Deep Representation Learning for Protein Engineering

The patent replaces the mechanical/random approach of traditional mutagenesis and screening with a computational/intellectual system based on deep learning models. The system uses neural networks to predict protein function and stability, substituting random experimental variation with rational, model-guided design. This allows researchers to computationally predict which mutations will achieve desired functions before performing experiments, dramatically improving both efficiency and predictability.

The patent changes the fundamental parameters of protein engineering by introducing quantitative models that predict protein properties as continuous functions of sequence. Instead of relying on discrete, random mutations followed by screening, the system uses deep learning to evaluate and predict the effects of mutations on protein stability and function, enabling rational design based on predicted parameter changes rather than random sampling.

The patent performs preliminary computational analysis using deep learning models to predict protein function and stability before conducting experiments. The system evaluates numerous potential variants in silico, identifying promising candidates for experimental validation. This preliminary computational screening reduces the number of variants that require extensive experimental characterization, saving time and resources while maintaining the ability to explore diverse protein sequence space.

The patent creates computational copies and models of protein sequences and their predicted functions. Instead of physically creating and testing every possible variant, the system generates virtual representations of protein variants and their expected properties through deep learning models. These computational copies allow researchers to screen and evaluate numerous variants in silico before selecting a small subset for physical experimentation.

The patent develops a universal deep learning framework that can predict multiple protein properties (stability, function, binding affinity) from sequence data alone. The same neural network architecture and training approach can be applied across different protein families and functions, providing a generalizable solution rather than requiring separate models for each specific property or protein type. This multi-functional framework consolidates various prediction tasks into a unified system.

The patent segments the complex task of protein design into distinct predictive components handled by specialized neural network models. Different models or model components are trained to predict specific properties (e.g., stability, enzymatic activity, binding affinity) independently. This segmentation allows each model to be optimized for its specific function while maintaining a modular architecture that can be combined and applied generally across different protein engineering tasks.