Evidence map›Paper›PMID 40013243›Full record

ArticleACS catalysis2025

Automated Engineering Protein Dynamics via Loop Grafting: Improving

Joan Planas-Iglesias, Marika Majerova, Daniel Pluskal, Michal Vasina, Jiri Damborsky, Zbynek Prokop, Martin Marek, David Bednar

Abstract read
In one paragraph

Article in ACS catalysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Joan Planas-IglesiasLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.ORCID https://orcid.org/0000-0002-6279-2483
Marika MajerovaLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.
Daniel PluskalLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.
Michal VasinaLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.
Jiri DamborskyLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.ORCID https://orcid.org/0000-0002-7848-8216
Zbynek ProkopLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.ORCID https://orcid.org/0000-0001-9358-4081
Martin MarekLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.ORCID https://orcid.org/0000-0001-7220-5644
David BednarLoschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno 602 00, Czech Republic.ORCID https://orcid.org/0000-0002-6803-0340

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Engineering protein dynamics is a challenging and unsolved problem in protein design. Loop transplantation or loop grafting has been previously employed to transfer dynamic properties between proteins. We recently released a LoopGrafter Web server to execute the loop grafting task, employing eight computational tools and one database. The LoopGrafter method relies on the prediction of the local dynamic behavior of the elements to be transplanted and has successfully reconstructed previously engineered sequences. However, it was unclear whether catalytically competitive previously uncharacterized designs could be obtained by this method. Here, we address this question, showing how LoopGrafter generates viable loop-grafted chimeras of luciferases, how these chimeras encompass the activity of interest and unique kinetic properties, and how all this process is done fully automatically and agnostic of any previous knowledge. All constructed designs proved to be catalytically active, and the most active one improved the activity of the template enzyme by 4 orders of magnitude. The computational details and parameter optimization of the sequence pairing step of the LoopGrafter workflow are revealed. The optimized and experimentally validated loop grafting workflow available as a fully automated Web server represents a powerful approach for engineering catalytically efficient enzymes by modification of protein dynamics.

Identifiers

PMID40013243
PMCPMC11851775

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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.