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Updated: Aug 16, 2026
07:08
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Aligning protein-generative models to experimental fitness with ProteinDPO
Talal Widatalla1,2, Ashir A Borah2,3, Samuel H King1,2
1Stanford University, Stanford, CA, USA.
Nature Methods
|August 14, 2026
Summary
Biological generative models were improved using direct preference optimization (DPO) to align protein language models for stability prediction. This approach bridges the
Area of Science:
- Computational biology
- Protein engineering
- Machine learning
Background:
- Biological generative models offer broad function prediction but lack specialization.
- An 'alignment gap' limits unsupervised models, as learned rules don't match desired functions.
Purpose of the Study:
- To align structure-conditioned protein language models for enhanced stability prediction.
- To integrate task-specific biophysical information without losing general knowledge.
Main Methods:
- Utilized direct preference optimization (DPO) to fine-tune a protein language model.
- Trained the model to preferentially generate stable protein sequences.
Main Results:
- The aligned model, ProteinDPO, achieved stability prediction competitive with specialized models.
- ProteinDPO demonstrated generalization for stabilizing protein complexes and predicting binding affinity.
- Applied to hemagglutinin trimers (influenza vaccine component), ~80% of designs showed improved or similar stability, with up to 32°C increases.
Conclusions:
- Direct preference optimization effectively aligns biological foundation models with specific biophysical tasks.
- This framework enhances generative models by incorporating task-specific information, improving protein design and function prediction.
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