Evidence map›Paper›PMID 32295283›Full record

ReviewInternational journal of molecular sciences2020

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.

Bartłomiej Surpeta, Carlos Eduardo Sequeiros-Borja, Jan Brezovsky

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. The Impact ofInternational journal of molecular sciences · 2023
    Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Frontiers in bioengineering and biotechnology · 2021
    Article
  13. Review
  14. 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

3 authors.

Bartłomiej SurpetaLaboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland.ORCID 0000-0002-7436-8284
Carlos Eduardo Sequeiros-BorjaLaboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland.ORCID 0000-0002-7817-0240
Jan BrezovskyLaboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, 61-614 Poznan, Poland.ORCID 0000-0001-9677-5078

Funding

Narodowe Centrum Nauki 2017/25/B/NZ1/01307Narodowe Centrum Nauki 2017/26/E/NZ1/00548
6 · The paper itself

Abstract

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Indexed as

Computational BiologyModels, MolecularProtein EngineeringMutationProtein Interaction MappingProtein Interaction MapsProteinsProteinscomputational designde novo designensemble-based approachflexible backbonehotspot predictionligand transportmutational analysisprotein dynamicsprotein engineeringrational design

Identifiers

PMID32295283
PMCPMC7215530

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.