Evidence map›Paper›PMID 26072477›Full record

ArticleBioinformatics (Oxford, England)2015

cNMA: a framework of encounter complex-based normal mode analysis to model conformational changes in protein interactions.

Tomasz Oliwa, Yang Shen

Open access · bronzeAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

19 citing papers in PubMed, 42 citations in OpenAlex.

  1. Article
  2. Article
  3. Approaches to Backbone Flexibility in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Assessment of Protein-Protein Docking Models Using Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2024
    Review
  5. Article
  6. Advances to tackle backbone flexibility in protein docking.Current opinion in structural biology · 2021
    Review
  7. Article
  8. Protein Docking Model Evaluation by Graph Neural Networks.Frontiers in molecular biosciences · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Pushing the Backbone in Protein-Protein Docking.Structure (London, England : 1993) · 2016
    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 at 1 institution in 1 country.

Tomasz OliwaToyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Yang ShenToyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA Toyota Technological Institute at Chicago, Chicago, IL 60637, USA and Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Toyota Technological Institute at Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationIt remains both a fundamental and practical challenge to understand and anticipate motions and conformational changes of proteins during their associations. Conventional normal mode analysis (NMA) based on anisotropic network model (ANM) addresses the challenge by generating normal modes reflecting intrinsic flexibility of proteins, which follows a conformational selection model for protein-protein interactions. But earlier studies have also found cases where conformational selection alone could not adequately explain conformational changes and other models have been proposed. Moreover, there is a pressing demand of constructing a much reduced but still relevant subset of protein conformational space to improve computational efficiency and accuracy in protein docking, especially for the difficult cases with significant conformational changes. METHOD AND

resultsWith both conformational selection and induced fit models considered, we extend ANM to include concurrent but differentiated intra- and inter-molecular interactions and develop an encounter complex-based NMA (cNMA) framework. Theoretical analysis and empirical results over a large data set of significant conformational changes indicate that cNMA is capable of generating conformational vectors considerably better at approximating conformational changes with contributions from both intrinsic flexibility and inter-molecular interactions than conventional NMA only considering intrinsic flexibility does. The empirical results also indicate that a straightforward application of conventional NMA to an encounter complex often does not improve upon NMA for an individual protein under study and intra- and inter-molecular interactions need to be differentiated properly. Moreover, in addition to induced motions of a protein under study, the induced motions of its binding partner and the coupling between the two sets of protein motions present in a near-native encounter complex lead to the improved performance. A study to isolate and assess the sole contribution of intermolecular interactions toward improvements against conventional NMA further validates the additional benefit from induced-fit effects. Taken together, these results provide new insights into molecular mechanisms underlying protein interactions and new tools for dimensionality reduction for flexible protein docking. AVAILABILITY AND IMPLEMENTATION: Source codes are available upon request.

Indexed as

Molecular Docking SimulationMotionMultiprotein ComplexesProtein ConformationProtein Interaction MappingMultiprotein Complexes

Identifiers

PMID26072477
PMCPMC4765865
OpenAlexW2162258290

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.