Evidence map›Paper›PMID 29155578›Full record

ArticleJournal of chemical information and modeling2017

Determination of Hydrophobic Lengths of Membrane Proteins with the HDGB Implicit Membrane Model.

Bercem Dutagaci, Michael Feig

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 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. Review
  2. Article
  3. Spatial arrangement of proteins in planar and curved membranes by PPM 3.0.Protein science : a publication of the Protein Society · 2022
    Article
  4. Diverse Scientific Benchmarks for Implicit Membrane Energy Functions.Journal of chemical theory and computation · 2021
    Article
  5. Article
  6. Review
  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

2 authors.

Bercem DutagaciDepartment of Biochemistry and Molecular Biology, Michigan State University , 603 Wilson Road, Room BCH, 218, East Lansing, Michigan 48824, United States.
Michael FeigDepartment of Biochemistry and Molecular Biology, Michigan State University , 603 Wilson Road, Room BCH, 218, East Lansing, Michigan 48824, United States.ORCID 0000-0001-9380-6422

Funding

Protein structure refinement through effective sampling and scoringR01GM084953 · NIGMS · MICHIGAN STATE UNIVERSITY · PI FEIG, MICHAEL · 2008 to 2017
$2.5M
NIGMS NIH HHS R01 GM084953
6 · The paper itself

Abstract

A protocol for predicting the hydrophobic length of membrane proteins using the heterogeneous dielectric generalized Born (HDGB) implicit membrane model is presented. The method involves optimal positioning in the membrane and identification of lipid-facing and inward-facing residues, followed by energy optimization of the implicit membrane model to obtain the hydrophobic length from the optimal membrane width. The latest HDGB version 3 (HDGBv3) and HDGB van der Waals (HDGBvdW) models were applied to a test set containing 15 proteins (seven β-barrel and eight α-helical proteins), for which matching membrane widths are available from experiment, and an additional set contains ten α-helical and ten β-barrel proteins without any experimental data. The results with the HDGB model compare favorably with predictions from methods used in the Orientations of Proteins in Membranes (OPM) and Protein Data Bank of Transmembrane Proteins (PDB-TM) databases.

Indexed as

AnimalsBacteriaBacterial ProteinsCell MembraneDatabases, ProteinHumansHydrophobic and Hydrophilic InteractionsLipid BilayersMembrane ProteinsModels, MolecularProtein Structure, SecondarySoftwareThermodynamicsBacterial ProteinsLipid BilayersMembrane Proteins

Identifiers

PMID29155578
PMCPMC5743600

What OpenQuestion holds

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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.