Inverse Protein Folding Using Deep Bayesian Optimization
This work addresses the challenge of reliably designing protein sequences that fold to specific backbones, with potential applications in de novo protein design, though it is incremental as it builds on existing generative models.
The paper tackles the problem of inverse protein folding by improving generated sequences through deep Bayesian optimization, resulting in sequences with greatly reduced structural error (measured by TM score and RMSD) and fewer computational resources.
Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of proteins. Contemporary approaches have cast this problem as a conditional generative modelling problem, where a large generative model over protein sequences is conditioned on the backbone. While these generative models very rapidly produce promising sequences, independent draws from generative models may fail to produce sequences that reliably fold to the correct backbone. Furthermore, it is challenging to adapt pure generative approaches to other settings, e.g., when constraints exist. In this paper, we cast the problem of improving generated inverse folds as an optimization problem that we solve using recent advances in "deep" or "latent space" Bayesian optimization. Our approach consistently produces protein sequences with greatly reduced structural error to the target backbone structure as measured by TM score and RMSD while using fewer computational resources. Additionally, we demonstrate other advantages of an optimization-based approach to the problem, such as the ability to handle constraints.