Docking and pharmacodynamic studies on hGAT1 inhibition activity in the presence of selected neuronal and astrocytic inhibitors. Part I

Development towards a novel screening method for nipecotic acid bioisosteres using molecular imprinted polymers (MIPs) as alternative to in vitro cellular uptake assays

Impaired expression of GABA transporters (GATs) is closely related to the pathogenesis of among others Parkinson's disease and epilepsy. As such, lipophilic nipecotic acid analogs have been extensively studied as GAT1-addressing drugs and radioligands but suffer from limited brain uptake due to the zwitterionic properties of the nipecotic acid moiety. Bioisosteric replacement of the carboxylic acid group is a promising strategy to improve the brain uptake, though it requires knowledge on the binding of these isosteres to GAT1. To screen nipecotic acid isosteres for their affinity to GAT1 in a time- and cost-effective manner, this research aims to develop a molecular imprinted polymer (MIP) that mimics the natural binding site of GAT1 and can act as an alternative screening tool to the current radiometric and mass spectrometry cellular-based assays. To this end, a nipecotic acid MIP was created using the electropolymerization of ortho-phenylenediamine (oPD) by cyclic voltammetry (CV). The optimization of the generated receptor layer was achieved by varying the scan rate (50-250 mV/s) and number of CV cycles (5-12), yielding an optimized MIP with an average imprinting factor of 2.6, a linear range of 1-1000 nm, and a theoretical LOD of 0.05 nm, as analyzed by electrical impedance spectroscopy (EIS). Selectivity studies facilitated the investigation of major binding interactions between the MIP and the substrate, building an experimental model that compares characteristics of various analogs. Results from this model indicate that the substrate carboxylic acid group plays a more important role in binding than an amine group, after comparing the binding of cyclohexanecarboxylic acid (average IF of 1.7) and piperidine (average IF of 0.46). The research culminates in a discussion regarding the feasibility of the in vitro model, comparing the synthetic system against the biological performance of GAT1. Thus, evaluating if it is possible to generate a synthetic GAT1 mimic, and if so, provide directions for follow-up research.

Visualizing GABA transporters in vivo: an overview of reported radioligands and future directions

By clearing GABA from the synaptic cleft, GABA transporters (GATs) play an essential role in inhibitory neurotransmission. Consequently, in vivo visualization of GATs can be a valuable diagnostic tool and biomarker for various psychiatric and neurological disorders. Not surprisingly, in recent years several research attempts to develop a radioligand have been conducted, but so far none have led to suitable radioligands that allow imaging of GATs. Here, we provide an overview of the radioligands that were developed with a focus on GAT1, since this is the most abundant transporter and most of the research concerns this GAT subtype. Initially, we focus on the field of GAT1 inhibitors, after which we discuss the development of GAT1 radioligands based on these inhibitors. We hypothesize that the radioligands developed so far have been unsuccessful due to the zwitterionic nature of their nipecotic acid moiety. To overcome this problem, the use of non-classical GAT inhibitors as basis for GAT1 radioligands or the use of carboxylic acid bioisosteres may be considered. As the latter structural modification has already been used in the field of GAT1 inhibitors, this option seems particularly viable and could lead to the development of more successful GAT1 radioligands in the future.

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

BACKGROUND

Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

OBJECTIVE

Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

METHODS

SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

RESULTS

Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

CONCLUSION

Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

KV11.1, NaV1.5, and CaV1.2 Transporter Proteins as Antitarget for Drug Cardiotoxicity

Safety assessment of pharmaceuticals is a rapidly developing area of pharmacy and medicine. The new advanced guidelines for testing the toxicity of compounds require specialized tools that provide information on the tested drug in a quick and reliable way. Ion channels represent the third-largest target. As mentioned in the literature, ion channels are an indispensable part of the heart's work. In this paper the most important information concerning the guidelines for cardiotoxicity testing and the way the tests are conducted has been collected. Attention has been focused on the role of selected ion channels in this process.

Machine Learning to Predict Binding Affinity

Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic coordinates of protein-ligand complexes. These new computational tools made application of a broad spectrum of machine-learning techniques to study protein-ligand interactions possible. The essential aspect of these machine-learning approaches is to train a new computational model by using technologies such as supervised machine-learning techniques, convolutional neural network, and random forest to mention the most commonly applied methods. In this chapter, we focus on supervised machine-learning techniques and their applications in the development of protein-targeted scoring functions for the prediction of binding affinity. We discuss the development of the program SAnDReS and its application to the creation of machine-learning models to predict inhibition of cyclin-dependent kinase and HIV-1 protease. Moreover, we describe the scoring function space, and how to use it to explain the development of targeted scoring functions.