Evidence map›Paper›PMID 37214973›Full record

ArticlebioRxiv : the preprint server for biology2023

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

Benjamin Fram, Ian Truebridge, Yang Su, Adam J Riesselman, John B Ingraham, Alessandro Passera, Eve Napier, Nicole N Thadani, Samuel Lim, Kristen Roberts and 7 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Benjamin FramDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Ian TruebridgeInstitute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA.
Yang SuDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Adam J RiesselmanDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
John B IngrahamDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Alessandro PasseraDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
Eve NapierSchool of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland.
Nicole N ThadaniDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Samuel LimDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Kristen RobertsSelux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA.
Gurleen KaurSelux Diagnostics, Inc., 56 Roland Street, Charlestown, MA, USA.
Michael StifflerDepartment of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
Debora S MarksDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Christopher D BahlInstitute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA.
Amir R KhanSchool of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland.
Chris SanderDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.
Nicholas P GauthierDepartment of Systems Biology, Harvard Medical School, Boston, MA, USA.

Funding

X-ray Scattering Technology CoreP30GM133893 · NIGMS · BROOKHAVEN SCIENCE ASSOC-BROOKHAVEN LAB · PI Vivian Stojanoff · 2019 to 2026
$38.6M
User Training and OutreachP30GM124165 · NIGMS · CORNELL UNIVERSITY · PI STEVEN E EALICK · 2018 to 2026
$34.2M
NIGMS NIH HHS P30 GM124165NIGMS NIH HHS P30 GM133893
6 · The paper itself

Abstract

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.

Identifiers

PMID37214973
PMCPMC10197589

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.