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.

The cleverSuite approach for protein characterization: Predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities / Klus, Petr; Bolognesi, Benedetta; Agostini, Federico; Marchese, Domenica; Zanzoni, Andreas; Tartaglia, Gian Gaetano. - In: BIOINFORMATICS. - ISSN 1367-4811. - 30:11(2014), pp. 1601-1608. [10.1093/bioinformatics/btu074]

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

Marchese, Domenica;Tartaglia, Gian Gaetano
2014

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1279494
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