Evidence map›Paper›PMID 29329283›Full record

ArticlePLoS computational biology2018

Modeling the assembly order of multimeric heteroprotein complexes.

Lenna X Peterson, Yoichiro Togawa, Juan Esquivel-Rodriguez, Genki Terashi, Charles Christoffer, Amitava Roy, Woong-Hee Shin, Daisuke Kihara

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

17 citing papers in PubMed, 37 citations in OpenAlex.

  1. Review
  2. Assessment of Protein-Protein Docking Models Using Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2024
    Review
  3. Article
  4. Simulation toolkits at the molecular scale for trans-scale thermal signaling.Computational and structural biotechnology journal · 2023
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. LZerD Protein-Protein Docking Webserver Enhanced WithFrontiers in molecular biosciences · 2021
    Review
  12. Article
  13. Protein Docking Model Evaluation by Graph Neural Networks.Frontiers in molecular biosciences · 2021
    Article
  14. Review
  15. Article
  16. Article
  17. 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

8 authors at 2 institutions in 1 country.

Lenna X PetersonDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0001-7734-1784
Yoichiro TogawaDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0001-6562-5341
Juan Esquivel-RodriguezDepartment of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.
Genki TerashiDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.
Charles ChristofferDepartment of Computer Science, Purdue University, West Lafayette, Indiana, United States of America.
Amitava RoyDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0002-9100-4108
Woong-Hee ShinDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.
Daisuke KiharaDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana, United States of America.ORCID 0000-0003-4091-6614
Purdue University West Lafayette · USNational Institutes of Health · US

Funding

Structural Modeling of Multifarious Protein ComplexesR01GM123055 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2017 to 2020
$1.2M
Identification of protein-metabolite interactome.R01GM097528 · NIGMS · PURDUE UNIVERSITY · PI KIHARA, DAISUKE · 2011 to 2014
$1.1M
NIGMS NIH HHS R01 GM097528NIGMS NIH HHS R01 GM123055NIH HHS R01GM097528NIH HHS R01GM123055
6 · The paper itself

Abstract

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

Indexed as

AlgorithmsCholera ToxinComputational BiologyDatabases, ProteinHelicobacter pyloriHumansModels, StatisticalMolecular Docking SimulationProtein BindingProtein DomainsProtein Interaction MappingProteinsSoftwareThermodynamicsCholera ToxinProteins

Identifiers

PMID29329283
PMCPMC5785014
OpenAlexW2783728269

What OpenQuestion holds

Textmetadata
LicenceCC0
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.