The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities

Abstract

Motivation: The recent shift towards high-throughput screening is posing new challenges for the interpretation of experimental results. Here we propose the cleverSuite approach for large-scale characterization of protein groups.Description: The central part of the cleverSuite is the cleverMachine (CM), an algorithm that performs statistics on protein sequences by comparing their physico-chemical propensities. The second element is called cleverClassifier and builds on top of the models generated by the CM to allow classification of new datasets.Results: We applied the cleverSuite to predict secondary structure properties, solubility, chaperone requirements and RNA-binding abilities. Using cross-validation and independent datasets, the cleverSuite reproduces experimental findings with great accuracy and provides models that can be used for future investigations.Availability: The intuitive interface for dataset exploration, analysis and prediction is available at http://s.tartaglialab.com/clever_suite.Contact: gian.tartaglia@crg.esSupplementary information: Supplementary data are available at Bioinformatics online.

A Comprehensive Analysis of RALF Proteins in Green Plants Suggests There Are Two Distinct Functional Groups

“…CleverMachine (Klus et al, 2014) allows for the detection of protein properties that differ between two datasets and has been used to distinguish P-bodies and stress granules from other globular proteins (Marchese et al, 2016) and to classify homo-repeat proteins (Yu Lobanov et al, 2016). A multi-cleverMachine property prediction and comparison of the RALFs reveals that the clade IV proteins differ in a variety of physico-chemical properties from the other clades (Figure S4), such as a reduced disorder propensity.…”

“…The online multi-cleverMachine tool (Klus et al, 2014) was used to compare the physico-chemical properties of RALF proteins. Unaligned sequences for each clade were uploaded to the server in FASTA format, with “clade IV” proteins being denoted as the positive set and clade I, II, and III proteins as negative sets.…”

Advances in the characterization of RNA‐binding proteins

“…cat RAPID signature exploits properties such as hydrophobicity, secondary structure, disorder, and burial . Each feature defines a unique signature , or profile, containing position‐specific information arranged in sequential order from the N‐ to the C‐terminus . In addition to the RNA‐binding score, cat RAPID signature predicts regions contacting RNA.…”

“…26 Especially physicochemical properties of amino acids, such as structural disorder and polarity, are relevant to characterize the RNA-binding ability of proteins. 27,28 Indeed, recent studies reported that in addition to classical RNA-binding domains other regions found in ribosomal proteins, translation elongation factors, zinc fingers as well as structurally disordered parts participate in contacting transcripts. 29 Here we review the most recent experimental and computational advances for the detection of protein-RNA interactions and introduce new challenges for future developments in the field.…”

Quarterly intrinsic disorder digest (April–May–June, 2014)

“…Furthermore, it includes a classifier to allow for the classification of new data sets. 55 Dickson and Brooks present a novel strategy to determine structural ensembles for molecular dynamics simulations that uses dynamically defined sampling regions organized in a hierarchical framework. 56 Finally, Pryor and Wiener evaluated multiple existing disorder predictors for the detection of intrinsic disorder in membrane proteins.…”