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 and Adam R. Klivans
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Daniel J. Diaz: Department of Computer Science
Chengyue Gong: Department of Computer Science
Jeffrey Ouyang-Zhang: Department of Computer Science
James M. Loy: LLC
Jordan Wells: McKetta Department of Chemical Engineering
David Yang: Department of Molecular Biosciences
Andrew D. Ellington: Department of Molecular Biosciences
Alexandros G. Dimakis: Chandra Family Department of Electrical and Computer Engineering
Adam R. Klivans: Department of Computer Science

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49780-2

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