Evidence map›Paper›PMID 22811380›Full record

ArticleProtein science : a publication of the Protein Society2012

Comparison of designed and randomly generated catalysts for simple chemical reactions.

Yakov Kipnis, David Baker

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Perspective: Path Sampling Methods Applied to Enzymatic Catalysis.Journal of chemical theory and computation · 2022
    Review
  2. Article
  3. Article
  4. Role of conformational dynamics in the evolution of novel enzyme function.Chemical communications (Cambridge, England) · 2018
    Review
  5. Review
  6. Article
  7. Article
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

2 authors.

Yakov KipnisDepartment of Biochemistry, University of Washington, Seattle, Washington 98195, USA.
David Baker

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

There has been recent success in designing enzymes for simple chemical reactions using a two-step protocol. In the first step, a geometric matching algorithm is used to identify naturally occurring protein scaffolds at which predefined idealized active sites can be realized. In the second step, the residues surrounding the transition state model are optimized to increase transition state binding affinity and to bolster the primary catalytic side chains. To improve the design methodology, we investigated how the set of solutions identified by the design calculations relate to the overall set of solutions for two different chemical reactions. Using a TIM barrel scaffold in which catalytically active Kemp eliminase and retroaldolase designs were obtained previously, we carried out activity screens of random libraries made to be compositionally similar to active designs. A small number of active catalysts were found in screens of 10³ variants for each of the two reactions, which differ from the computational designs in that they reuse charged residues already present in the native scaffold. The results suggest that computational design considerably increases the frequency of catalyst generation for active sites involving newly introduced catalytic residues, highlighting the importance of interaction cooperativity in enzyme active sites.

Indexed as

Aldehyde-LyasesAlgorithmsAmino Acid SequenceBiocatalysisCatalytic DomainComputational BiologyLyasesModels, MolecularMolecular Sequence DataMutationProtein ConformationProtein EngineeringSolubilityAldehyde-LyasesLyases

Identifiers

PMID22811380
PMCPMC3631367

What OpenQuestion holds

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Registered trials

None linked

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.