Evidence map›Paper›PMID 35000062›Full record

ArticleJournal of biological physics2022

Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model.

Xun Chen, Wei Lu, Min-Yeh Tsai, Shikai Jin, Peter G Wolynes

Open access · hybridAbstract read
In one paragraph

Article in Journal of biological physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 35% of its field
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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 2 countries.

Xun Chen *Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.
Wei Lu *Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.
Min-Yeh TsaiDepartment of Chemistry, Tamkang University, 25137, New Taipei City, Taiwan (R.O.C.).
Shikai JinCenter for Theoretical Biological Physics, Rice University, Houston, TX, USA.
Peter G WolynesCenter for Theoretical Biological Physics, Rice University, Houston, TX, USA. pwolynes@rice.edu.ORCID 0000-0001-7975-9287
Center for Theoretical Biological Physics · USTamkang University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein-ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

Indexed as

HemeproteinsHemeHydrogen BondingProtein ConformationProtein FoldingStatic ElectricityThermodynamicsHemeHemeproteinsForcefieldHemeNucleation mechanismPredictionProtein folding

Identifiers

PMID35000062
PMCPMC8866609
OpenAlexW4205946727

What OpenQuestion holds

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LicenceCC BY
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.