An AI-driven approach for nanobody affinity maturation
Yang, X.; Guo, Z.; Zhao, Q.
Show abstract
B7-H3 (CD276), an immunoregulatory checkpoint molecule overexpressed in numerous cancers, is a promising therapeutic target. Nanobodies possess unique advantages for targeting B7-H3, such as small size, high stability, and the ability to bind cryptic epitopes. However, the rational affinity maturation of these nanobodies is challenging, especially in the absence of detailed structural data on antigen-antibody interactions. Here, we present a computational strategy that leverages artificial intelligence (AI) and molecular modeling, including homology modeling, molecular docking, and free-energy calculations--to systematically predict affinity-enhancing mutations for humanized anti-B7-H3 nanobodies. This AI-driven framework provides a powerful and cost-effective pipeline for accelerating the development of high-affinity nanobody therapeutics prior to experimental validation.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Enhancement of antibody thermostability and affinity by computational design in the absence of antigen 96%
- Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope 95%
- Development of potent humanized TNFα inhibitory nanobodies for therapeutic applications in TNFα-mediated diseases 95%
Similar papers in this journal
Similar papers in this journal
- Improving antibody thermostability based on statistical analysis of sequence and structural consensus data 95%
- Generation of antagonistic biparatopic anti-CD30 antibody from an agonistic antibody by precise epitope determination and utilization of structural characteristics of CD30 molecule 93%
- Accelerated Antibody Discovery Targeting the SARS-CoV-2 Spike Protein for COVID-19 Therapeutic Potential 93%
Similar papers in this journal
- A novel consensus-based computational pipeline for rapid screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including omicron variant 94%
- Improved prediction of stabilizing mutations in proteins by incorporation of mutational effects on ligand binding 94%
- De novo design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein 94%
Similar papers in this journal
- Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. 95%
- Development of an Escape-resistant SARS CoV-2 Neutralizing Synthetic Nanobody 94%
- SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning 94%
"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.