Evidence map›Paper›PMID 24619909›Full record

ArticleProteins2014

Assessment of protein side-chain conformation prediction methods in different residue environments.

Lenna X Peterson, Xuejiao Kang, Daisuke Kihara

Abstract read
In one paragraph

Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Characterising Side Chain Conformations in Proteins UsingChemistry (Weinheim an der Bergstrasse, Germany) · 2026
    Article
  2. Article
  3. Article
  4. Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol.Topics in current chemistry (Cham) · 2022
    Review
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  7. Article
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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.

Lenna X PetersonDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907.
Xuejiao Kang
Daisuke Kihara

Funding

Identification of protein-metabolite interactome.R01GM097528 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2011 to 2014
$1.1M
Role of Mitochondrial CYP2E1 in Chemical Exposure-Driven NeurodegenerationF32ES027306 · NIEHS · DUKE UNIVERSITY · PI HARTMAN, JESSICA HELENE · 2016 to 2018
$171k
NIEHS NIH HHS 27306C0004NIGMS NIH HHS R01 GM097528NIGMS NIH HHS R01GM097528
6 · The paper itself

Abstract

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic-detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.

Indexed as

Protein Structure, TertiaryAlgorithmsComputational BiologyDatabases, ProteinModels, MolecularProteinsProteinscomputational methodsprediction accuracyprotein structure predictionside-chain conformation predictionside-chain rotamerstructure modeling

Identifiers

PMID24619909
PMCPMC5007623

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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.