Franziska Seeger

MIMOSA: Guiding De Novo Antibody Design with Natural Interaction Fingerprints

MIMOSA improves de novo antibody design by constraining generative models to known binder interfaces, achieving validated binders on targets where unconstrained methods fail.

DyAb: sequence-based antibody design and property prediction in a low-data regime

DyAb predicts antibody properties and designs new sequences with high binding affinity using a low-data approach.

CDR Conformation Aware Antibody Sequence Design with ConformAb

ConformAb designs antibody sequences that maintain CDR canonical conformations, improving binding affinity without target-specific data.

Rational design of oxidation-resistant antibodies through local electrostatic modulation

This paper develops a machine learning model to predict Trp oxidation risk in antibodies and shows that modulating electrostatic potential reduces oxidation while maintaining binding affinity.

Lab-in-the-loop therapeutic antibody design with deep learning

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

Improving Generalizability of Protein Sequence Models with Data Augmentations

The paper improves protein sequence model generalizability by applying data augmentations and contrastive learning, showing performance gains across multiple tasks.

Feature Design for Protein Interface hotspots using KFC2 and Rosetta

The paper combines KFC2a features with Rosetta scoring terms to improve prediction of protein interface hotspots using machine learning.