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

作     者：Rawi, Reda Mall, Raghvendra Kunji, Khalid Shen, Chen-Hsiang Kwong, Peter D. Chuang, Gwo-Yu 

作者机构：NIAID Vaccine Res Ctr NIH Bethesda MD 20892 USA Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha 34110 Qatar 

出 版 物：《BIOINFORMATICS》 (生物信息学)

年 卷 期：2018年第34卷第7期

页      面：1092-1098页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Intramural Research Program (National Institute of Allergy and Infectious Diseases  National Institutes of Health  USA) 

主　　题：PROTEIN solubility BOOSTING algorithms SEQUENCES (Mathematics) CORRELATORS TRIPEPTIDES ACCURACY 

摘      要：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.