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

Bioinformatics. 2014 Jun 1;30(11):1601-8. doi: 10.1093/bioinformatics/btu074. Epub 2014 Feb 3.


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

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Intrinsically Disordered Proteins / chemistry
  • Molecular Chaperones / chemistry*
  • Molecular Chaperones / metabolism
  • Protein Structure, Secondary
  • Proteins / chemistry*
  • RNA-Binding Proteins / chemistry*
  • RNA-Binding Proteins / metabolism
  • Sequence Analysis, Protein
  • Software*
  • Solubility


  • Intrinsically Disordered Proteins
  • Molecular Chaperones
  • Proteins
  • RNA-Binding Proteins