Targeting imidazoline site on monoamine oxidase B through molecular docking simulations

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.

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.

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis

Background::

The enzyme trans-enoyl-[acyl carrier protein] reductase (InhA) is a central protein for the development of antitubercular drugs. This enzyme is the target for the pro-drug isoniazid, which is catalyzed by the enzyme catalase-peroxidase (KatG) to become active.

Objective::

Our goal here is to review the studies on InhA, starting with general aspects and focusing on the recent structural studies, with emphasis on the crystallographic structures of complexes involving InhA and inhibitors.

Method::

We start with a literature review, and then we describe recent studies on InhA crystallographic structures. We use this structural information to depict protein-ligand interactions. We also analyze the structural basis for inhibition of InhA. Furthermore, we describe the application of computational methods to predict binding affinity based on the crystallographic position of the ligands.

Results::

Analysis of the structures in complex with inhibitors revealed the critical residues responsible for the specificity against InhA. Most of the intermolecular interactions involve the hydrophobic residues with two exceptions, the residues Ser 94 and Tyr 158. Examination of the interactions has shown that many of the key residues for inhibitor binding were found in mutations of the InhA gene in the isoniazid-resistant Mycobacterium tuberculosis. Computational prediction of the binding affinity for InhA has indicated a moderate uphill relationship with experimental values.

Conclusion::

Analysis of the structures involving InhA inhibitors shows that small modifications on these molecules could modulate their inhibition, which may be used to design novel antitubercular drugs specific for multidrug-resistant strains.

Obsessive-compulsive Disorder with Comorbid Bipolar Disorders: Clinical Features and Treatment Implications

BACKGROUND

Obsessive-compulsive disorder (OCD) symptoms within the context of a bipolar disorder (BD) have been described since the 19th century. Interestingly, the existence of a relevant overlap between the aforementioned psychiatric syndromes has been confirmed by a number of recent epidemiological and family studies.

AIMS

The aim of the present paper is to review the clinical features and the therapeutic implications of the OCD-BD comorbidity.

DISCUSSION

In the last two decades, the frequent association between OCD and BD has been earning a growing interest given its relevant nosological and therapeutic implications. Usually patients suffering from OCD-BD comorbidity show a peculiar clinical course, characterized by a larger number of concomitant depressive episodes and episodic course. In these cases, the treatment with antidepressants is more likely to elicit hypomanic or manic switches, while mood stabilizers significantly improve the overall clinical picture. Moreover, OCD-BD patients are frequently comorbid with a number of other psychiatric disorders, in particular anxiety disorders, social phobia, and different substance abuses, such as alcohol, nicotine, caffeine and sedatives.

CONCLUSIONS

BD-OCD comorbidity needs further investigations in order to provide more solid evidences to give patients a more precise clinical diagnosis and a more targeted therapeutic approach.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

SAnDReS: A Computational Tool for Docking

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

Molegro Virtual Docker for Docking

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.

Homology Modeling of Protein Targets with MODELLER

Homology modeling is a computational approach to generate three-dimensional structures of protein targets when experimental data about similar proteins are available. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance spectroscopy successfully solved the structures of nearly 150,000 macromolecules, there is still a gap in our structural knowledge. We can fulfill this gap with computational methodologies. Our goal in this chapter is to explain how to perform homology modeling of protein targets for drug development. We choose as a homology modeling tool the program MODELLER. To illustrate its use, we describe how to model the structure of human cyclin-dependent kinase 3 using MODELLER. We explain the modeling procedure of CDK3 apoenzyme and the structure of this enzyme in complex with roscovitine.

Molecular Docking Simulations with ArgusLab

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Molecular aspects of monoamine oxidase B

Monoamine oxidases (MAO) influence the monoamine levels in brain by virtue of their role in neurotransmitter breakdown. MAO B is the predominant form in glial cells and in platelets. MAO B structure, function and kinetics are described as a background for the effect of alterations in its activity on behavior. The need to inhibit MAO B to combat decreased brain amines continues to drive the search for new drugs. Reversible and irreversible inhibitors are now designed using data-mining, computational screening, docking and molecular dynamics. Multi-target ligands designed to combat the elevated activity of MAO B in Alzheimer's and Parkinson's Diseases incorporate MAO inhibition (usually irreversible) as well as iron chelation, antioxidant or neuroprotective properties. The main focus of drug design is the catalytic activity of MAO, but the imidazoline I2 site in the entrance cavity of MAO B is also a pharmacological target. Endogenous regulation of MAO B expression is discussed briefly in light of new studies measuring mRNA, protein, or activity in healthy and degenerative samples, including the effect of DNA methylation on the expression. Overall, this review focuses on examples of recent research on the molecular aspects of the expression, activity, and inhibition of MAO B.