Dataset Information

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

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.

SUBMITTER: Rawi R

PROVIDER: S-EPMC6031027 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Rawi Reda R Mall Raghvendra R Kunji Khalid K Shen Chen-Hsiang CH Kwong Peter D PD Chuang Gwo-Yu GY

Bioinformatics (Oxford, England) 20180401 7

<h4>Motivation</h4>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.<h4>Results</h4>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, PaRSn ...[more]

PMID: 29069295

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Dataset Information

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

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

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

Similar Datasets

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