Abstract
The project proposal aims to investigate the dynamics associated with ligand binding on dipeptidyl peptidases (DPP) 4, 8, and 9. Despite biological interest in these systems, obtaining inhibition selectivity remains a challenge, given the similar active site architectures. However, very recently, compounds have been synthesized that are 10-100 orders of magnitude more selective for DPP9 than for DPP8 and DPP4. Although this is very promising, the issue remains that we do not understand the physicochemical and structural reasons for this selectivity. To address this lack of understanding, the proposal aims to investigate the dynamics of ligand binding to DPP4, 8, and 9, using a combination of molecular dynamics (MD)-based simulations and deep learning (DL) techniques. By generating large datasets of MD trajectories and using DL to analyse these simulations, key patterns that influence ligand binding will be investigated. The project will also focus on the functional role of the two channels that link the internal binding pocket with the solvent, with the aim of then identifying small molecules capable of binding in one of the channels. Several studies have used MD to study the dynamics of DPPs and to identify key residues involved in ligand binding. However, there have been no studies that have adapted DL techniques to investigate ligand binding dynamics in DPPs. This project is a collaboration between the Laboratory of Medicinal Chemistry (FBD; UA) and IDLab (UA/IMEC).
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