Open Access Highly Accessed Research article

Identification of properties important to protein aggregation using feature selection

Yaping Fang12, Shan Gao2, David Tai2, C Russell Middaugh3 and Jianwen Fang24*

Author Affiliations

1 College of Science, Institute for Computer Applications, Huazhong Agricultural University, Wuhan 430070, P.R. China

2 Applied Bioinformatics Laboratory, University of Kansas, 2034 Becker Drive, Lawrence, KS 66047, USA

3 Department of Pharmaceutical Chemistry, University of Kansas, 2030 Becker Drive, Lawrence, KS 66047, USA

4 Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 9609 Medical Center Dr, Rockville, MD 20850, USA

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BMC Bioinformatics 2013, 14:314  doi:10.1186/1471-2105-14-314

Published: 28 October 2013

Abstract

Background

Protein aggregation is a significant problem in the biopharmaceutical industry (protein drug stability) and is associated medically with over 40 human diseases. Although a number of computational models have been developed for predicting aggregation propensity and identifying aggregation-prone regions in proteins, little systematic research has been done to determine physicochemical properties relevant to aggregation and their relative importance to this important process. Such studies may result in not only accurately predicting peptide aggregation propensities and identifying aggregation prone regions in proteins, but also aid in discovering additional underlying mechanisms governing this process.

Results

We use two feature selection algorithms to identify 16 features, out of a total of 560 physicochemical properties, presumably important to protein aggregation. Two predictors (ProA-SVM and ProA-RF) using selected features are built for predicting peptide aggregation propensity and identifying aggregation prone regions in proteins. Both methods are compared favourably to other state-of-the-art algorithms in cross validation. The identified important properties are fairly consistent with previous studies and bring some new insights into protein and peptide aggregation. One interesting new finding is that aggregation prone peptide sequences have similar properties to signal peptide and signal anchor sequences.

Conclusions

Both predictors are implemented in a freely available web application (http://www.abl.ku.edu/ProA/ webcite). We suggest that the quaternary structure of protein aggregates, especially soluble oligomers, may allow the formation of new molecular recognition signals that guide aggregate targeting to specific cellular sites.

Keywords:
Aggregation; Amyloid; Peptide; Prediction; Feature selection; Machine learning