Multiscale Modeling of Two-Photon Probes for Parkinson's Diagnostics Based on Monoamine Oxidase B Biomarker

DFT, ADME studies and evaluation of the binding with HSA and MAO-B inhibitory potential of protoberberine alkaloids from Guatteria friesiana: theoretical insights of promising candidates for the treatment of Parkinson's disease

CONTEXT

Parkinson's disease is a chronic neurodegenerative condition that has no cure, characterized by the progressive degeneration of specific brain cells responsible for producing dopamine, a crucial neurotransmitter for controlling movement and muscle coordination. Parkinson's disease is estimated to affect around 1% of the world's population over the age of 60, but it can be diagnosed at younger ages. One of the treatment strategies for Parkinson's disease involves the use of drugs that aim to increase dopamine levels or simulate the action of dopamine in the brain. A class of commonly prescribed drugs are the so-called monoamine oxidase B (MAO-B) inhibitors due to the fact that this enzyme is responsible for metabolizing dopamine, thus reducing its levels in the brain. Studies have shown that berberine-derived alkaloids have the ability to selectively inhibit MAO-B activity, resulting in increased dopamine availability in the brain. In this context, berberine derivatives 13-hydroxy-discretinine and 7,8-dihydro-8-hydroxypalmatine, isolated from Guatteria friesiana, were evaluated via density functional theory followed by ADME studies, docking and molecular dynamic simulations with MAO-B, aiming to evaluate their anti-Parkinson potential, which have not been reported yet. Docking simulations with HSA were carried out aiming to evaluate the transport of these molecules through the circulatory system.

METHODS

The 3D structures of the berberine-derived alkaloids were modeled via the DFT approach at B3LYP-D3(BJ)/6-311 + + G(2df, 2pd) theory level using Gaussian 09 software. Solvation free energies were determined through Truhlar's solvation model. MEP and ALIE maps were generated with Multiwfn software. Autodock Vina software was used for molecular docking simulations and analysis of the interactions in the binding sites. The 3D structure of MAO-B was obtained from the Protein Data Bank website under PDB code 2V5Z. For the interaction of studied alkaloids with human serum albumin (HSA) drug sites, 3D structures with PDB codes 2BXD, 2BXG, and 4L9K were used. Molecular dynamics simulations were carried out using GROMACS 2019.4 software, with the GROMOS 53A6 force field at 100 ns simulation time. The estimation of the ligand's binding free energies was obtained via molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method.

Structure-Based Design of Novel MAO-B Inhibitors: A Review

With the significant growth of patients suffering from neurodegenerative diseases (NDs), novel classes of compounds targeting monoamine oxidase type B (MAO-B) are promptly emerging as distinguished structures for the treatment of the latter. As a promising function of computer-aided drug design (CADD), structure-based virtual screening (SBVS) is being heavily applied in processes of drug discovery and development. The utilization of molecular docking, as a helping tool for SBVS, is providing essential data about the poses and the occurring interactions between ligands and target molecules. The current work presents a brief discussion of the role of MAOs in the treatment of NDs, insight into the advantages and drawbacks of docking simulations and docking software, and a look into the active sites of MAO-A and MAO-B and their main characteristics. Thereafter, we report new chemical classes of MAO-B inhibitors and the essential fragments required for stable interactions focusing mainly on papers published in the last five years. The reviewed cases are separated into several chemically distinct groups. Moreover, a modest table for rapid revision of the revised works including the structures of the reported inhibitors together with the utilized docking software and the PDB codes of the crystal targets applied in each study is provided. Our work could be beneficial for further investigations in the search for novel, effective, and selective MAO-B inhibitors.

Computational Investigations into Two-Photon Fibril Imaging Using the DANIR-2c Probe

The design of novel fibril imaging molecules for medical diagnosis requires the simultaneous optimization of fibril-specific optical properties and binding specificity toward amyloid fibrils. Because of the possibility to monitor internal organs and deep tissues, the two-photon probes that can absorb in the infrared (IR) and near-IR (NIR) region with a significant two-photon absorption cross section are of immense interest. To contribute to this exploration of chemical compounds suitable for two-photon fibril imaging, we have computationally studied the one- and two-photon properties of a donor-acceptor-substituted DANIR-2c probe, which was used for in vivo detection of β-amyloid deposits using fluorescence spectroscopy. In particular, a multiscale computational approach was employed involving molecular docking, molecular dynamics, hybrid QM/MM molecular dynamics, and coupled-cluster/MM to study the binding of the studied probe to amyloid fibril and its one- and two-photon absorption properties in the fibrillar environment. Multiple binding sites are available for this probe in amyloid fibril, and the one corresponding to the largest binding affinity exhibits also the largest and experimentally meaningful two-photon absorption cross section, thus demonstrating the potential of the studied probe in two-photon microscopy.

Computationally Assisted Lead Optimization of Novel Potent and Selective MAO-B Inhibitors

A series of dietary flavonoid acacetin 7-O-methyl ether derivatives were computationally designed aiming to improve the selectivity and potency profiles against monoamine oxidase (MAO) B. The designed compounds were evaluated for their potential to inhibit human MAO-A and -B. Compounds 1c, 2c, 3c, and 4c were the most potent with a Ki of 37 to 68 nM against MAO-B. Compounds 1c–4c displayed more than a thousand-fold selectivity index towards MAO-B compared with MAO-A. Moreover, compounds 1c and 2c showed reversible inhibition of MAO-B. These results provide a basis for further studies on the potential application of these modified flavonoids for the treatment of Parkinson’s Disease and other neurological disorders.

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson’s disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson’s disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.