Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.
Nat Commun
; 15(1): 6170, 2024 Jul 23.
Article
in En
| MEDLINE
| ID: mdl-39043654
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. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Thermodynamics
/
Proteins
/
Protein Stability
/
Mutation
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Type:
Article
Affiliation country:
United States