Evidence map›Paper›PMID 28845596›Full record

ArticleProteins2018

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

Lenna X Peterson, Woong-Hee Shin, Hyungrae Kim, Daisuke Kihara

Abstract read
In one paragraph

Article in Proteins, 2018. 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

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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

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Protein Docking Model Evaluation by Graph Neural Networks.Frontiers in molecular biosciences · 2021
    Article
  8. LZerD Protein-Protein Docking Webserver Enhanced WithFrontiers in molecular biosciences · 2021
    Review
  9. Review
  10. Article
  11. Article
  12. Article
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  14. Article
  15. Article
  16. Review
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

4 authors.

Lenna X PetersonDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana.
Woong-Hee ShinDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana.
Hyungrae KimDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana.
Daisuke KiharaDepartment of Biological Sciences, Purdue University, West Lafayette, Indiana.ORCID 0000-0003-4091-6614

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 GM123055
6 · The paper itself

Abstract

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase.

Indexed as

Models, MolecularMolecular Docking SimulationProtein ConformationBinding SitesComputational BiologyDatabases, ProteinHumansProtein BindingProtein Interaction MappingProteinsSequence Analysis, ProteinStructural Homology, ProteinProteinsCAPRIcomputational methodsLZerDprediction accuracyprotein docking predictionprotein-protein dockingprotein-protein interactionprotein structure predictionstructure modelingtemplate-based modeling

Identifiers

PMID28845596
PMCPMC5820220

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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.