Article

Three-dimensional reconstruction of protein networks provides insight into human genetic disease.

Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
Nature Biotechnology (Impact Factor: 39.08). 01/2012; 30(2):159-64. DOI: 10.1038/nbt.2106
Source: PubMed

ABSTRACT To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.

1 Follower
 · 
100 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background With the explosion of genomic data over the last decade, there has been a tremendous amount of effort to understand the molecular basis of cancer using informatics approaches. However, this has proven to be extremely difficult primarily because of the varied etiology and vast genetic heterogeneity of different cancers and even within the same cancer. One particularly challenging problem is to predict prognostic outcome of the disease for different patients. Results Here, we present ENCAPP, an elastic-net-based approach that combines the reference human protein interactome network with gene expression data to accurately predict prognosis for different human cancers. Our method identifies functional modules that are differentially expressed between patients with good and bad prognosis and uses these to fit a regression model that can be used to predict prognosis for breast, colon, rectal, and ovarian cancers. Using this model, ENCAPP can also identify prognostic biomarkers with a high degree of confidence, which can be used to generate downstream mechanistic and therapeutic insights. Conclusion ENCAPP is a robust method that can accurately predict prognostic outcome and identify biomarkers for different human cancers. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1465-9) contains supplementary material, which is available to authorized users.
    BMC Genomics 04/2015; 16(1). DOI:10.1186/s12864-015-1465-9 · 4.04 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This document is used in presential courses and regularly updated. Related files can be found in https://dl.dropboxusercontent.com/u/43260764/CU-Network_analysis_files.zip
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D2OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D2OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD2OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D2OCK predictions from the convenience of their web-browsers.
    PLoS ONE 03/2015; 10(3):e0118107. DOI:10.1371/journal.pone.0118107 · 3.53 Impact Factor

Preview

Download
1 Download
Available from