Disorder Atlas: Web-based software for the proteome-based interpretation of intrinsic disorder predictions. (December 2019)

• Record Type: Journal Article
• Authors: Vincent, Michael; Schnell, Santiago
• Is Part Of: Computational biology and chemistry. Volume 83(2019)
• Publication Date: 2019-12
• Subjects: Intrinsic disorder -- Protein disorder -- Standardization -- Disorder prediction -- Proteome -- Continuous disorder -- IUPred -- DisEMBL -- Consensus disorder; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2019.107090
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Disorder Atlas provides a user-friendly tool to compare protein disorder predictions. It facilitates protein disorder interpretation using proteome statistics. Algorithm-specific expected values and percentile cutoffs are explicitly provided. This service facilitates large-scale systematic exploratory searches for proteins. It also allows users to browse the prevalence of multiple disorder features at the proteome level. Abstract: Intrinsically disordered proteins lack a stable three-dimensional structure under physiological conditions. While this property has gained considerable interest within the past two decades, disorder poses substantial challenges to experimental characterization efforts. In effect, numerous computational tools have been developed to predict disorder from primary sequences, however, interpreting the output of these algorithms remains a challenge. To begin to bridge this gap, we present Disorder Atlas, web-based software that facilitates the interpretation of intrinsic disorder predictions using proteome-based descriptive statistics. This service is also equipped to facilitate large-scale systematic exploratory searches for proteins encompassing disorder features of interest, and further allows users to browse the prevalence of multiple disorder features at the proteome level. As a result, Disorder Atlas provides a user-friendly tool that places algorithm-generated disorder predictions in the context of the proteome, thereby providing an instrument to compare the results of a query protein against predictions made for an entire population. Disorder Atlas currently supports ten eukaryotic proteomes and is freely available for non-commercial users at http://www.disorderatlas.org .