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Kementzidis, G.

Publications and source records attributed to Kementzidis, G..

2 recordsLinked to original sources

Structural and Dynamical Analyses of Apo and Cap-binding eIF4E: An in silico Study

Molecular dynamics (MD) simulations were utilized to investigate the dynamic behavior of eukaryotic translation initiation factor 4E (eIF4E) in its apo (ligand-free) and Cap-bound (complexed with mRNA Cap) forms. Specifically, we focused on the rearrangement of critical eIF4E residues, including W102, E103, W56, and key charged residues within the alpha helix, such as lysine and arginine. Three independent 1 {micro}s MD simulations were performed for four eIF4E structures based on an available Capbound eIF4E crystal structure (PDB: 4TPW) to sample protein conformations and explore metastable states. Metastable states were successfully sampled within the 1 {micro}s trajectories for all four structures; however, 2D-root-mean-square deviation (RMSD) and free-energy landscape (FEL) analyses indicate that additional trajectory data may be needed to fully explore the conformational space. Notably, the clearest state transitions were observed in the Cap-A simulations. Our data suggests that previously reported allosteric site eIF4E inhibitor 4EGI-1 stabilized the 7mGTP-eIF4E interaction by binding to a site dorsal from the eIF4G binding site, facilitating stable protein states even after ligand removal and promoting cleaner state transitions in simulations. Root-mean-square fluctuation (RMSF) and distance analyses aligned consistently with experimental results, affirming the validity of our MD simulation system. Observations on apo and Cap eIF4E structures revealed structural changes in one of the alpha helices (i.e., H3), particularly the transition from alpha helix to loop upon Cap binding. This transition was also observed during the cap to apo transition, where residues E105-R109 underwent structural rearrangements from loop to alpha helix. These findings support the strategy of designing agents targeting critical Cap-binding residues (W56, W102, E103) and H3 lysine residues (K106, K108) to disrupt the 7mGTP-eIF4E interaction, potentially informing future drug design efforts targeting this oncogenic protein. Future work will focus on studying the apo to Cap transition using available eIF4E apo structures, such as PDB entry 2GPQ, to deepen our understanding of eIF4Es metastable states during Cap binding.

biochemistry↗

An AI-Driven Framework for Discovery of BACE1 Inhibitors for Alzheimer's Disease

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that affects over 51 million individuals globally. The {beta}-secretase (BACE1) enzyme is responsible for the production of amyloid beta (A{beta}) plaques in the brain. The accumulation of A{beta} plaques leads to neuronal death and the impairment of cognitive abilities, both of which are fundamental symptoms of AD. Thus, BACE1 has emerged as a promising therapeutic target for AD. Previous BACE1 inhibitors have faced various issues related to molecular size and blood-brain barrier permeability, preventing any of them from maturing into FDA-approved AD drugs. In this work, a generative AI framework is developed as the first AI application to the de novo generation of BACE1 inhibitors. Through a simple, robust, and accurate molecular representation, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), and a Genetic Algorithm (GA), the framework generates and optimizes over 1,000,000 candidate inhibitors that improve upon the bioactive and pharmacological properties of current BACE1 inhibitors. Then, the molecular docking simulation models the candidate inhibitors and identifies 14 candidate drugs that exhibit stronger binding interactions to the BACE1 active site than previous candidate BACE1 drugs from clinical trials. Overall, the framework successfully discovers BACE1 inhibitors and candidate AD drugs, accelerating the developmental process for a novel AD treatment.

biochemistry↗