Bora Guloglu

AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks

AbBFN2 is a generative antibody foundation model that unifies diverse data sources and enables flexible conditional generation for multiple antibody design tasks.

Protein Sequence Modelling with Bayesian Flow Networks

This paper introduces ProtBFN, a Bayesian Flow Network for protein sequence generation, which outperforms existing models and is fine-tuned for antibody generation.

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

The paper evaluates TCR structure prediction models using a large dataset, finds comparable structural diversity in alpha and beta chains, and provides over 1.5M predicted structures for further analysis.

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

The paper updates a tool to assess developability risks of kappa and lambda antibodies using machine learning, finding lambda antibodies have higher risk but some are more tractable.

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

The paper shows that VL domain attributes influence CDR-H3 structure and variability through steric effects using crystal structures and simulations.