Author
Listed:
- Benjamin Fram
(Harvard Medical School
Dana-Farber Cancer Institute)
- Yang Su
(Harvard Medical School)
- Ian Truebridge
(Institute for Protein Innovation
Harvard Medical School
AI Proteins)
- Adam J. Riesselman
(Harvard Medical School
Harvard Medical School)
- John B. Ingraham
(Harvard Medical School)
- Alessandro Passera
(Dana-Farber Cancer Institute
Vienna BioCenter (VBC))
- Eve Napier
(Trinity College Dublin)
- Nicole N. Thadani
(Harvard Medical School
Apriori Bio)
- Samuel Lim
(Harvard Medical School)
- Kristen Roberts
(Selux Diagnostics Inc.)
- Gurleen Kaur
(Selux Diagnostics Inc.)
- Michael A. Stiffler
(Dana-Farber Cancer Institute
Dyno Therapeutics)
- Debora S. Marks
(Harvard Medical School
Broad Institute of MIT and Harvard)
- Christopher D. Bahl
(Institute for Protein Innovation
Harvard Medical School
AI Proteins)
- Amir R. Khan
(Trinity College Dublin
Boston Children’s Hospital)
- Chris Sander
(Harvard Medical School
Dana-Farber Cancer Institute
Broad Institute of MIT and Harvard)
- Nicholas P. Gauthier
(Harvard Medical School
Dana-Farber Cancer Institute
Broad Institute of MIT and Harvard)
Abstract
A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.
Suggested Citation
Benjamin Fram & Yang Su & Ian Truebridge & Adam J. Riesselman & John B. Ingraham & Alessandro Passera & Eve Napier & Nicole N. Thadani & Samuel Lim & Kristen Roberts & Gurleen Kaur & Michael A. Stiffl, 2024.
"Simultaneous enhancement of multiple functional properties using evolution-informed protein design,"
Nature Communications, Nature, vol. 15(1), pages 1-16, December.
Handle:
RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49119-x
DOI: 10.1038/s41467-024-49119-x
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49119-x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.