Statistical criteria for the identification of protein active sites using theoretical microscopic titration curves

Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties

DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.

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.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and nonenzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or nonenzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90%-97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

Significance statement

Identification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

Identification and characterization of alternative sites and molecular probes for SARS-CoV-2 target proteins

Three protein targets from SARS-CoV-2, the viral pathogen that causes COVID-19, are studied: the main protease, the 2'-O-RNA methyltransferase, and the nucleocapsid (N) protein. For the main protease, the nucleophilicity of the catalytic cysteine C145 is enabled by coupling to three histidine residues, H163 and H164 and catalytic dyad partner H41. These electrostatic couplings enable significant population of the deprotonated state of C145. For the RNA methyltransferase, the catalytic lysine K6968 that serves as a Brønsted base has significant population of its deprotonated state via strong coupling with K6844 and Y6845. For the main protease, Partial Order Optimum Likelihood (POOL) predicts two clusters of biochemically active residues; one includes the catalytic H41 and C145 and neighboring residues. The other surrounds a second pocket adjacent to the catalytic site and includes S1 residues F140, L141, H163, E166, and H172 and also S2 residue D187. This secondary recognition site could serve as an alternative target for the design of molecular probes. From in silico screening of library compounds, ligands with predicted affinity for the secondary site are reported. For the NSP16-NSP10 complex that comprises the RNA methyltransferase, three different sites are predicted. One is the catalytic core at the conserved K-D-K-E motif that includes catalytic residues D6928, K6968, and E7001 plus K6844. The second site surrounds the catalytic core and consists of Y6845, C6849, I6866, H6867, F6868, V6894, D6895, D6897, I6926, S6927, Y6930, and K6935. The third is located at the heterodimer interface. Ligands predicted to have high affinity for the first or second sites are reported. Three sites are also predicted for the nucleocapsid protein. This work uncovers key interactions that contribute to the function of the three viral proteins and also suggests alternative sites for ligand design.

Electrostatic fingerprints of catalytically active amino acids in enzymes

The computed electrostatic and proton transfer properties are studied for 20 enzymes that represent all six major enzyme commission classes and a variety of different folds. The properties of aspartate, glutamate, and lysine residues that have been previously experimentally determined to be catalytically active are reported. The catalytic aspartate and glutamate residues studied here are strongly coupled to at least one other aspartate or glutamate residue and often to multiple other carboxylate residues with intrinsic pKa differences less than 1 pH unit. Sometimes these catalytic acidic residues are also coupled to a histidine residue, such that the intrinsic pKa of the acidic residue is higher than that of the histidine. All catalytic lysine residues studied here are strongly coupled to tyrosine or cysteine residues, wherein the intrinsic pKa of the anion-forming residue is higher than that of the lysine. Some catalytic lysines are also coupled to other lysines with intrinsic pKa differences within 1 pH unit. Some evidence of the possible types of interactions that facilitate nucleophilicity is discussed. The interactions reported here provide important clues about how side chain functional groups that are weak Brønsted acids or bases for the free amino acid in solution can achieve catalytic potency and become strong acids, bases or nucleophiles in the enzymatic environment.

Machine learning differentiates enzymatic and non-enzymatic metals in proteins

Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic  metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model's ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.

Amino acid interactions that facilitate enzyme catalysis

Interactions in enzymes between catalytic and neighboring amino acids and how these interactions facilitate catalysis are examined. In examples from both natural and designed enzymes, it is shown that increases in catalytic rates may be achieved through elongation of the buffer range of the catalytic residues; such perturbations in the protonation equilibria are, in turn, achieved through enhanced coupling of the protonation equilibria of the active ionizable residues with those of other ionizable residues. The strongest coupling between protonation states for a pair of residues that deprotonate to form an anion (or a pair that accept a proton to form a cation) is achieved when the difference in the intrinsic pK_as of the two residues is approximately within 1 pH unit. Thus, catalytic aspartates and glutamates are often coupled to nearby acidic residues. For an anion-forming residue coupled to a cation-forming residue, the elongated buffer range is achieved when the intrinsic pK_a of the anion-forming residue is higher than the intrinsic pK_a of the (conjugate acid of the) cation-forming residue. Therefore, the high pK_a, anion-forming residues tyrosine and cysteine make good coupling partners for catalytic lysine residues. For the anion-cation pairs, the optimum difference in intrinsic pK_as is a function of the energy of interaction between the residues. For the energy of interaction ε expressed in units of (ln 10)RT, the optimum difference in intrinsic pK_as is within ∼1 pH unit of ε.

Analysis of electrostatic coupling throughout the laboratory evolution of a designed retroaldolase

The roles of local interactions in the laboratory evolution of a highly active, computationally designed retroaldolase (RA) are examined. Partial Order Optimum Likelihood (POOL) is used to identify catalytically important amino acid interactions in several RA95 enzyme variants. The series RA95.5, RA95.5–5, RA95.5–8, and RA95.5–8F, representing progress along an evolutionary trajectory with increasing activity, is examined. Computed measures of coupling between charged states of residues show that, as evolution proceeds and higher activities are achieved, electrostatic coupling between the biochemically active amino acids and other residues is increased. In silico residue scanning suggests multiple coupling partners for the catalytic lysine K83. The effects of two predicted partners, Y51 and E85, are tested using site‐directed mutagenesis and kinetic analysis of the variants Y51F and E85Q. The Y51F variants show decreases in k_cat relative to wild type, with the greatest losses observed for the more evolved constructs; they also exhibit significant decreases in k_cat/K_M across the series. Only modest decreases in k_cat/K_M are observed for the E85Q variants with little effect on k_cat. Computed metrics of the degree of coupling between protonation states rise significantly as evolution proceeds and catalytic turnover rate increases. Specifically, the charge state of the catalytic lysine K83 becomes more strongly coupled to those of other amino acids as the enzyme evolves to a better catalyst.

Tri-arginine exosite patch of caspase-6 recruits substrates for hydrolysis

Caspases are cysteine-aspartic proteases involved in the regulation of programmed cell death (apoptosis) and a number of other biological processes. Despite overall similarities in structure and active-site composition, caspases show striking selectivity for particular protein substrates. Exosites are emerging as one of the mechanisms by which caspases can recruit, engage, and orient these substrates for proper hydrolysis. Following computational analyses and database searches for candidate exosites, we utilized site-directed mutagenesis to identify a new exosite in caspase-6 at the hinge between the disordered N-terminal domain (NTD), residues 23-45, and core of the caspase-6 structure. We observed that substitutions of the tri-arginine patch Arg-42-Arg-44 or the R44K cancer-associated mutation in caspase-6 markedly alter its rates of protein substrate hydrolysis. Notably, turnover of protein substrates but not of short peptide substrates was affected by these exosite alterations, underscoring the importance of this region for protein substrate recruitment. Hydrogen-deuterium exchange MS-mediated interrogation of the intrinsic dynamics of these enzymes suggested the presence of a substrate-binding platform encompassed by the NTD and the 240's region (containing residues 236-246), which serves as a general exosite for caspase-6-specific substrate recruitment. In summary, we have identified an exosite on caspase-6 that is critical for protein substrate recognition and turnover and therefore highly relevant for diseases such as cancer in which caspase-6-mediated apoptosis is often disrupted, and in neurodegeneration in which caspase-6 plays a central role.

A comparative modeling and molecular docking study on Mycobacterium tuberculosis targets involved in peptidoglycan biosynthesis

An alarming rise of multidrug-resistant Mycobacterium tuberculosis strains and the continuous high global morbidity of tuberculosis have reinvigorated the need to identify novel targets to combat the disease. The enzymes that catalyze the biosynthesis of peptidoglycan in M. tuberculosis are essential and noteworthy therapeutic targets. In this study, the biochemical function and homology modeling of MurI, MurG, MraY, DapE, DapA, Alr, and Ddl enzymes of the CDC1551 M. tuberculosis strain involved in the biosynthesis of peptidoglycan cell wall are reported. Generation of the 3D structures was achieved with Modeller 9.13. To assess the structural quality of the obtained homology modeled targets, the models were validated using PROCHECK, PDBsum, QMEAN, and ERRAT scores. Molecular dynamics simulations were performed to calculate root mean square deviation (RMSD) and radius of gyration (Rg) of MurI and MurG target proteins and their corresponding templates. For further model validation, RMSD and Rg for selected targets/templates were investigated to compare the close proximity of their dynamic behavior in terms of protein stability and average distances. To identify the potential binding mode required for molecular docking, binding site information of all modeled targets was obtained using two prediction algorithms. A docking study was performed for MurI to determine the potential mode of interaction between the inhibitor and the active site residues. This study presents the first accounts of the 3D structural information for the selected M. tuberculosis targets involved in peptidoglycan biosynthesis.

Cutoff lensing: predicting catalytic sites in enzymes

Predicting function-related amino acids in proteins with unknown function or unknown allosteric binding sites in drug-targeted proteins is a task of paramount importance in molecular biomedicine. In this paper we introduce a simple, light and computationally inexpensive structure-based method to identify catalytic sites in enzymes. Our method, termed cutoff lensing, is a general procedure consisting in letting the cutoff used to build an elastic network model increase to large values. A validation of our method against a large database of annotated enzymes shows that optimal values of the cutoff exist such that three different structure-based indicators allow one to recover a maximum of the known catalytic sites. Interestingly, we find that the larger the structures the greater the predictive power afforded by our method. Possible ways to combine the three indicators into a single figure of merit and into a specific sequential analysis are suggested and discussed with reference to the classic case of HIV-protease. Our method could be used as a complement to other sequence- and/or structure-based methods to narrow the results of large-scale screenings.

Prediction of distal residue participation in enzyme catalysis

A scoring method for the prediction of catalytically important residues in enzyme structures is presented and used to examine the participation of distal residues in enzyme catalysis. Scores are based on the Partial Order Optimum Likelihood (POOL) machine learning method, using computed electrostatic properties, surface geometric features, and information obtained from the phylogenetic tree as input features. Predictions of distal residue participation in catalysis are compared with experimental kinetics data from the literature on variants of the featured enzymes; some additional kinetics measurements are reported for variants of Pseudomonas putida nitrile hydratase (ppNH) and for Escherichia coli alkaline phosphatase (AP). The multilayer active sites of P. putida nitrile hydratase and of human phosphoglucose isomerase are predicted by the POOL log ZP scores, as is the single-layer active site of P. putida ketosteroid isomerase. The log ZP score cutoff utilized here results in over-prediction of distal residue involvement in E. coli alkaline phosphatase. While fewer experimental data points are available for P. putida mandelate racemase and for human carbonic anhydrase II, the POOL log ZP scores properly predict the previously reported participation of distal residues.

Biochemical functional predictions for protein structures of unknown or uncertain function

With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.

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

Background

The 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.

Results

Implementation 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.

Conclusions

A 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.

Protein structure based prediction of catalytic residues

BACKGROUND

Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation.

RESULTS

We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods.

CONCLUSIONS

We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

Effects of non-catalytic, distal amino acid residues on activity of E. coli DinB (DNA polymerase IV)

DinB is one of two Y family polymerases in E. coli and is involved in copying damaged DNA. DinB is specialized to bypass deoxyguanosine adducts that occur at the N(2) position, with its cognate lesion being the furfuryl adduct. Active site residues have been identified that make contact with the substrate and carry out deoxynucleotide triphosphate (dNTP) addition to the growing DNA strand. In DNA polymerases, these include negatively charged aspartate and glutamate residues (D8, D103, and E104 in E. coli DNA polymerase IV DinB). These residues position the essential magnesium ions correctly to facilitate nucleophilic attack by the primer hydroxyl group on the α-phosphate group of the incoming dNTP. To study the contribution of DinB residues to lesion bypass, the computational methods THEMATICS and POOL were employed. These methods correctly predict the known active site residues, as well as other residues known to be important for activity. In addition, these methods predict other residues involved in substrate binding as well as more remote residues. DinB variants with mutations at the predicted positions were constructed and assayed for bypass of the N(2) -furfuryl-dG lesion. We find a wide range of effects of predicted residues, including some mutations that abolish damage bypass. Moreover, most of the DinB variants constructed are unable to carry out the extension step of lesion bypass. The use of computational prediction methods represents another tool that will lead to a more complete understanding of translesion DNA synthesis.

Identification of catalytic residues using a novel feature that integrates the microenvironment and geometrical location properties of residues

Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤ 10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.

Evidence of the participation of remote residues in the catalytic activity of Co-type nitrile hydratase from Pseudomonas putida

Active sites may be regarded as layers of residues, whereby the residues that interact directly with substrate also interact with residues in a second shell and these in turn interact with residues in a third shell. These residues in the second and third layers may have distinct roles in maintaining the essential chemical properties of the first-shell catalytic residues, particularly their spatial arrangement relative to the substrate binding pocket, and their electrostatic and dynamic properties. The extent to which these remote residues participate in catalysis and precisely how they affect first-shell residues remains unexplored. To improve our understanding of the roles of second- and third-shell residues in catalysis, we used THEMATICS to identify residues in the second and third shells of the Co-type nitrile hydratase from Pseudomonas putida (ppNHase) that may be important for catalysis. Five of these predicted residues, and three additional, conserved residues that were not predicted, have been conservatively mutated, and their effects have been studied both kinetically and structurally. The eight residues have no direct contact with the active site metal ion or bound substrate. These results demonstrate that three of the predicted second-shell residues (α-Asp164, β-Glu56, and β-His147) and one predicted third-shell residue (β-His71) have significant effects on the catalytic efficiency of the enzyme. One of the predicted residues (α-Glu168) and the three residues not predicted (α-Arg170, α-Tyr171, and β-Tyr215) do not have any significant effects on the catalytic efficiency of the enzyme.

Crystal structure of a metal-dependent phosphoesterase (YP_910028.1) from Bifidobacterium adolescentis: Computational prediction and experimental validation of phosphoesterase activity

The crystal structures of an unliganded and adenosine 5'-monophosphate (AMP) bound, metal-dependent phosphoesterase (YP_910028.1) from Bifidobacterium adolescentis are reported at 2.4 and 1.94 Å, respectively. Functional characterization of this enzyme was guided by computational analysis and then confirmed by experiment. The structure consists of a polymerase and histidinol phosphatase (PHP, Pfam: PF02811) domain with a second domain (residues 105-178) inserted in the middle of the PHP sequence. The insert domain functions in binding AMP, but the precise function and substrate specificity of this domain are unknown. Initial bioinformatics analyses yielded multiple potential functional leads, with most of them suggesting DNA polymerase or DNA replication activity. Phylogenetic analysis indicated a potential DNA polymerase function that was somewhat supported by global structural comparisons identifying the closest structural match to the alpha subunit of DNA polymerase III. However, several other functional predictions, including phosphoesterase, could not be excluded. Theoretical microscopic anomalous titration curve shapes, a computational method for the prediction of active sites from protein 3D structures, identified potential reactive residues in YP_910028.1. Further analysis of the predicted active site and local comparison with its closest structure matches strongly suggested phosphoesterase activity, which was confirmed experimentally. Primer extension assays on both normal and mismatched DNA show neither extension nor degradation and provide evidence that YP_910028.1 has neither DNA polymerase activity nor DNA-proofreading activity. These results suggest that many of the sequence neighbors previously annotated as having DNA polymerase activity may actually be misannotated.

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.

Prediction of active site cleft using support vector machines

Computational tools are available today for the detection and delineation of the clefts and cavities in protein 3D structure and ranking them on the basis of probable binding site clefts. There is a need to improve the ranking of clefts and accuracy of predicting catalytic site clefts. Our results show that the distance of the clefts from protein centroid and sequence entropy of the lining residues, when used in conjunction with the volume, are valuable descriptors for predicting the catalytic site. We have applied the SVM approach for recognizing and ranking the active site clefts and tested its performance using different combinations of attributes. In both the ligand-bound and the unbound forms of structures, our method correctly predicts the active site clefts in 73% of cases at rank one. If we consider the results at rank 3 (i.e., the correct solution is among one of the top three solutions), the correctly predicted cases are 94% and 90% for the bound and the unbound forms of structures, respectively. Our approach improves the ranking of binding site clefts in comparison with CASTp and is comparable to other existing methods like Fpocket. Although the data set for training the SVM approach is rather small in size, the results are encouraging for the method to be used as complementary to other existing tools.

Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure

Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

Protein molecules are ubiquitous in the cell; they perform thousands of functions crucial for life. Proteins accomplish nearly all of these functions by interacting with other molecules. These interactions are mediated by specific amino acid positions in the proteins. Knowledge of these “functional sites” is crucial for understanding the molecular mechanisms by which proteins carry out their functions; however, functional sites have not been identified in the vast majority of proteins. Here, we present ConCavity, a computational method that predicts small molecule binding sites in proteins by combining analysis of evolutionary sequence conservation and protein 3D structure. ConCavity provides significant improvement over previous approaches, especially on large, multi-chain proteins. In contrast to earlier methods which only predict entire binding sites, ConCavity makes specific predictions of positions in space that are likely to overlap ligand atoms and of residues that are likely to contact bound ligands. These predictions can be used to aid computational function prediction, to guide experimental protein analysis, and to focus computationally intensive techniques used in drug discovery.

Partial order optimum likelihood (POOL): maximum likelihood prediction of protein active site residues using 3D Structure and sequence properties

A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers.

Genome sequencing has revealed the codes for thousands of previously unknown proteins for humans and for hundreds of other species. Many of these proteins are of unknown or unclear function. The information contained in the genome sequences holds tremendous potential benefit to humankind, including new approaches to the diagnosis and treatment of disease. In order to realize these benefits, a key step is to understand the functions of the proteins for which these genes hold the code. A first step in understanding the function of a protein is to identify the functional site, the local area on the surface of a protein where it affects its functional activity. This paper reports on a new computational methodology to predict protein functional sites from protein 3D structures. A new machine learning approach called Partial Order Optimum Likelihood (POOL) is introduced here. It is shown that POOL outperforms previous methods for the prediction of protein functional sites from 3D structures.

Functional analysis of free methionine-R-sulfoxide reductase from Saccharomyces cerevisiae

Methionine sulfoxide reductases (Msrs) are oxidoreductases that catalyze thiol-dependent reduction of oxidized methionines. MsrA and MsrB are the best known Msrs that repair methionine-S-sulfoxide (Met-S-SO) and methionine-R-sulfoxide (Met-R-SO) residues in proteins, respectively. In addition, an Escherichia coli enzyme specific for free Met-R-SO, designated fRMsr, was recently discovered. In this work, we carried out comparative genomic and experimental analyses to examine occurrence, evolution, and function of fRMsr. This protein is present in single copies and two mutually exclusive subtypes in about half of prokaryotes and unicellular eukaryotes but is missing in higher plants and animals. A Saccharomyces cerevisiae fRMsr homolog was found to reduce free Met-R-SO but not free Met-S-SO or dabsyl-Met-R-SO. fRMsr was responsible for growth of yeast cells on Met-R-SO, and the double fRMsr/MsrA mutant could not grow on a mixture of methionine sulfoxides. However, in the presence of methionine, even the triple fRMsr/MsrA/MsrB mutant was viable. In addition, fRMsr deletion strain showed an increased sensitivity to oxidative stress and a decreased life span, whereas overexpression of fRMsr conferred higher resistance to oxidants. Molecular modeling and cysteine residue targeting by thioredoxin pointed to Cys(101) as catalytic and Cys(125) as resolving residues in yeast fRMsr. These residues as well as a third Cys, resolving Cys(91), clustered in the structure, and each was required for the catalytic activity of the enzyme. The data show that fRMsr is the main enzyme responsible for the reduction of free Met-R-SO in S. cerevisiae.

Prediction of interaction sites from apo 3D structures when the holo conformation is different

The predictability of catalytic and binding sites from apo structures is addressed for proteins that undergo significant conformational change upon binding. Theoretical microscopic titration curves (THEMATICS), an electrostatics-based method for the prediction of functional sites, is performed on a test set of 24 proteins with both apo and holo structures available. For 23 of these 24 proteins (96%), THEMATICS predicts the correct catalytic or binding site for both the apo and holo forms. For only one of the 24 proteins, THEMATICS makes the correct prediction for the holo structure but fails for the apo structure. The metrics used by THEMATICS to identify functional residues generally are larger in absolute value for the functional residues in the holo forms compared to the corresponding residues in the apo forms. However, even in the apo forms, these identifying metrics are still statistically significantly larger for functional residues than for residues not involved in catalysis or binding. This indicates that some of the unusual electrostatic properties of functional residues are preserved in the apo conformation. Evidence is presented that certain residues immediately surrounding the active catalytic and binding residues impart functionally important chemical and electrostatic properties to the active residues. At least parts of these microenvironments exist in the unbound conformations, such that THEMATICS is able to distinguish the functional residues even in the apo structures.

An insight into the general relationship between the three dimensional structures of enzymes and their electronic wave functions: Implication for the prediction of functional sites of enzymes

In this study, we explored the general relationship between the three-dimensional (3D) structures of enzymes and their electronic wave functions. Furthermore, we developed a method for the prediction of their functionally important sites. For this purpose, we first performed linear-scaling molecular orbital calculations for 112 nonredundant, non-homologous enzymes with known structure and function. In consequence, we showed that the canonical molecular orbitals (MOs) of the enzymes could be classified into three groups according to the degree of electron delocalization: highly localized orbitals (Group A), highly delocalized orbitals whose electrons are distributed over almost the whole molecule (Group B), and moderately delocalized orbitals (Group C). The MOs belonging to Group A are located near the HOMO-LUMO band gap, and thereby include the frontier orbitals of a given enzyme. We inferred that the MOs of Group B play a role in stabilizing the 3D structure of the enzyme, while those of Group C contribute to constructing the covalent bond framework of the enzyme. Next, we investigated whether the frontier orbitals of enzymes could be used for identifying their potential functional sites. As a result, we found that the frontier orbitals of the 112 enzymes have a high propensity to be colocalized with the known functional sites, especially when the enzymes are hydrated. Such a propensity is shown to be remarkable when Glu or Asp is a functional site residue. On the basis of these results, we finally propose a protocol for the prediction of functional sites of enzymes.

Mapping local protein electrostatics by EPR of pH-sensitive thiol-specific nitroxide

A first thiol-specific pH-sensitive nitroxide spin-label of the imidazolidine series, methanethiosulfonic acid S-(1-oxyl-2,2,3,5,5-pentamethylimidazolidin-4-ylmethyl) ester (IMTSL), has been synthesized and characterized. X-Band (9 GHz) and W-band (94 GHz) EPR spectral parameters of the new spin-label in its free form and covalently attached to an amino acid cysteine and a tripeptide glutathione were studied as a function of pH and solvent polarity. The pKa value of the protonatable tertiary amino group of the spin-label was found to be unaffected by other ionizable groups present in side chains of unstructured small peptides. The W-band EPR spectra were shown to allow for pKa determination from precise g-factor measurements. Is has been demonstrated that the high accuracy of pKa determination for pH-sensitive nitroxides could be achieved regardless of the frequency of measurements or the regime of spin exchange: fast at X-band and slow at W-band. IMTSL was found to react specifically with a model protein, iso-1-cytochrome c from the yeast Saccharomyces cerevisiae, giving EPR spectra very similar to those of the most commonly employed cysteine-specific label MTSL. CD data indicated no perturbations to the overall protein structure upon IMTSL labeling. It was found that for IMTSL, g iso correlates linearly with A iso, but the slopes are different for the neutral and charged forms of the nitroxide. This finding was attributed to the solvent effects on the spin density at the oxygen atom of the NO group and on the excitation energy of the oxygen lone-pair orbital.

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.

Assessing the quality of the homology-modeled 3D structures from electrostatic standpoint: test on bacterial nucleoside monophosphate kinase families

In this study, we address the issue of performing meaningful pK(a) calculations using homology modeled three-dimensional (3D) structures and analyze the possibility of using the calculated pK(a) values to detect structural defects in the models. For this purpose, the 3D structure of each member of five large protein families of a bacterial nucleoside monophosphate kinases (NMPK) have been modeled by means of homology-based approach. Further, we performed pK(a) calculations for the each model and for the template X-ray structures. Each bacterial NMPK family used in the study comprised on average 100 members providing a pool of sequences and 3D models large enough for reliable statistical analysis. It was shown that pK(a) values of titratable groups, which are highly conserved within a family, tend to be conserved among the models too. We demonstrated that homology modeled structures with sequence identity larger than 35% and gap percentile smaller than 10% can be used for meaningful pK(a) calculations. In addition, it was found that some highly conserved titratable groups either exhibit large pK(a) fluctuations among the models or have pK(a) values shifted by several pH units with respect to the pK(a) calculated for the X-ray structure. We demonstrated that such case usually indicates structural errors associated with the model. Thus, we argue that pK(a) calculations can be used for assessing the quality of the 3D models by monitoring fluctuations of the pK(a) values for highly conserved titratable residues within large sets of homologous proteins.

BANMOKI: a searchable database of homology-based 3D models and their electrostatic properties of five bacterial nucleoside monophosphate kinase families

The nucleoside monophosphate kinases (NMPK) are important enzymes that control the ratio of mono- and di-phosphate nucleosides and participate in gene regulation and signal transduction in the cell. However, despite their importance only several 3D structures were experimentally determined in contrast to the wealth of sequences available for each of the NMPK families. To fill this gap we present a Web-based database containing structural models for all proteins of the five bacterial nucleoside monophosphate kinase (bNMPK) families. The models were computed by means of homology-based approach using a few experimentally determined bNMPK structures. The database also contains pK(a) values and their components calculated for the homology-based 3D models, which is a unique feature of the database. The BActerial Nucleoside MOnophosphate KInases (BANMOKI) database is freely accessible (http://www.ces.clemson.edu/compbio/banmoki) and offers an easy user-friendly interface for browsing, searching and downloading content of the database. The users can investigate, using the searching tools of the database, the properties of the bNMP kinases in respect to sequence composition, electrostatic interactions and structural differences.

Prediction of functional sites based on the fuzzy oil drop model

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related.

Selective prediction of interaction sites in protein structures with THEMATICS

BACKGROUND

Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS), a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites.

RESULTS

Using a test set of 169 enzymes from the original Catalytic Residue Dataset (CatRes) it is shown that THEMATICS can deliver precise, localised site predictions. Furthermore, adjustment of the cut-off criteria can improve the recall rates for catalytic residues with only a small sacrifice in precision. Recall rates for CatRes/CSA annotated catalytic residues are 41.1%, 50.4%, and 54.2% for Z score cut-off values of 1.00, 0.99, and 0.98, respectively. The corresponding precision rates are 19.4%, 17.9%, and 16.4%. The success rate for catalytic sites is higher, with correct or partially correct predictions for 77.5%, 85.8%, and 88.2% of the enzymes in the test set, corresponding to the same respective Z score cut-offs, if only the CatRes annotations are used as the reference set. Incorporation of additional literature annotations into the reference set gives total success rates of 89.9%, 92.9%, and 94.1%, again for corresponding cut-off values of 1.00, 0.99, and 0.98. False positive rates for a 75-protein test set are 1.95%, 2.60%, and 3.12% for Z score cut-offs of 1.00, 0.99, and 0.98, respectively.

CONCLUSION

With a preferred cut-off value of 0.99, THEMATICS achieves a high success rate of interaction site prediction, about 86% correct or partially correct using CatRes/CSA annotations only and about 93% with an expanded reference set. Success rates for catalytic residue prediction are similar to those of other structure-based methods, but with substantially better precision and lower false positive rates. THEMATICS performs well across the spectrum of E.C. classes. The method requires only the structure of the query protein as input. THEMATICS predictions may be obtained via the web from structures in PDB format at: http://pfweb.chem.neu.edu/thematics/submit.html.

New insights into the reaction mechanism catalyzed by the glutamate racemase enzyme: pH titration curves and classical molecular dynamics simulations

The mechanism of the reactions catalyzed by the pyridoxal-phosphate-independent amino acid racemases and epimerases faces the difficult task of deprotonating a relatively low acidicity proton, the amino acid's alpha-hydrogen, with a relatively poor base, a cysteine. In this work, we propose a mechanism for one of these enzymes, glutamate racemase (MurI), about which many controversies exist, and the roles that its active site residues may play. The titration curves and the pK1/2 values of all of the ionizable residues for different structures leading from reactants to products have been analyzed. From these results a concerted mechanism has been proposed in which the Cys70 residue would deprotonate the alpha-hydrogen of the substrate while, at the same time, being deprotonated by the Asp7 residue. To study the consistency of this mechanism classical molecular dynamics (MD) simulations have been carried out along with pK1/2 calculations on the MD-generated structures.

Identification of functional subclasses in the DJ-1 superfamily proteins

Genomics has posed the challenge of determination of protein function from sequence and/or 3-D structure. Functional assignment from sequence relationships can be misleading, and structural similarity does not necessarily imply functional similarity. Proteins in the DJ-1 family, many of which are of unknown function, are examples of proteins with both sequence and fold similarity that span multiple functional classes. THEMATICS (theoretical microscopic titration curves), an electrostatics-based computational approach to functional site prediction, is used to sort proteins in the DJ-1 family into different functional classes. Active site residues are predicted for the eight distinct DJ-1 proteins with available 3-D structures. Placement of the predicted residues onto a structural alignment for six of these proteins reveals three distinct types of active sites. Each type overlaps only partially with the others, with only one residue in common across all six sets of predicted residues. Human DJ-1 and YajL from Escherichia coli have very similar predicted active sites and belong to the same probable functional group. Protease I, a known cysteine protease from Pyrococcus horikoshii, and PfpI/YhbO from E. coli, a hypothetical protein of unknown function, belong to a separate class. THEMATICS predicts a set of residues that is typical of a cysteine protease for Protease I; the prediction for PfpI/YhbO bears some similarity. YDR533Cp from Saccharomyces cerevisiae, of unknown function, and the known chaperone Hsp31 from E. coli constitute a third group with nearly identical predicted active sites. While the first four proteins have predicted active sites at dimer interfaces, YDR533Cp and Hsp31 both have predicted sites contained within each subunit. Although YDR533Cp and Hsp31 form different dimers with different orientations between the subunits, the predicted active sites are superimposable within the monomer structures. Thus, the three predicted functional classes form four different types of quaternary structures. The computational prediction of the functional sites for protein structures of unknown function provides valuable clues for functional classification.

Analysing the pH-dependent properties of proteins using pKa calculations

The results of protein pKa calculations are routinely being analysed to understand the pH-dependence of protein characteristics such as stability and catalysis. Systems of functionally important titratable groups are identified from protein from pKa calculations, but the rationalisation of the behaviour of such systems is inherently problematic due to a lack of theoretical tools and methods. I present a number of novel methods for analysing the results of protein pKa calculations which have been embedded in a graphical user interface (pKaTool). In the present paper I present novel methods for assessing the reliability of protein pKa calculations and for analysing the roles of individual residues in determining active site pKa values and the pH-dependence of protein stability. The methods presented are freely available to academic researchers at http://enzyme.ucd.ie/Science/pKa/pKaTool .

Atomic resolution crystallography reveals how changes in pH shape the protein microenvironment

Hydrogen atoms are a vital component of enzyme structure and function. In recent years, atomic resolution crystallography (>or=1.2 A) has been successfully used to investigate the role of the hydrogen atom in enzymatic catalysis. Here, atomic resolution crystallography was used to study the effect of pH on cholesterol oxidase from Streptomyces sp., a flavoenzyme oxidoreductase. Crystallographic observations of the anionic oxidized flavin cofactor at basic pH are consistent with the UV-visible absorption profile of the enzyme and readily explain the reversible pH-dependent loss of oxidation activity. Furthermore, a hydrogen atom, positioned at an unusually short distance from the main chain carbonyl oxygen of Met122 at high pH, was observed, suggesting a previously unknown mechanism of cofactor stabilization. This study shows how a redox active site responds to changes in the enzyme's environment and how these changes are able to influence the mechanism of enzymatic catalysis.

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.

Optical reflectance assay for the detection of biofilm formation

We describe the protocol for an inexpensive and nondestructive optical reflectance assay for the measurement of biofilm formation. Reflectance data are obtained using an Ocean Optics (Dunedin, Florida) USB 2000 spectrometer with a polychromatic light source. A fiber optic cable is used both for illumination and collection, and Ocean Optics OOIBase32 Platinum software is used for preliminary processing of the data. Differences in reflectance data collected at times ranging from 2 to 24 h distinguish between cell attachment and volume growth for two strains of Enterococci. Confocal scanning laser microscopy imaging is used to confirm these results. Phase contrast microscopy images are also obtained in conjunction with reflectance measurements for several different biofilm specimens. The experiments consider biofilm formation on glass and polystyrene substrata, but the method can be used for many other abiotic substrata of interest, both opaque and nonopaque.