Evidence map›Paper›PMID 24599843›Full record

ArticleHuman mutation2014

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.

Jishnu Das, Hao Ran Lee, Adithya Sagar, Robert Fragoza, Jin Liang, Xiaomu Wei, Xiujuan Wang, Matthew Mort, Peter D Stenson, David N Cooper and 1 more

Abstract read
In one paragraph

Article in Human mutation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Comprehensive assessment of cancer missense mutation clustering in protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2015
    Article
  10. Article
  11. Article
  12. Article
  13. 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

11 authors.

Jishnu DasDepartment of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, 14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853.
Hao Ran Lee
Adithya Sagar
Robert Fragoza
Jin Liang
Xiaomu Wei
Xiujuan Wang
Matthew Mort
Peter D Stenson
David N Cooper
Haiyuan Yu

Funding

Whole Genome Sequencing to Discover Familial Myeloma Risk GenesR01CA167824 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, OFFIT, KENNETH · 2012 to 2016
$1.9M
Towards a comprehensive protein interactome network in Schizosaccharomyces pombeR01GM097358 · NIGMS · CORNELL UNIVERSITY · PI YU, HAIYUAN · 2012 to 2016
$1.7M
Using Protein Interactome Networks to Understand the Functional Role of Coding DNR01GM104424 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, YU, HAIYUAN · 2013 to 2016
$1.7M
NCI NIH HHS CA167824NCI NIH HHS R01 CA167824NIGMS NIH HHS GM104424NIGMS NIH HHS R01 GM097358NIGMS NIH HHS R01 GM104424
6 · The paper itself

Abstract

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

Indexed as

Genetic Diseases, InbornSystems BiologyComputational BiologyDatabases, ProteinHumansModels, TheoreticalProtein Interaction MapsStructure-Activity Relationshipnetwork systems biologypathogenic variationsprotein-protein interactionsstructural biology

Identifiers

PMID24599843
PMCPMC4876038

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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.