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Smialowski, P. ; Doose, G.* ; Torkler, P.* ; Kaufmann, S.* ; Frishman, D.

PROSO II - a new method for protein solubility prediction.

FEBS J. 279, 2192-2200 (2012)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Many fields of science and industry depend on efficient production of active protein using heterologous expression in Escherichia coli. The solubility of proteins upon expression is dependent on their amino acid sequence. Prediction of solubility from sequence is therefore highly valuable. We present a novel machine-learning-based model called PROSO II which makes use of new classification methods and growth in experimental data to improve coverage and accuracy of solubility predictions. The classification algorithm is organized as a two-layered structure in which the output of a primary Parzen window model for sequence similarity and a logistic regression classifier of amino acid k-mer composition serve as input for a second-level logistic regression classifier. Compared with previously published research our model is trained on five times more data than used by any other method before (82 000 proteins). When tested on a separate holdout set not used at any point of method development our server attained the best results in comparison with other currently available methods: accuracy 75.4%, Matthews correlation coefficient 0.39, sensitivity 0.731, specificity 0.759, gain (soluble) 2.263. In summary, due to utilization of cutting edge machine learning technologies combined with the largest currently available experimental data set the PROSO II server constitutes a substantial improvement in protein solubility predictions. PROSO II is available at .
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Classification ; Feature Selection ; Machine Learning ; Prediction ; Protein Solubility; SEQUENCE-BASED PREDICTION; ESCHERICHIA-COLI; STRUCTURAL PROTEOMICS; STABILITY CHANGES; POINT MUTATIONS; INCLUSION-BODY; EXPRESSION; DATABASE; SERVER; TOOL
ISSN (print) / ISBN 1742-464X
e-ISSN 1742-4658
Zeitschrift FEBS Journal, The
Quellenangaben Band: 279, Heft: 12, Seiten: 2192-2200 Artikelnummer: , Supplement: ,
Verlag Wiley
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Bioinformatics and Systems Biology (IBIS)