Jorge Fernández-de-Cossio-Díaz

The paper designs mutational paths for WW domains to switch specificity using computational methods and validates them experimentally, showing high success rates for structured paths over random ones.

This paper introduces ILMC, a sampling strategy for protein language models that improves diversity while steering generation toward desired properties.

The paper introduces LaRBMs to uncover a shared latent representation of spontaneous brain activity across individuals, showing functional cell assemblies are conserved in larval zebrafish.

This study uses Hidden Markov Models to link neural activity and behavior in zebrafish larvae, revealing state-based correlations between swimming patterns and circuit activation.

The paper designs RNA switches using Restricted Boltzmann machines and validates their structural switching properties through experimental testing.

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 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 introduces diffRBM, a transfer-learning model using Restricted Boltzmann Machines to predict antigen immunogenicity and T-cell receptor specificity, achieving performance comparable to existing methods.

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

The paper introduces a computational method to infer protein fitness landscapes from deep mutational scans, showing accurate predictions and structural insights.