Inactivation of GABA transaminase by 4-acryloylphenol

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

BACKGROUND

Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori.

OBJECTIVE

To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values.

METHODS

The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model.

RESULTS

Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior.

CONCLUSION

PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

Design and Mechanism of GABA Aminotransferase Inactivators. Treatments for Epilepsies and Addictions

When the brain concentration of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) diminishes below a threshold level, the excess neuronal excitation can lead to convulsions. This imbalance in neurotransmission can be corrected by inhibition of the enzyme γ-aminobutyric acid aminotransferase (GABA-AT), which catalyzes the conversion of GABA to the excitatory neurotransmitter l-glutamic acid. It also has been found that raising GABA levels can antagonize the rapid elevation and release of dopamine in the nucleus accumbens, which is responsible for the reward response in addiction. Therefore, the design of new inhibitors of GABA-AT, which increases brain GABA levels, is an important approach to new treatments for epilepsy and addiction. This review summarizes findings over the last 40 or so years of mechanism-based inactivators (unreactive compounds that require the target enzyme to catalyze their conversion to the inactivating species, which inactivate the enzyme prior to their release) of GABA-AT with emphasis on their catalytic mechanisms of inactivation, presented according to organic chemical mechanism, with minimal pharmacology, except where important for activity in epilepsy and addiction. Patents, abstracts, and conference proceedings are not covered in this review. The inactivation mechanisms described here can be applied to the inactivations of a wide variety of unrelated enzymes.