Biology subjects

Sayyah, E.

Publications and source records attributed to Sayyah, E..

3 recordsLinked to original sources

bioRxiv2024

Employing Steered MD Simulations for Effective Virtual Screening: Active Pharmacophore Search by Dynamic Corrections to target MKK3-MYC Interactions

The intricate relationship between mitogen-activated protein kinase 3 (MKK3) and MYC proto-oncogene protein (MYC) activation holds deep implications for the progression of cancer, particularly in the context of triple negative breast cancer (TNBC). Despite significant progress, the challenge of discovering effective MYC-targeted drugs persists, demanding innovative approaches to control MYC-dependent malignancies. A promising avenue in this pursuit involves disrupting the protein-protein interactions (PPIs) between MKK3 and MYC. The significance of this interaction is emphasized by the activation of MYC by MKK3 in diverse cell types, presenting a novel perspective for therapeutic interventions in MYC-driven pathways. In the current study, a novel in silico strategy to screen small molecule libraries that target the MKK3-MYC interaction was conducted. Dynamic structure-based pharmacophore models were developed and utilized for screening the small molecule libraries, enabling the identification of compounds exhibiting favorable alignment with the defined pharmacophore features. Subsequently, physics-based simulations approaches were conducted on these selected hit molecules. Steered molecular dynamics (sMD) simulations were utilized to assess the correlation between the necessary forces to dissociate candidate hit ligands from the binding pocket and their corresponding average binding free energies (MM/GBSA). Comparative analysis of the average binding free energies of the identified hits obtained from the small molecule libraries represent that the identified compounds have promising predicted binding affinities compared to the reference molecule SGI-1027. Therefore, these findings may signify a crucial advancement in our ability to control MYC activation in cancer.

bioinformatics↗

bioRxiv2024

Deep Learning-Driven Discovery of FDA-Approved BCL2 Inhibitors: In Silico Analysis Using a Deep Generative Model NeuralPlexer for Drug Repurposing in Cancer Treatment

Finding strong inhibitors of the BCL2 target, which is essential for controlling apoptosis and ensuring the survival of cancer cells, has prompted research into FDA-approved drugs. This study uses an advanced deep generative model called NeuralPlexer to produce protein-ligand complex conformations one-by-one and perform in silico analysis. This is the first time in the literature using NeuralPlexer in virtual screening, as we comprehensively evaluate the conformations of an FDA-approved drug library to ascertain their potential efficacy in suppressing BCL2 by utilizing NeuralPlexers capabilities. Obtained results were re-confirmed by physics-based molecular simulations and neural relational inference (NRI) analysis. Our study reveals several intriguing candidates such as Lathyrol and Fadrozole with potent inhibitory interactions with the BCL2 target, offering important new information for repurposing currently available drugs in cancer treatment. This work highlights the promise of deep learning technology in pharmaceutical research by integrating NeuralPlexer into the process of drug development, while also improving the accuracy of predictions made about protein-ligand interactions.

bioinformatics↗

bioRxiv2023

Dynamic Structure-based Pharmacophore Models for Virtual Screening of Small Molecule Libraries Targeting the YB-1

In drug discovery, ligand-based techniques offer rapid screening, whereas structure-based approaches provide deeper insights but are time-consuming. Hybrid methods like structure-based pharmacophore models combine advantages for accurate screening of large ligand libraries. However, there are substantial limits to build structure-based pharmacophore models. Static models relying on a single co-crystallized structure or docking pose often fall short in capturing the dynamic nature of binding interactions. In this study, we present dynamic structure-based pharmacophore models, aimed at better representing physiological conditions and addressing these challenges. The urgent need for improved cancer treatment has led to the search for new chemotherapeutic strategies. Y box binding protein 1 (YB-1) is a multifunctional protein associated with tumor progression and treatment resistance in various cancers. For the first time in the literature, our study utilizes a known small molecule YB-1 inhibitor (SU056) bound to the active regions of the RNA-binding sites to develop dynamic structure-based pharmacophores. These models were then used in the screening of large ligand libraries.

biophysics↗