In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates

Abstract

CD44 promotes metastasis, chemoresistance, and stemness in different types of cancer and is a target for the development of new anti-cancer therapies. All CD44 isoforms share a common N-terminal domain that binds to hyaluronic acid (HA). Herein, we used a computational approach to design new potential CD44 antagonists and evaluate their target-binding ability. By analyzing 30 crystal structures of the HA-binding domain (CD44HAbd), we characterized a subdomain that binds to 1,2,3,4-tetrahydroisoquinoline (THQ)-containing compounds and is adjacent to residues essential for HA interaction. By computational combinatorial chemistry (CCC), we designed 168,190 molecules and compared their conformers to a pharmacophore containing the key features of the crystallographic THQ binding mode. Approximately 0.01% of the compounds matched the pharmacophore and were analyzed by computational docking and molecular dynamics (MD). We identified two compounds, Can125 and Can159, that bound to human CD44HAbd (hCD44HAbd) in explicit-solvent MD simulations and therefore may elicit CD44 blockage. These compounds can be easily synthesized by multicomponent reactions for activity testing and their binding mode, reported here, could be helpful in the design of more potent CD44 antagonists.

ASHA:A Chemical Sapce Navigator app for Dual–Target-Directed-Inhibitor design

“…The Normalized Principal Moment of Inertia ratios (NPR) plot is a method for characterizing the shapes of molecules based on their rotational motion. Two studies, CD44 antagonist candidates for tetrahydroisoquinoline-based molecules (Ruiz-Moreno et al, 2021) and protein pocket shape comparison in virtual screening, show how important NPR can be in drug discovery (Wirth et al, 2013). This web-app's shape analysis module employs the RDKit Descriptors3D module to compute NPR 1 and NPR 2 (Sauer and Schwarz, 2003), which describe the molecular shape (rod, spherical, disc) for each molecule in both datasets based on ratios of the principal moments of inertia of a molecule…”

Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds

“…In MCRs, the combination of three or more different starting material groups allows for the quick and easy creation of large combinatorial libraries that accept a wide range of chemical functionality. MCR libraries have been shown to yield drug candidates from various chemical scaffolds [112][113][114]. Combinatorial library screening (either virtual or experimental) has proved to be an effective method in the hit-and-lead discovery phase.…”

Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses

“…In this context, the use of molecular descriptor analyses and machine learning models can be a good starting point, since these methods have been known to provide valuable insights into several different tasks in medicinal chemistry. [30][31][32][33] This is especially true when said models are paired with interpretation techniques that allow for understanding the contribution of each descriptor to the predictions made. [34,35] In this work, we aim to evaluate whether the observed lack of translation from cruzain inhibitors to T. cruzi active compounds can be explained as differences in interpretable physicochemical properties and molecular structural motifs.…”

“…The lack of translation from enzyme to biological activity is a problem of high importance and understanding it would allow for a more rational design of compounds with the desired activity profile against T. cruzi . In this context, the use of molecular descriptor analyses and machine learning models can be a good starting point, since these methods have been known to provide valuable insights into several different tasks in medicinal chemistry [30–33] . This is especially true when said models are paired with interpretation techniques that allow for understanding the contribution of each descriptor to the predictions made [34,35] …”