Peter M. Tessier

The paper introduces a dataset of VHH-Fc antibodies and shows that IgG-trained models outperform VHH-Fc-specific models in predicting developability properties.

This paper analyzes 160 bispecific antibodies and their parental arms to identify developability inheritance patterns and predictive models for bispecific antibody design.

This study develops HALA antibodies that adjust ADC delivery based on tumor HER2 expression, improving efficacy across varying expression levels in preclinical models.

The paper introduces a high-throughput platform to generate large datasets for training machine learning models to predict antibody developability properties.

This study identifies TDP43 autoregulation as a source of shortened isoforms, showing they are regulated by NMD and post-translational degradation, and their dysregulation leads to neurodegeneration.

The paper introduces PSERM scoring to predict antibody variant properties from deep sequencing data, showing it outperforms traditional methods in reproducibility and correlation with experimental measurements.

The paper identifies a high-homology light chain sequence from a hybridoma that is antigen-specific, highlighting challenges in variable domain sequencing.