Simultaneous enhancement of multiple functional properties using evolution-informed protein design.


Journal

bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187

Informations de publication

Date de publication:
09 May 2023
Historique:
pubmed: 22 5 2023
medline: 22 5 2023
entrez: 22 5 2023
Statut: epublish

Résumé

Designing optimized proteins is important for a range of practical applications. Protein design is a rapidly developing field that would benefit from approaches that enable many changes in the amino acid primary sequence, rather than a small number of mutations, while maintaining structure and enhancing function. Homologous protein sequences contain extensive information about various protein properties and activities that have emerged over billions of years of evolution. Evolutionary models of sequence co-variation, derived from a set of homologous sequences, have proven effective in a range of applications including structure determination and mutation effect prediction. In this work we apply one of these models (EVcouplings) to computationally design highly divergent variants of the model protein TEM-1 β-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays. Nearly all designed variants were functional, including one with 84 mutations from the nearest natural homolog. Surprisingly, all functional designs had large increases in thermostability and most had a broadening of available substrates. These property enhancements occurred while maintaining a nearly identical structure to the wild type enzyme. Collectively, this work demonstrates that evolutionary models of sequence co-variation (1) are able to capture complex epistatic interactions that successfully guide large sequence departures from natural contexts, and (2) can be applied to generate functional diversity useful for many applications in protein design.

Identifiants

pubmed: 37214973
doi: 10.1101/2023.05.09.539914
pmc: PMC10197589
pii:
doi:

Types de publication

Preprint

Langues

eng

Auteurs

Benjamin Fram (B)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Ian Truebridge (I)

Institute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA.
Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, MA, USA.
current address: AI Proteins; Boston, MA, USA.

Yang Su (Y)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Adam J Riesselman (AJ)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Program in Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

John B Ingraham (JB)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Alessandro Passera (A)

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
current address: Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030 Vienna, Austria.

Eve Napier (E)

School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland.

Nicole N Thadani (NN)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Samuel Lim (S)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Kristen Roberts (K)

Selux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA.

Gurleen Kaur (G)

Selux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA.

Michael Stiffler (M)

Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.

Debora S Marks (DS)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Christopher D Bahl (CD)

Institute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA.
Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School; Boston, MA, USA.
current address: AI Proteins; Boston, MA, USA.

Amir R Khan (AR)

School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland.
Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA.

Chris Sander (C)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Nicholas P Gauthier (NP)

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.

Classifications MeSH