Graph Neural Network Protein Mutation Effect Predictor
A GAT-based platform that predicts how single-point mutations affect protein stability and antibody-antigen binding affinity, using graph attention over protein structure.
Protein engineering and therapeutic antibody design require understanding how mutations affect protein behavior. Experimental measurement of mutational effects is slow and expensive — a computational model that predicts stability changes (ΔΔG) and binding affinity from structure alone can dramatically accelerate the design cycle.
MutaGraph represents proteins as graphs — nodes are residues, edges encode spatial proximity and biochemical interactions. A Graph Attention Network learns to propagate information across the structure, capturing long-range dependencies that sequence-only models miss.
Protein structure parsed into a residue-level graph. Edges defined by spatial distance cutoff and biochemical interaction type. Node features include amino acid physicochemical properties.
Multi-head GAT layers learn attention weights over neighboring residues, capturing which structural contacts matter most for stability and binding. Mutation encoded as node feature perturbation.
Graph-level readout predicts the thermodynamic stability change upon mutation, and optionally the change in antibody-antigen binding affinity for therapeutic design applications.
Screen thousands of candidate mutations in silico before committing to expensive wet lab synthesis and assays.
Predict how mutations in the CDR loops affect antigen binding, guiding affinity maturation campaigns.
Assess the functional impact of clinically observed missense variants on protein stability and interactions.