Felipe Vilicich

This paper develops a deep learning framework to predict protein flexibility from static structural data, achieving moderate accuracy and identifying key structural features associated with flexibility.

The paper introduces a protein language model-based method to predict conformational B-cell epitopes on antigen structures, outperforming existing methods on benchmark datasets.

The paper identifies extracellular components in the developing fetal neocortex of humans and non-human primates using multi-omics approaches.