Affinity maturation in the immune response is limited in terms of affinity gain, and natural antibodies often do not have the binding affinity required for therapeutic applications. Therefore, improving the binding affinity of antibodies is essential for developing antibody-based therapeutics. Designing antibodies using experimental methods is expensive in terms of cost and time owing to the large range of complementarity-determining regions to be explored. Recently, computational methods have been developed as low-cost and fast means of designing and redesigning antibodies. This study evaluated the design performance of AlphaFold2 and the binder hallucination, which can predict protein 3D structures with high accuracy even without experimental antibodies, by redesigning antibody sequences to improve the binding affinity of existing antigen-antibody complexes. Therefore, antibody sequences with higher affinity can be designed for antigen-antibody complexes not included in the training data of AlphaFold2, indicating that the proposed method may be effective as an antibody design method.
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