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Discrimination of soluble and aggregation-prone proteins based on sequence information

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Molecular BioSystems Vol - 9 Issue - 3

Molecular BioSystems Vol - 9 Issue - 2

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Discrimination of soluble and aggregation-prone proteins based on sequence information

Content Provider	Royal Society of Chemistry (RSC)
Author	Fang, Yaping Fang, Jianwen
Copyright Year	2013
Abstract	Understanding the factors governing protein solubility is a key to grasp the mechanisms of protein solubility and may provide insight into protein aggregation and misfolding related diseases such as Alzheimer's disease. In this work, we attempt to identify factors important to protein solubility using feature selection. Firstly, we calculate 1438 features including physicochemical properties and statistics for each protein. Random Forest algorithm is used to select the most informative and the minimal subset of features based on their predictive performance. A predictive model is built based on 17 selected features. Compared with previous models, our model achieves better performance with a sensitivity of 0.82, specificity 0.85, ACC 0.84, AUC 0.91 and MCC 0.67. Furthermore, a model using a redundancy-reduced dataset (sequence identity <= 30%) achieves the same performance as the model without redundancy reduction. Our results provide not only a reliable model for predicting protein solubility but also a list of features important to protein solubility. The predictive model is implemented as a freely available web application at http://shark.abl.ku.edu/ProS/.
Starting Page	806
Ending Page	811
Page Count	6
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	9
Issue Number	4
Journal	Molecular BioSystems
DOI	10.1039/c3mb70033j
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Feature selection Anterior cingulate cortex Web application Random forest MCC Algorithm Protein Random Forest
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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