Development of Potential Inhibitors for Human T-lymphotropic Virus Type I Integrase Enzyme: A Molecular Modeling Approach

INTRODUCTION

Integration of viral DNA into the host cell genome, carried out by the HTLV-1 integrase enzyme, is a crucial step in the Human T-lymphotropic Virus type I (HTLV-1) life cycle. Thus, HTLV-1 integrase is considered an attractive therapeutic target; however, no clinically effective inhibitors are available to treat HTLV-1 infection.

OBJECTIVE

The main objective was to identify potential drug-like compounds capable of effectively inhibiting HTLV-1 integrase activity.

METHODS

In this study, a model of HTLV-1 integrase structure and three integrase inhibitors (dolutegravir, raltegravir, and elvitegravir as scaffolds) were used for designing new inhibitors. Designed molecules were used as templates for virtual screening to retrieve new inhibitors from PubChem, ZINC15, and ChEMBL databases. Drug-likeness and docked energy of the molecules were investigated using the SWISS-ADME portal and GOLD software. Stability and binding energy of the complexes were further investigated using molecular dynamic (MD) simulation.

RESULTS

Four novel potential inhibitors were developed using a structure-based design protocol and three compounds from virtual screening. They formed hydrogen bonding interactions with critical residues Asp69, Asp12, Tyr96, Tyr143, Gln146, Ile13, and Glu105. In addition, π stacking, halogen, and hydrogen bond interactions were seen between compounds (especially halogenated benzyl moieties) and viral DNA similar to those seen in parent molecules. MD simulation confirmed higher stability of the receptor-ligand complex than the ligand-free enzyme.

CONCLUSION

Combing structure-based design and virtual screening resulted in identifying three drug-like molecules (PubChem CID_138739497, _70381610, and _140084032) that are suggested as lead compounds for developing effective drugs targeting HTLV-1 integrase enzyme.

Ligand-Based Drug Design of Genipin Derivatives with Cytotoxic Activity against HeLa Cell Line: A Structural and Theoretical Study

Cervical cancer is a malignant neoplastic disease, mainly associated to HPV infection, with high mortality rates. Among natural products, iridoids have shown different biological activities, including cytotoxic and antitumor effects, in different cancer cell types. Geniposide and its aglycone Genipin have been assessed against different types of cancer. In this work, both iridoids were evaluated against HeLa and three different cervical cancer cell lines. Furthermore, we performed a SAR analysis incorporating 13 iridoids with a high structural similarity to Geniposide and Genipin, also tested in the HeLa cell line and at the same treatment time. Derived from this analysis, we found that the dipole moment (magnitude and direction) is key for their cytotoxic activity in the HeLa cell line. Then, we proceeded to the ligand-based design of new Genipin derivatives through a QSAR model (R2 = 87.95 and Q2 = 62.33) that incorporates different quantum mechanic molecular descriptor types (ρ, ΔPSA, ∆Polarizability2, and logS). Derived from the ligand-based design, we observed that the presence of an aldehyde or a hydroxymethyl in C4, hydroxyls in C1, C6, and C8, and the lack of the double bond in C7-C8 increased the predicted biological activity of the iridoids. Finally, ten simple iridoids (D9, D107, D35, D36, D55, D56, D58, D60, D61, and D62) are proposed as potential cytotoxic agents against the HeLa cell line based on their predicted IC50 value and electrostatic features.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

Design of Inhibitors of the Intrinsically Disordered Protein NUPR1: Balance between Drug Affinity and Target Function

Intrinsically disordered proteins (IDPs) are emerging as attractive drug targets by virtue of their physiological ubiquity and their prevalence in various diseases, including cancer. NUPR1 is an IDP that localizes throughout the whole cell, and is involved in the development and progression of several tumors. We have previously repurposed trifluoperazine (TFP) as a drug targeting NUPR1 and, by using a ligand-based approach, designed the drug ZZW-115 starting from the TFP scaffold. Such derivative compound hinders the development of pancreatic ductal adenocarcinoma (PDAC) in mice, by hampering nuclear translocation of NUPR1. Aiming to further improve the activity of ZZW-115, here we have used an indirect drug design approach to modify its chemical features, by changing the substituent attached to the piperazine ring. As a result, we have synthesized a series of compounds based on the same chemical scaffold. Isothermal titration calorimetry (ITC) showed that, with the exception of the compound preserving the same chemical moiety at the end of the alkyl chain as ZZW-115, an increase of the length by a single methylene group (i.e., ethyl to propyl) significantly decreased the affinity towards NUPR1 measured in vitro, whereas maintaining the same length of the alkyl chain and adding heterocycles favored the binding affinity. However, small improvements of the compound affinity towards NUPR1, as measured by ITC, did not result in a corresponding improvement in their inhibitory properties and in cellulo functions, as proved by measuring three different biological effects: hindrance of the nuclear translocation of the protein, sensitization of cells against DNA damage mediated by NUPR1, and prevention of cancer cell growth. Our findings suggest that a delicate compromise between favoring ligand affinity and controlling protein function may be required to successfully design drugs against NUPR1, and likely other IDPs.

Computational Drug Design Applied to the Study of Metabotropic Glutamate Receptors

Metabotropic glutamate (mGlu) receptors are a family of eight GPCRs that are attractive drug discovery targets to modulate glutamate action and response. Here we review the application of computational methods to the study of this family of receptors. X-ray structures of the extracellular and 7-transmembrane domains have played an important role to enable structure-based modeling approaches, whilst we also discuss the successful application of ligand-based methods. We summarize the literature and highlight the areas where modeling and experiment have delivered important understanding for mGlu receptor drug discovery. Finally, we offer suggestions of future areas of opportunity for computational work.

Design of Selective Benzoxazepin PI3Kδ Inhibitors Through Control of Dihedral Angles

A novel selective benzoxazepin inhibitor of PI3Kδ has been discovered. Beginning from compound 3, an αPI3K inhibitor, we utilized structure-based drug design and computational analysis of dihedral torsion angles to optimize for PI3Kδ isoform potency and isoform selectivity. Further medicinal chemistry optimization of the series led to the identification of 24, a highly potent and selective inhibitor of PI3Kδ.

The use of fake ligands from computational solvent mapping in ligand and structure-based virtual screening

AIM

Virtual screening selects compounds that resemble a known modulator or compounds that fit into the binding site of a target protein. Computational solvent mapping defines important chemical features for binding to a target protein. Results/methodology: We have tested the ability to use solvent mapping for generating a 'fake' ligand that is a negative image of the binding site. We used this fake ligand as a query for the program ROCS and to define the search space of the docking programs FRED and HYBRID.

CONCLUSION

The fake ligands perform comparably to or better than the ligands from crystal structures across a set of ten targets. Thus, the approach is suitable for guiding virtual screening and hit-to-lead optimization.