Sai Pooja Mahajan

Property Enhancer - a data efficient multi-objective approach for functional antibody optimization

Property Enhancer optimizes antibody properties using a data-efficient framework, achieving significant affinity gains in both in silico and in vitro tests.

Contextual protein and antibody encodings from equivariant graph transformers

The paper introduces a method to learn protein and antibody residue representations using equivariant graph transformers, showing structural context can determine amino acid labels and improve antibody design.

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.

Discovery of a single-subunit oligosaccharyltransferase that enables glycosylation of full-length IgG antibodies in Escherichia coli

The paper identifies a single-subunit oligosaccharyltransferase from Desulfovibrio marinus that enables glycosylation of full-length IgG antibodies in E. coli.

Hallucinating structure-conditioned antibody libraries for target-specific binders

The paper introduces FvHallucinator, a deep learning model that generates antibody CDR libraries conditioned on structure, improving binding affinity and enriching for therapeutic candidates.

Induced fit with replica exchange improves protein complex structure prediction

The paper introduces ReplicaDock 2.0, a method that improves protein complex structure prediction by simulating induced-fit dynamics, achieving better accuracy for flexible targets.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

The paper introduces IgFold, a deep learning method for fast and accurate antibody structure prediction, achieving results comparable to AlphaFold in under a minute.