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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Project description:Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg-Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213-623 K) and pressures (0.1-25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12-647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), Redlich-Kwong (RK), Zudkevitch-Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

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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