J. Fernandez-de-Cossio-Diaz

This paper introduces a biophysically-inspired model to predict the viability of AAV2 capsid variants using DMS data, evaluating its performance on log-selectivity and binary classification.

This paper develops a multi-stage approach combining phage display experiments, machine learning, and biophysical modeling to design antibodies with customized specificity profiles.

This paper introduces AnnealDCA, a machine-learning framework to infer protein fitness landscapes from sequencing data, demonstrating its effectiveness on antibody Rep-Seq and screening experiments.

This study shows formate induces a metabolic switch increasing adenine nucleotides, glycolysis, and repressing AMPK, with implications for cancer metabolism.

The paper develops a physical model of cell metabolism that explains three energy demands and aligns with experimental data on growth dependencies.