Inverse Folding

Inverse folding aims to predict the amino acid sequence of a protein given its 3D structure, a crucial challenge in protein engineering and drug discovery. Current research focuses on developing generative models, including graph neural networks and diffusion models, to address the inherent one-to-many mapping problem and generate diverse, biologically plausible sequences. These models are often enhanced by incorporating codon optimization for improved protein expression and leveraging techniques like Bayesian optimization or knowledge distillation from pre-trained structure prediction models to improve efficiency and accuracy. Advances in inverse folding hold significant promise for accelerating protein design, enabling the creation of novel proteins with tailored functionalities for various applications.