Identifier: TFG:9355
Authors: Miguel Rodríguez, Ignacio
Abstract:
Proteins are crucial macromolecules in biological systems, taking part in diverse functions such as structural support, enzymatic activity, and signaling. To do so, proteins often interact with ligands. Intermolecular recognition is a key process to various biological functions, where interactions governed by non-covalent forces such as hydrogen bonds, Van der Waals forces, electrostatic interactions, and hydrophobic effects, are essential. The ligand’s binding affinity, a measure of the strength of these interactions, can be described using parameters like , , and � �50. These metrics along with interaction data are pivotal in drug design, offering valuable information about the potency and stability of protein-ligand complexes. Recent works in computer-aided drug discovery (CADD) have heavily transformed the process of drug development thanks to the usage of computational techniques like structure-based drug design (SBDD) and ligand-based drug design (LBDD). While SBDD uses the 3D structure of target proteins to design and screen potential ligands, LBDD builds models based on known ligands to predict interactions with target proteins. Both methods benefit from databases that catalog protein-ligand interactions, enabling more efficient drug development. This work proposes the development of a database that facilitates the study of protein-ligand affinities and interactions, aiding the refinement of CADD methodologies and that could be used to improve the prediction of binding affinities for drug discovery.
Repositori URV