AntiBMPNN: Structure-Guided Graph Neural Networks for Precision Antibody Engineering.

ABSTRACT: Antibodies are crucial for medical applications, yet traditional methods for designing sequences are inefficient. This study introduces AntiBMPNN, an advanced deep-learning framework that leverages an antibody-specific 3D dataset, a fine-tuned message-passing neural network (MPNN), a frequency-based scoring function, and AlphaFold 3 to achieve highly accurate antibody sequence design. AntiBMPNN surpasses ProteinMPNN with a perplexity of 1.5 and over 80% sequence recovery. Its scoring function, combined with AlphaFold 3, effectively prioritizes sequences based on structural recovery, positional stability, and biochemical or complex properties. Experimental validation highlights a 75% success rate in single-point antibody design. AntiBMPNN consistently outperforms AbMPNN, AntiFold, and Prote

SUBMITTER: Sun ZY 

PROVIDER: S-EPMC12463024 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature