Ab initio study of electronic interaction energies and desolvation energies for dopaminergic ligands in the catechol-O-methyltransferase active site

Investigating Paracetamol's Role as a Potential Treatment for Parkinson's Disease: Ab Initio Analysis of Dopamine, l-DOPA, Paracetamol, and NAPQI Interactions with Enzymes Involved in Dopamine Metabolism

Recently, it was found that paracetamol can extend the therapeutic window of l-DOPA treatment for Parkinson's disease [Golding (2019) BJPharm, 4(2), Article 619]. It has been posited that the effect could be due to paracetamol and its metabolite, NAPQI, inhibiting pain signals in the spinal column. In this work, we examine the possibility that the therapeutic effect of the paracetamol for the Parkinson's disease patient may be due to an inhibition of the enzymes that metabolize dopamine and/or l-DOPA, thus effectively extending the lifetime of the l-DOPA treatment. In this work, we use the M062X/6-311+G* level of theory to calculate the electronic binding energies (including explicit desolvation) of several ligands (paracetamol, NAPQI, dopamine, and l-DOPA) with a series of enzymes important to the production and metabolism of dopamine and compare them to calculated binding energy values for the natural substrates for those enzymes in order to predict possible inhibition. Benchmark interaction energies for a subset of the systems studied are calculated using the more accurate second-order Møller-Plesset perturbation (MP2) method in order to calibrate the accuracy of the M062X method. If we assume that the interaction energies calculated here can serve as a proxy for in vivo inhibition, then we can predict that paracetamol and NAPQI should not inhibit the natural production of dopamine and may in fact inhibit the metabolism of l-DOPA and dopamine, thus extending the length of l-DOPA treatments.

Synthesis and analysis of novel catecholic ligands as inhibitors of catechol-O-methyltransferase

L-DOPA, a dopamine precursor, is commonly used as a treatment for patients with conditions such as Parkinson's disease. This therapeutic L-DOPA, as well as the dopamine derived from L-DOPA, can be deactivated via metabolism by catechol-O-methyltransferase (COMT). Targeted inhibition of COMT prolongs the effectiveness of L-DOPA and dopamine, resulting in a net increase in pharmacological efficiency of the treatment strategy. Following the completion of a previous ab initio computational analysis of 6-substituted dopamine derivatives, several novel catecholic ligands with a previously unexplored neutral tail functionality were synthesized in good yields and their structures were confirmed. The ability of the catecholic nitriles and 6-substituted dopamine analogues to inhibit COMT was tested. The nitrile derivatives inhibited COMT most effectively, in agreement with our previous computational work. pKa values were used to further examine the factors involved with the inhibition and molecular docking studies were performed to support the ab initio and experimental work. The nitrile derivatives with a nitro substituent show the most promise as inhibitors, confirming that both the neutral tail and the electron withdrawing group are essential on this class of inhibitors.

Cheminformatic quantum mechanical enzyme model design: A catechol-O-methyltransferase case study

To accurately simulate the inner workings of an enzyme active site with quantum mechanics (QM), not only must the reactive species be included in the model but also important surrounding residues, solvent, or coenzymes involved in crafting the microenvironment. Our lab has been developing the Residue Interaction Network Residue Selector (RINRUS) toolkit to utilize interatomic contact network information for automated, rational residue selection and QM-cluster model generation. Starting from an x-ray crystal structure of catechol-O-methyltransferase, RINRUS was used to construct a series of QM-cluster models. The reactant, product, and transition state of the methyl transfer reaction were computed for a total of 550 models, and the resulting free energies of activation and reaction were used to evaluate model convergence. RINRUS-designed models with only 200-300 atoms are shown to converge. RINRUS will serve as a cornerstone for improved and automated cheminformatics-based enzyme model design.