Nature Communications (Jul 2024)

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

  • Daniel J. Diaz,
  • Chengyue Gong,
  • Jeffrey Ouyang-Zhang,
  • James M. Loy,
  • Jordan Wells,
  • David Yang,
  • Andrew D. Ellington,
  • Alexandros G. Dimakis,
  • Adam R. Klivans

DOI
https://doi.org/10.1038/s41467-024-49780-2
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 15

Abstract

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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.