Literature DB >> 24096645

Exploring mechanisms of human disease through structurally resolved protein interactome networks.

Robert Fragoza1,2, Hao Ran Lee3,1, Nicolas A Cordero1, Jishnu Das3,1, Yu Guo1,2, Michael J Meyer3,1,4, Tommy V Vo1,2, Xiujuan Wang3,1, Haiyuan Yu3,1.   

Abstract

The study of the molecular basis of human disease has gained increasing attention over the past decade. With significant improvements in sequencing efficiency and throughput, a wealth of genotypic data has become available. However the translation of this information into concrete advances in diagnostic and clinical setups has proved far more challenging. Two major reasons for this are the lack of functional annotation for genomic variants and the complex nature of genotype-to-phenotype relationships. One fundamental approach to bypass these issues is to examine the effects of genetic variation at the level of proteins as they are directly involved in carrying out biological functions. Within the cell, proteins function by interacting with other proteins as a part of an underlying interactome network. This network can be determined using interactome mapping - a combination of high-throughput experimental toolkits and curation from small-scale studies. Integrating structural information from co-crystals with the network allows generation of a structurally resolved network. Within the context of this network, the structural principles of disease mutations can be examined and used to generate reliable mechanistic hypotheses regarding disease pathogenesis.

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Year:  2014        PMID: 24096645      PMCID: PMC4061614          DOI: 10.1039/c3mb70225a

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  58 in total

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  18 in total

Review 1.  Functional variomics and network perturbation: connecting genotype to phenotype in cancer.

Authors:  Song Yi; Shengda Lin; Yongsheng Li; Wei Zhao; Gordon B Mills; Nidhi Sahni
Journal:  Nat Rev Genet       Date:  2017-03-27       Impact factor: 53.242

Review 2.  Template-based prediction of protein function.

Authors:  Donald Petrey; T Scott Chen; Lei Deng; Jose Ignacio Garzon; Howook Hwang; Gorka Lasso; Hunjoong Lee; Antonina Silkov; Barry Honig
Journal:  Curr Opin Struct Biol       Date:  2015-02-10       Impact factor: 6.809

Review 3.  How do oncoprotein mutations rewire protein-protein interaction networks?

Authors:  Emily H Bowler; Zhenghe Wang; Rob M Ewing
Journal:  Expert Rev Proteomics       Date:  2015-09-01       Impact factor: 3.940

4.  TBC1D24 mutation causes autosomal-dominant nonsyndromic hearing loss.

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Journal:  Hum Mutat       Date:  2014-05-06       Impact factor: 4.878

5.  mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome.

Authors:  Michael J Meyer; Ryan Lapcevic; Alfonso E Romero; Mark Yoon; Jishnu Das; Juan Felipe Beltrán; Matthew Mort; Peter D Stenson; David N Cooper; Alberto Paccanaro; Haiyuan Yu
Journal:  Hum Mutat       Date:  2016-02-18       Impact factor: 4.878

Review 6.  Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease.

Authors:  Stephen D Ginsberg; Thomas A Neubert; Sahil Sharma; Chander S Digwal; Pengrong Yan; Calin Timbus; Tai Wang; Gabriela Chiosis
Journal:  FEBS J       Date:  2021-06-12       Impact factor: 5.622

7.  ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers.

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8.  Quo vadis computational analysis of PPI data or why the future isn't here yet.

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Journal:  Front Genet       Date:  2015-09-15       Impact factor: 4.599

9.  Structure-PPi: a module for the annotation of cancer-related single-nucleotide variants at protein-protein interfaces.

Authors:  Miguel Vázquez; Alfonso Valencia; Tirso Pons
Journal:  Bioinformatics       Date:  2015-03-11       Impact factor: 6.937

Review 10.  Getting to the edge: protein dynamical networks as a new frontier in plant-microbe interactions.

Authors:  Cassandra C Garbutt; Purushotham V Bangalore; Pegah Kannar; M S Mukhtar
Journal:  Front Plant Sci       Date:  2014-06-30       Impact factor: 5.753

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