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Wang Y, Song J, Marquez-Lago TT, Leier A, Li C, Lithgow T, Webb GI, Shen HB. Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites. Sci Rep 2017; 7:5755. [PMID: 28720874 PMCID: PMC5515926 DOI: 10.1038/s41598-017-06219-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 06/08/2017] [Indexed: 11/24/2022] Open
Abstract
Matrix Metalloproteases (MMPs) are an important family of proteases that play crucial roles in key cellular and disease processes. Therefore, MMPs constitute important targets for drug design, development and delivery. Advanced proteomic technologies have identified type-specific target substrates; however, the complete repertoire of MMP substrates remains uncharacterized. Indeed, computational prediction of substrate-cleavage sites associated with MMPs is a challenging problem. This holds especially true when considering MMPs with few experimentally verified cleavage sites, such as for MMP-2, -3, -7, and -8. To fill this gap, we propose a new knowledge-transfer computational framework which effectively utilizes the hidden shared knowledge from some MMP types to enhance predictions of other, distinct target substrate-cleavage sites. Our computational framework uses support vector machines combined with transfer machine learning and feature selection. To demonstrate the value of the model, we extracted a variety of substrate sequence-derived features and compared the performance of our method using both 5-fold cross-validation and independent tests. The results show that our transfer-learning-based method provides a robust performance, which is at least comparable to traditional feature-selection methods for prediction of MMP-2, -3, -7, -8, -9 and -12 substrate-cleavage sites on independent tests. The results also demonstrate that our proposed computational framework provides a useful alternative for the characterization of sequence-level determinants of MMP-substrate specificity.
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Affiliation(s)
- Yanan Wang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, 3800, Australia
| | - Jiangning Song
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
- ARC Centre of Excellence for Advanced Molecular Imaging, Monash University, Melbourne, VIC, 3800, Australia
| | - Tatiana T Marquez-Lago
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - André Leier
- Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Chen Li
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, 3800, Australia
| | - Trevor Lithgow
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, 3800, Australia.
| | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
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