Protein Sequence Design with Dynamic AlphaFold Structure Prediction

The patent applies dynamics by training AlphaFold to predict ensembles of structures representing different conformational states rather than a single static structure. This enables the model to capture proteins in thermodynamic equilibrium and during conformational changes, making the prediction system adaptive to dynamic biological realities while maintaining high structural accuracy for each predicted state.

The patent implements feedback by using the trained AlphaFold model to evaluate and refine designed protein sequences iteratively. The model predicts structures for designed sequences, and these predictions feed back into the design process to guide optimization toward sequences that fold into accurate, native-like high-resolution structures. This closed-loop approach ensures both novelty and reliability.

The patent applies copying by training AlphaFold on experimental protein structure data from databases like PDB. The model learns to replicate the accuracy of experimental structure determination by copying known structure-sequence relationships. Once trained, it can rapidly predict structures for novel sequences without performing actual experiments, achieving both experimental-level accuracy and computational speed.

The patent applies dynamics by modifying AlphaFold to predict multiple conformations and ensembles rather than a single static structure. This enables the model to represent proteins undergoing conformational changes and intrinsically disordered proteins that sample multiple states, while still providing well-defined structural information for each predicted conformational state through the dynamics module.