Alberto Pérez

The paper introduces NEF, a standard for NMR data exchange, and demonstrates its application in structure determination, metabolomics, and ligand screening.

The paper introduces an active learning strategy using Thompson sampling to efficiently screen peptide libraries with AlphaFold-based binding predictions, achieving significant improvements over random sampling.

The paper investigates how sequence variations in BET ET domains influence paralog-specific peptide recognition through loop dynamics and conformational changes.

This study benchmarks eleven force fields on twelve peptides to assess their ability to model folding behaviors, revealing structural biases and limitations in capturing disorder and secondary structure.

This study uses multiscale modeling and cryo-EM to show that transient protein structures guide electron transfer in mitochondrial supercomplexes, improving energy conversion efficiency by 30%.

This study shows AlphaFold2 can learn some protein folding principles without MSAs or templates, revealing intermediate structures and a 'local first, global later' mechanism.

MDZip compresses molecular dynamics trajectories using neural networks, achieving high compression with minimal loss of structural and ensemble-level information.

This paper introduces HELM, a hybrid clustering method combining k-means efficiency with hierarchical flexibility, and demonstrates its application to protein-DNA and protein folding simulations.

MELD-Adapt dynamically evaluates data reliability to improve structure prediction accuracy by adjusting initial beliefs and identifying reliable data fractions.

PRIME improves identification of native-like protein structures from MD simulations by using extended similarity indices, achieving lower RMSD than traditional centroid-based methods.

This paper introduces a computational pipeline using AlphaFold2 to prioritize protein-protein binding candidates from high-throughput libraries and identify binding epitopes.