Evidence map›Paper›PMID 24268901›Full record

ReviewTrends in biotechnology2014

Protein folding and de novo protein design for biotechnological applications.

George A Khoury, James Smadbeck, Chris A Kieslich, Christodoulos A Floudas

Abstract readReview
In one paragraph

Review in Trends in biotechnology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 citing papers in PubMed.

  1. Article
  2. Article
  3. Living/controlled supramolecular protein polymerization.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Review
  5. Assembly and Functionality of 2D Protein Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  6. Article
  7. Article
  8. Computational Protein Design - Where it goes?Current medicinal chemistry · 2024
    Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Review
  14. Stabilization of Enzymes by Using Thermophiles.Methods in molecular biology (Clifton, N.J.) · 2023
    Review
  15. Review
  16. Review
  17. Article
  18. Protein design via deep learning.Briefings in bioinformatics · 2022
    Review
  19. Article
  20. 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

4 authors.

George A KhouryDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
James SmadbeckDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Chris A KieslichDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
Christodoulos A FloudasDepartment of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA. Electronic address: floudas@titan.princeton.edu.

Funding

STRUCTURE PREDICTION OF PEPTIDES VIA GLOBAL OPTIMIZATIONR01GM052032 · NIGMS · PRINCETON UNIVERSITY · PI FLOUDAS, CHRISTODOULOS ACHILLEUS · 1996 to 2011
$1.9M
NIGMS NIH HHS R01 GM052032NIGMS NIH HHS R01GM052032
6 · The paper itself

Abstract

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications.

Indexed as

Protein FoldingBiotechnologyComputational BiologyMolecular Dynamics SimulationProtein EngineeringRecombinant ProteinsRecombinant Proteinsbiotechnologycomputational biologyde novo protein designprotein structure prediction

Identifiers

PMID24268901
PMCPMC3922204

What OpenQuestion holds

Textmetadata
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Read underepoch 390

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.