Computational design of serine hydrolases

Abstract

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies ( k cat / K m ) up to 2.2 × 10 5 M −1 s −1 and crystal structures that closely match the design models (Cα root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.

Shown for reading. Its publisher allows quoting it, not republishing it.

References 55

1. 1.
   De novo design of protein structure and function with RFdiffusion · 2023

   No notice
2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1038/s41586-022-04456-z

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1038/nature06879

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1126/science.1152692

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1126/science.1190239

6. 6.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1073/pnas.1118082108

7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1021/ja3037367

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1038/nchembio.1498

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library10.1002/pro.462

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1002/pro.604

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/s41557-021-00763-6

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.1010381107

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1126/science.adl2528

14. 14.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/nchem.2555

15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.251555398

16. 16.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/s41586-019-1262-8

17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1126/science.aau3744

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1021/bi201881p

19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1021/jacs.5b07812

20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

21. 21.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1038/332564a0

22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1002/prot.340070405

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1007/s00018-005-5160-x

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.83.11.3743

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

26. 26.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

27. 27.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1146/annurev.bi.46.070177.001555

28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1021/cr000033x

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1021/ja803213p

33. 33.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1101/2024.02.28.582624

34. 34.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1073/pnas.0601910103

35. 35.
    De novo design of luciferases using deep learning · 2023

    No notice
36. 36.
    Atomic context-conditioned protein sequence design using LigandMPNN · 2025

    No notice
37. 37.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1146/annurev.biochem.77.062906.171838

38. 38.
    Improving de novo protein binder design with deep learning · 2023

    No notice
39. 39.
    Highly accurate protein structure prediction with AlphaFold · 2021

    No notice
40. 40.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library10.1002/anie.202003635

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.