Evidence map›Paper›PMID 28201818›Full record

ArticleProtein engineering, design & selection : PEDS2017

Collective repacking reveals that the structures of protein cores are uniquely specified by steric repulsive interactions.

J C Gaines, A Virrueta, D A Buch, S J Fleishman, C S O'Hern, L Regan

Abstract read
In one paragraph

Article in Protein engineering, design & selection : PEDS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Using physical features of protein core packing to distinguish real proteins from decoys.Protein science : a publication of the Protein Society · 2020
    Article
  5. Article
  6. Article
  7. 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.

J C GainesProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
A VirruetaIntegrated Graduate Program in Physical and Engineering Biology (IGPPEB), Yale University, New Haven, CT 06520, USA.
D A BuchC. Eugene Bennett Department of Chemistry, 217 Clark Hall, West Virginia University, Morgantown, WV 26506, USA.
S J FleishmanDepartment of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
C S O'HernProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
L ReganProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.

Funding

MEDICAL INFORMATICS RESEARCH TRAINING AT YALET15LM007056 · NLM · YALE UNIVERSITY · PI Mark Bender Gerstein, LUCILA OHNO-MACHADO · 1987 to 2026
$22.2M
PREDOCTORAL PROGRAM IN BIOPHYSICST32GM008283 · NIGMS · YALE UNIVERSITY · PI XIONG, YONG · 1988 to 2022
$10.5M
NIGMS NIH HHS T32 GM008283NLM NIH HHS T15 LM007056
6 · The paper itself

Abstract

Protein core repacking is a standard test of protein modeling software. A recent study of six different modeling software packages showed that they are more successful at predicting side chain conformations of core compared to surface residues. All the modeling software tested have multicomponent energy functions, typically including contributions from solvation, electrostatics, hydrogen bonding and Lennard-Jones interactions in addition to statistical terms based on observed protein structures. We investigated to what extent a simplified energy function that includes only stereochemical constraints and repulsive hard-sphere interactions can correctly repack protein cores. For single residue and collective repacking, the hard-sphere model accurately recapitulates the observed side chain conformations for Ile, Leu, Phe, Thr, Trp, Tyr and Val. This result shows that there are no alternative, sterically allowed side chain conformations of core residues. Analysis of the same set of protein cores using the Rosetta software suite revealed that the hard-sphere model and Rosetta perform equally well on Ile, Leu, Phe, Thr and Val; the hard-sphere model performs better on Trp and Tyr and Rosetta performs better on Ser. We conclude that the high prediction accuracy in protein cores obtained by protein modeling software and our simplified hard-sphere approach reflects the high density of protein cores and dominance of steric repulsion.

Indexed as

Databases, ProteinModels, MolecularSoftwareProtein DomainsProteinsProteinsprotein core repackingprotein crystal structuresprotein designRosettaside chain conformations

Identifiers

PMID28201818
PMCPMC7263838

What OpenQuestion holds

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LicenceCC BY-NC
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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.