Engineering proteins with multiple beneficial mutations is rarely a simple additive process.
As the number of mutations increases, interactions become increasingly complex, making it difficult to predict how combinations of three, four, or more substitutions will affect protein performance.
Traditional experimental screening becomes impractical as the number of possible variants grows exponentially.
Neoncorte Bio applies AI-driven higher-order mutation prediction to identify promising multi-mutation protein variants and support efficient protein engineering programs.