Raphael R. Eguchi

ADAPT-M rapidly quantifies protein binder affinities and kinetics using microfluidics, enabling efficient selection of high-affinity variants from yeast-display libraries.

The paper introduces Ig-VAE, a variational auto-encoder that generates 3D coordinates of immunoglobulin proteins, demonstrating its use in creating a SARS-CoV2-RBD binder and guiding protein design.

A 1.2B-parameter autoregressive model trained on 280M tagged sequences completes held-out proteins with near-native Rosetta energies and ranks high-fitness GB1 variants without any GB1 training.

This paper designs protein sequences using a deep neural network trained on crystal structures, producing stable designs that match high-resolution structures.

This paper introduces a deep learning method to classify protein structures into architectural classes using semantic segmentation, achieving high accuracy in fold identification and structural quality assessment.