Mason Minot

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.

This paper introduces nucleotide augmentation (NTA) to improve machine learning models for protein engineering by generating biologically meaningful sequence data, achieving performance gains even with limited training data.

The paper develops a reduced order mathematical model of the human complement system that is predictive and computationally efficient, showing that multiple therapeutic targets are needed to disrupt complement activation.