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Bioinformatics. 2018 Apr 1;34(7):1092-1098. doi: 10.1093/bioinformatics/btx662.

PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine.

Author information

1
Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
2
Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, 34110, Qatar.

Abstract

Motivation:

Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought.

Results:

In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability.

Availability and implementation:

PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP).

Contact:

gwo-yu.chuang@nih.gov.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
29069295
PMCID:
PMC6031027
DOI:
10.1093/bioinformatics/btx662
[Indexed for MEDLINE]
Free PMC Article

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