appear to result from two molecules of Tyr binding sequentially to the same site. A second molecule of Tyr perhaps aids the dissociation of Tyr-tRNA by displacing the tyrosyl moiety from its binding site. A monomer of the enzyme is probably too small to allow both recognition and aminoacylation of a tRNA molecule. This could explain the requirement for the enzyme to function as an asymmetric dimer.     

An immunochemical study of D-glyceraldehyde-3-phosphatedehydrogenase]

Immunochemical study of D-glyceraldehyde-3-phosphate dehydrogenase from rat skeletal muscle was done using antibodies, raised in rabbit. One molecule of the enzyme binds three antibody molecules at the equivalence point and eight molecules at full saturation of the antibody binding sites. Modification of SH-groups of the dehydrogenase with pCMB, as well as ADP-induced inactivation of the enzyme do not cause any alterations in the quantitative precipitation curve. This suggests that antigenic determinants and the active site are situated in different loci on the enzyme molecule. A quantitative relationship between the dehydrogenase concentration and the percentage decrease of its catalytic activity in the presence of antibody excess are established, and a mechanism of apparent inhibition in the insoluble immune complex is proposed.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.