Predicting a Protein’s Stability under a Million Mutations: Conclusion, Acknowledgement & Referencesby@mutation
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Predicting a Protein’s Stability under a Million Mutations: Conclusion, Acknowledgement & References

by The Mutation PublicationMarch 12th, 2024
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Protein engineering is the discipline of mutating a natural protein sequence to improve properties for industrial and pharmaceutical applications.
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under a Million Mutations: Conclusion, Acknowledgement & References
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This paper is available on arxiv under CC 4.0 license.


(1) Jeffrey Ouyang-Zhang, UT Austin

(2) Daniel J. Diaz, UT Austin

(3) Adam R. Klivans, UT Austin

(4) Philipp Krähenbühl, UT Austin

7 Conclusion

We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. Our method scales to millions of mutations with minimal computational overhead and runs in a fraction of the time it would take prior works.

8 Acknowledgements

This work is supported by the NSF AI Institute for Foundations of Machine Learning (IFML).


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