An Integrated in silico Approach and in vitro Study for the Discovery of Small‐Molecule USP7 Inhibitors as Potential Cancer Therapies

Abstract

The ubiquitin‐specific protease 7 (USP7) is a highly promising well‐validated target for a variety of malignancies. USP7 is critical in regulating the tumor suppressor p53 along with numerous epigenetic modifiers and transcription factors. Previous studies showed that USP7 inhibitors led to increased levels of p53 and anti‐proliferative effects in hematological and solid tumor cell lines. Thus, this study aimed to identify potent and safe USP7 hit inhibitors as potential anti‐cancer therapeutics via an integrated computational approach that combines pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations and post‐MD free energy calculations. In this study, the crystal structure of USP7 has been extensively investigated using a combination of three different chemical pharmacophore modeling approaches. We then screened ∼220.000 drug‐like small molecule library and the hit ligands predicted to be nontoxic were evaluated further. The identified hits from each pharmacophore modeling study were further examined by 1‐ns short MD simulations and MM/GBSA free energy analysis. In total, we ran 1 ns MD simulations for 1137 selected on small compounds. Based on their average MM/GBSA scores, 18 ligands were selected for 50 ns MD simulations along with one highly potent USP7 inhibitor used as a positive control. The in vitro enzymatic inhibition assay testing of our lead 18 molecules confirmed that 7 of these molecules were successful in USP7 inhibition. Screening results showed that within the used screening approaches, the most successful one was structure‐based pharmacophore modeling with the success rate of 75 %. The identification of potent and safe USP7 small molecules as potential inhibitors is a step closer to finding appropriate effective therapies for cancer. Our lead ligands can be used as a scaffold for further structural optimization and development, enabling further research in this promising field.

Identifying Potential SOS1 Inhibitors via Virtual Screening of Multiple Small Molecule Libraries against KRAS‐SOS1 Interaction

“…Values higher than 0.5 indicate potentially active compounds against cancer. [34] We received 91 molecules that were predicted as active against cancer. These identified hits may be considered to have promising pharmacokinetic profiles since they have already been cleared by the filters pan assay interference compounds (PAINS), rapid elimination of swill (REOS) (Table 1).…”

Developing Dynamic Structure‐Based Pharmacophore and ML‐Trained QSAR Models for the Discovery of Novel Resistance‐Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

“…Additionally, the virtual screening strategy is a fundamental method that our research team routinely uses in Lab for Innovative Drugs (Lab4IND) at HITMER and provides an efficient and fast method to evaluate candidate compounds. [26][27][28][29] These approaches offer a rational and efficient means to identify compounds with potent inhibitory activity against RET and improved therapeutic properties. Thus, in the current study, a new computational approach developed by our research group is used for the identification of new selective and resistance-free RET inhibitors from ultra-large ligand libraries.…”

“…Computational methods, including QSAR modeling, pharmacophore modeling, and MD simulations, provide a valuable avenue for the discovery of novel RET inhibitors. Additionally, the virtual screening strategy is a fundamental method that our research team routinely uses in Lab for Innovative Drugs (Lab4IND) at HITMER and provides an efficient and fast method to evaluate candidate compounds [26–29] . These approaches offer a rational and efficient means to identify compounds with potent inhibitory activity against RET and improved therapeutic properties.…”

Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison

“…Virtual screening can be divided into two categories, structure-based and ligand-based, depending on whether the three-dimensional structure of the target is known. These methods have now been successfully applied to discover USP7 inhibitors aiming at reducing costs and speeding up time in several studies [5][6][7][8]. However, the accuracy of the traditional virtual screening methods applied in these studies are not satisfactory and need to be further improved.…”