A Large Language Model Guides the Affinity Maturation of Variant Antibodies Generated by Combinatorial Optimization
Ashraf, F. B.; Zhang, Z.; Paco, K.; Mendivil, M. P.; Lay, J. A.; Ray, A.; Lonardi, S.
Show abstract
The ability of an antibody to bind an antigen with high specificity and strength (i.e., its binding affinity) are critical properties in the design of neutralizing antibodies. Recent technical advances in AI and a surge of experimental data on antigen-antibody interaction are driving innovations in the design and optimization of antibodies via affinity maturation. Here we introduce Ab-Affinity, a novel large language model which can accurately predict the binding affinity of specific antibodies against a target peptide within the SARS-CoV-2 spike protein. When used in conjunction with a genetic algorithm and simulated annealing, Ab-Affinity can generate novel antibodies with more than a 160-fold increase in predicted binding affinity compared to those obtained experimentally. Our experimental results show that the synthetic antibodies produced by Ab-Affinity have strong predicted biophysical properties. Molecular docking and molecular dynamics simulation of binding interactions of the best synthetic antibodies show enhanced interactions and stability on the target peptide epitope. In general, antibodies generated by Ab-Affinity are superior to those obtained with other existing computational methods.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Exploring the Potential of Structure-Based Deep Learning Approaches for T cell Receptor Design 96%
- Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics 96%
- THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications 96%
Similar papers in this journal
- BioPhi: A platform for antibody design, humanization and humanness evaluation based on natural antibody repertoires and deep learning 96%
- Towards generalizable prediction of antibody thermostability using machine learning on sequence and structure features 96%
- Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope 95%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.