Study for energetic characterization with PELE with application to real cases

Apr 17, 2026

Ignasi Puch Giner defended his doctoral thesis, supervised by Víctor Guallar Tasies, at the North Campus on April 16, 2026. Titled “Optimization of Monte Carlo and Molecular Dynamics Techniques for Receptor–Ligand Binding Studies”, the thesis focuses on taking advantage of the theoretical framework of Monte Carlo methods to design a protocol applicable to the energetic characterization of ligand–protein interactions. In addition, these methodologies, combined with other computational techniques, have been applied in three collaborations oriented to real cases related to pathologies of the nervous system and the cardiac system.

Study for energetic characterization with PELE with application to real cases — Development of PELE energy characterization and applied case. In A and B we show the results we obtained when we applied the PELE energy characterization framework for the MCL1 system. In B and D we show the results obtained from a real use case of the PELE energy characterization framework. In (A), a correlation metric evolution of the MCL1 dataset (for both Pearson and Spearman) depending on the amount of sampling carried out in each simulation. In (B), the best correlation obtained for the MCL1 system applying the PELE energy characterization. In (C), a scatter plot shows the relationship between binding energy and total system energy. Green dots represent geometrically feasible poses with the lowest binding energies. In (D), a representative structure from the filtered poses in (A) is shown. The C-terminal peptide of LAMP1 (green) interacts with the globular region of the GDAP1 homodimer (blue).

Drug discovery is a complex and resource-intensive process that relies on identifying molecular candidates with optimal binding properties. This thesis advances computational strategies for biomolecular modeling by integrating physics-based molecular mechanics methods—such as Monte Carlo and molecular dynamics simulations—with state-of-the-art machine learning approaches. The objective is to improve scoring functions, enhance simulation accuracy, and develop an efficient framework applicable to real-world
pharmacological challenges.

A central contribution of this work is the systematic characterization of the PELE (Protein Energy Landscape Exploration) simulation framework, including the evaluation of binding energy estimators in terms of predictive performance and computational efficiency. These
benchmark studies address existing limitations in scoring methodologies and enable more reliable protein–ligand interaction predictions.

The proposed methodologies are validated through pharmacologically relevant case studies, including the structural characterization of the amino acid transporter Asc1/CD98hc, the exploration of novel drug–DNA interactions involving Meis1–Hoxb13 transcription factors, and the investigation of GDAP1–LAMP1 dysfunction in Charcot–Marie–Tooth disease. Collectively, this research establishes a scalable computational framework that bridges traditional molecular simulations and AI-driven predictions, with significant implications for both academic research and industrial drug discovery.

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