Thomas Bikias

CAPO automates and selects protein-model adaptations by measuring feasibility and cost before training, improving performance while reducing compute usage and failures.

The paper predicts viral host range using a combination of experimental screening and deep learning to identify potential animal reservoirs for reverse zoonotic transmission.

The paper uses deep mutational learning to analyze serum antibody escape to SARS-CoV-2 variants, finding individualized escape profiles and conserved binding signatures.

This study designs antibody Fc-variants with programmable functional profiles using deep learning and experimental validation.

This study uses synthetic coevolution to explore mutational trajectories between neutralizing antibodies and SARS-CoV-2 variants, revealing antagonistic and compensatory mutations.

PLMFit benchmarks transfer learning methods on protein language models for protein engineering tasks, identifying optimal strategies for knowledge transfer.