Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.

ABSTRACT: Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation

SUBMITTER: Diaz DJ 

PROVIDER: S-EPMC11266546 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature