Literature DB >> 28719238

Mining databases for protein aggregation: a review.

Paraskevi L Tsiolaki1, Katerina C Nastou1, Stavros J Hamodrakas1, Vassiliki A Iconomidou1.   

Abstract

Protein aggregation is an active area of research in recent decades, since it is the most common and troubling indication of protein instability. Understanding the mechanisms governing protein aggregation and amyloidogenesis is a key component to the aetiology and pathogenesis of many devastating disorders, including Alzheimer's disease or type 2 diabetes. Protein aggregation data are currently found "scattered" in an increasing number of repositories, since advances in computational biology greatly influence this field of research. This review exploits the various resources of aggregation data and attempts to distinguish and analyze the biological knowledge they contain, by introducing protein-based, fragment-based and disease-based repositories, related to aggregation. In order to gain a broad overview of the available repositories, a novel comprehensive network maps and visualizes the current association between aggregation databases and other important databases and/or tools and discusses the beneficial role of community annotation. The need for unification of aggregation databases in a common platform is also addressed.

Entities:  

Keywords:  Protein aggregation; amyloid; amyloidogenesis; amyloidosis; database

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Year:  2017        PMID: 28719238     DOI: 10.1080/13506129.2017.1353966

Source DB:  PubMed          Journal:  Amyloid        ISSN: 1350-6129            Impact factor:   7.141


  2 in total

1.  Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics.

Authors:  Saba Shahzadi; Muhammad Yasir; Bisma Aftab; Sumbal Babar; Mubashir Hassan
Journal:  Methods Mol Biol       Date:  2022

Review 2.  Relevance of Electrostatic Charges in Compactness, Aggregation, and Phase Separation of Intrinsically Disordered Proteins.

Authors:  Greta Bianchi; Sonia Longhi; Rita Grandori; Stefania Brocca
Journal:  Int J Mol Sci       Date:  2020-08-27       Impact factor: 5.923

  2 in total

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