Evidence map›Paper›PMID 17367718›Full record

ReviewMethods (San Diego, Calif.)2007

Membrane protein prediction methods.

Marco Punta, Lucy R Forrest, Henry Bigelow, Andrew Kernytsky, Jinfeng Liu, Burkhard Rost

Abstract readReview
In one paragraph

Review in Methods (San Diego, Calif.), 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Trial
  2. Article
  3. Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Review
  5. Review
  6. Article
  7. Comparative Genomics ofFrontiers in microbiology · 2019
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Structural genomics plucks high-hanging membrane proteins.Current opinion in structural biology · 2012
    Review
  18. Article
  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

6 authors.

Marco PuntaDepartment of Biochemistry and Molecular Biophysics, Columbia University, 1130 St. Nicholas Ave., New York, NY 10032, USA.
Lucy R Forrest
Henry Bigelow
Andrew Kernytsky
Jinfeng Liu
Burkhard Rost

Funding

Sub 9 at AECMU54GM075026 · NIGMS · NEW YORK STRUCTURAL BIOLOGY CENTER · PI HENDRICKSON, WAYNE A. · 2005 to 2009
$16.8M
Predictions of structure/function by PredictProteinR01LM007329 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI PE'ER, ITSHACK G · 2003 to 2010
$2.6M
Predicting putative protein-protein interface segmentsR01GM064633 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI ROST, BURKHARD · 2003 to 2006
$1.1M
NIGMS NIH HHS R01-GM64633-01NIGMS NIH HHS U54-GM75026-01NLM NIH HHS R01 LM007329NLM NIH HHS R01-LM07329-01
6 · The paper itself

Abstract

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.

Indexed as

Models, ChemicalBiochemistryDatabases, FactualGenomicsMembrane ProteinsPredictive Value of TestsProtein ConformationStructure-Activity RelationshipMembrane Proteins

Identifiers

PMID17367718
PMCPMC1934899

What OpenQuestion holds

Textmetadata
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.