Evidence map›Paper›PMID 22814390›Full record

ArticleNature protocols2012

Template-based protein structure modeling using the RaptorX web server.

Morten Källberg, Haipeng Wang, Sheng Wang, Jian Peng, Zhiyong Wang, Hui Lu, Jinbo Xu

Abstract read
In one paragraph

Article in Nature protocols, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 808 papers.

0numbers the graph read from it
0cells of the map it votes in
808citing 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

808 citing papers in PubMed.

  1. Article
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  8. iScience · 2026
    Article
  9. Article
  10. Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
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  20. Article

748 more citing papers are in PubMed but not listed here.

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

7 authors.

Morten KällbergToyota Technological Institute at Chicago, Chicago, Illinois, USA.
Haipeng Wang
Sheng Wang
Jian Peng
Zhiyong Wang
Hui Lu
Jinbo Xu

Funding

New Computational Methods for Data-driven Protein Structure PredictionR01GM089753 · NIGMS · TOYOTA TECHNOLOGICAL INSTITUTE / CHICAGO · PI XU, JINBO · 2010 to 2023
$3.8M
NIGMS NIH HHS R01 GM089753NIGMS NIH HHS R01GM0897532
6 · The paper itself

Abstract

A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ~35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ~6,000 sequences submitted by ~1,600 users from around the world.

Indexed as

Models, MolecularSoftwareComputational BiologyProtein ConformationProteinsProtein Structure, SecondaryProtein Structure, TertiaryProteins

Identifiers

PMID22814390
PMCPMC4730388

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Registered trials

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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.