Mary Jo Ondrechen

THEMATICS: A simple computational predictor of enzyme function from structure

We show that theoretical microscopic titration curves (THEMATICS) can be used to identify active-site residues in proteins of known structure. Results are featured for three enzymes: triosephosphate isomerase (TIM), aldose reductase (AR), and phosphomannose isomerase (PMI). We note that TIM and AR have similar structures but catalyze different kinds of reactions, whereas TIM and PMI have different structures but catalyze similar reactions. Analysis of the theoretical microscopic titration curves for all of the ionizable residues of these proteins shows that a small fraction (3–7%) of the curves possess a flat region where the residue is partially protonated over a wide pH range. The preponderance of residues with such perturbed curves occur in the active site. Additional results are given in summary form to show the success of the method for proteins with a variety of different chemistries and structures.

The Intrinsic Anodic Stability of Several Anions Comprising Solvent‐Free Ionic Liquids

The advent of lithium-ion or rocking chair rechargeable battery technology has severely stretched the anodic limits of common nonaqueous electrolytes. Salts of the form 1,2-dimethyl-3-propylimidazolium X [where X = AsF{sub 6}{sup {minus}}, PF{sub 6}{sup {minus}}, (CF{sub 3}SO{sub 2}){sub 2}N{sup {minus}}, and (CF{sub 3}SO{sub 2}){sub 3}C{sup {minus}}] were prepared and purified. Linear sweep voltammetry was conducted at 80 C, a temperature at which all four salts were molten, at Pt, W, and glassy carbon working electrodes. The authors found that the intrinsic anodic stability of these anions was in the order (CF{sub 3}SO{sub 2}){sub 3}C{sup {minus}} > (CF{sub 3}SO{sub 2}){sub 2}N{sup {minus}} {approximately} AsF{sub 6}{sup {minus}} > PF{sub 6}{sup {minus}}. These experimental solution-phase oxidation potentials correlated well with gas-phase highest occupied molecular orbital energies calculated by an ab initio technique.

Maximum work from a finite reservoir by sequential Carnot cycles

The production of work from a heat source with finite heat capacity is discussed. We examine the conversion of heat from such a source first by a single Carnot engine and then by a sequence of Carnot engines. The optimum values of the operating temperatures of these engines are calculated. The work production and efficiency of a sequence with an arbitrary number of engines is derived, and it is shown that the maximum available work can be extracted only when the number of cycles in the sequence becomes infinite. The results illustrate the importance of recovery or bottoming processes in the optimization of work‐producing systems. In addition, the present model illuminates one practical limitation of the Carnot cycle: The Carnot efficiency is only obtainable from a heat source with infinite heat capacity. However, another cycle, somewhat reminiscent of the Otto and Brayton cycles, is derived which will provide the maximum efficiency for a heat source with a finite heat capacity.

The generalized Carnot cycle: A working fluid operating in finite time between finite heat sources and sinks

The production of work in finite time from a reservoir with finite heat capacity is studied. A model system, for which the only irreversibilities result from finite rates of heat conduction, is adopted. The maximum work obtainable in finite time from such a system is derived, and is found to be strongly dependent upon the reservoir heat capacity. The cycle producing the maximum work is derived for an arbitrary one‐component working fluid; no equation of state is assumed. In the optimum cycle, when the working substance is in contact with a finite reservoir, then the temperature of the working fluid is an exponential function of time and the entropy of the working substance is a linear function of time. While the maximum work obtainable in a single fixed‐time cycle is a strictly increasing function of the reservoir heat capacity, the efficiency (work produced/heat put in) is a strictly decreasing function of the reservoir heat capacity, for the model system with a finite hot reservoir and an infinite cold ...

Selective prediction of interaction sites in protein structures with THEMATICS.

BackgroundMethods 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.ResultsUsing 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.ConclusionWith 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

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.

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.

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

Theoretical Microscopic Titration Curves (THEMATICS) may be used to identify chemically important residues in active sites of enzymes by characteristic deviations from the normal, sigmoidal Henderson–Hasselbalch titration behavior. Clusters of such deviant residues in physical proximity constitute reliable predictors of the location of the active site. Originally the residues with deviant predicted behavior were identified by human observation of the computed titration curves. However, it is preferable to select the unusual residues by mathematically well‐defined criteria, in order to reduce the chance of error, eliminate any possible biases, and substantially speed up the selection process. Here we present some simple statistical tests that constitute such selection criteria. The first derivatives of the predicted titration curves resemble distribution functions and are normalized. The moments of these first derivative functions are computed. It is shown that the third and fourth moments, measures of asymmetry and kurtosis, respectively, are good measures of the deviations from normal behavior. Results are presented for 44 different enzymes. Detailed results are given for 4 enzymes with 4 different types of chemistry: arginine kinase from Limulus polyphemus (horseshoe crab); β‐lactamase from Escherichia coli; glutamate racemase from Aquifex pyrophilus; and 3‐isopropylmalate dehydrogenase from Thiobacillus ferrooxidans. The relationship between the statistical measures of nonsigmoidal behavior in the predicted titration curves and the catalytic activity of the residue is discussed. Proteins 2005.

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.

The human Aurora kinase inhibitor danusertib is a lead compound for anti-trypanosomal drug discovery via target repurposing

New drugs for neglected tropical diseases such as human African trypanosomiasis (HAT) are needed, yet drug discovery efforts are not often focused on this area due to cost. Target repurposing, achieved by the matching of essential parasite enzymes to those human enzymes that have been successfully inhibited by small molecule drugs, provides an attractive means by which new drug optimization programs can be pragmatically initiated. In this report we describe our results in repurposing an established class of human Aurora kinase inhibitors, typified by danusertib (1), which we have observed to be an inhibitor of trypanosomal Aurora kinase 1 (TbAUK1) and effective in parasite killing in vitro. Informed by homology modeling and docking, a series of analogs of 1 were prepared that explored the scope of the chemotype and provided a nearly 25-fold improvement in cellular selectivity for parasite cells over human cells.