Application of a simple quantum chemical approach to ligand fragment scoring for Trypanosoma brucei pteridine reductase 1 inhibition

Structure and dynamics of pteridine reductase 1: the key phenomena relevant to enzyme function and drug design

Pteridine reductase 1 (PTR1) is a folate and pterin pathway enzyme unique for pathogenic trypanosomatids. As a validated drug target, PTR1 has been the focus of recent research efforts aimed at finding more effective treatments against human parasitic diseases such as leishmaniasis or sleeping sickness. Previous PTR1-centered structural studies highlighted the enzyme characteristics, such as flexible regions around the active site, highly conserved structural waters, and species-specific differences in pocket properties and dynamics, which likely impacts the binding of natural substrates and inhibitors. Furthermore, several aspects of the PTR1 function, such as the substrate inhibition phenomenon and the level of ligand binding cooperativity in the enzyme homotetramer, likely related to the global enzyme dynamics, are poorly known at the molecular level. We postulate that future drug design efforts could greatly benefit from a better understanding of these phenomena through studying both the local and global PTR1 dynamics. This review highlights the key aspects of the PTR1 structure and dynamics relevant to structure-based drug design that could be effectively investigated by modeling approaches. Particular emphasis is given to the perspective of molecular dynamics, what has been accomplished in this area to date, and how modeling could impact the PTR1-targeted drug design in the future.

Anti-trypanosomatid structure-based drug design - lessons learned from targeting the folate pathway

INTRODUCTION

Trypanosomatidic parasitic infections of humans and animals caused by Trypanosoma brucei, Trypanosoma cruzi, and Leishmania species pose a significant health and economic burden in developing countries. There are few effective and accessible treatments for these diseases, and the existing therapies suffer from problems such as parasite resistance and side effects. Structure-based drug design (SBDD) is one of the strategies that has been applied to discover new compounds targeting trypanosomatid-borne diseases.

AREAS COVERED

We review the current literature (mostly over the last 5 years, searched in PubMed database on Nov 11^th 2021) on the application of structure-based drug design approaches to identify new anti-trypanosomatidic compounds that interfere with a validated target biochemical pathway, the trypanosomatid folate pathway.

EXPERT OPINION

The application of structure-based drug design approaches to perturb the trypanosomatid folate pathway has successfully provided many new inhibitors with good selectivity profiles, most of which are natural products or their derivatives or have scaffolds of known drugs. However, the inhibitory effect against the target protein(s) often does not translate to anti-parasitic activity. Further progress is hampered by our incomplete understanding of parasite biology and biochemistry, which is necessary to complement SBDD in a multiparameter optimization approach to discovering selective anti-parasitic drugs.

Extension of an Atom-Atom Dispersion Function to Halogen Bonds and Its Use for Rational Design of Drugs and Biocatalysts

A dispersion function Das in the form of a damped atom-atom asymptotic expansion fitted to ab initio dispersion energies from symmetry-adapted perturbation theory was improved and extended to systems containing heavier halogen atoms. To illustrate its performance, the revised Das function was implemented in the multipole first-order electrostatic and second-order dispersion (MED) scoring model. The extension has allowed applications to a much larger set of biocomplexes than it was possible with the original Das. A reasonable correlation between MED and experimentally determined inhibitory activities was achieved in a number of test cases, including structures featuring nonphysically shortened intermonomer distances, which constitute a particular challenge for binding strength predictions. Since the MED model is also computationally efficient, it can be used for reliable and rapid assessment of the ligand affinity or multidimensional scanning of amino acid side-chain conformations in the process of rational design of novel drugs or biocatalysts.

Revisiting the halogen bonding between phosphodiesterase type 5 and its inhibitors

Halogenated ligands are nowadays commonly designed in order to increase their potency against protein targets. Although novel computational methods of evaluating the affinity of such halogenated inhibitors have emerged, they still lack the sufficient accuracy, which is especially noticeable in the case of empirical scoring functions, being the method of choice in the drug design process. Here, we evaluated a series of halogenated inhibitors of phosphodiesterase type 5 with ab initio methods, revealing the physical nature of ligand binding and determining the components of interaction energy that are essential for proper inhibitor ranking. In particular, a nonempirical scoring model combining long-range contributions to the interaction energy provided a significant correlation with experimental binding potency, outperforming a number of commonly used empirical scoring functions. Considering the low computational cost associated with remarkable predictive abilities of the aforementioned model, it could be used for rapid assessment of the ligand affinity in the process of rational design of novel halogenated compounds.

A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics

Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.

Theoretical Model of EphA2-Ephrin A1 Inhibition

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds.