Fernando Luís Barroso da Silva

ARID-sf improves antibody-antigen docking model ranking by combining force fields, structural features, and embeddings in a deep learning framework, outperforming existing methods across diverse scenarios.

The paper introduces a protein language model fine-tuned to predict antigen-specific B cell receptors from immune repertoires, showing effectiveness across multiple antigens.

ANABAG provides a curated dataset of antibody-antigen complexes with structural and physicochemical features to improve Ab-Ag modeling and antibody engineering.

The paper uses in silico methods to study the self-assembly of norovirus capsid proteins and revises a previous assembly model.

The paper compares membrane interactions of NS1 proteins from two Zika virus strains to explain their differential virulence using molecular dynamics simulations.

The paper examines the self-association of NS1 proteins from Zika, Dengue, and West Nile viruses using biophysical simulations, revealing pH-dependent oligomerization and distinct interaction profiles.

The paper investigates molecular mechanisms of increased virulence in SARS-CoV-2 variants by analyzing the stability of the spike trimer in different conformational states.