Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information.
10.1016/j.gpb.2019.08.002
- Author:
Fuyi LI
1
,
2
;
Andre LEIER
3
;
Quanzhong LIU
4
;
Yanan WANG
1
,
2
;
Dongxu XIANG
1
,
5
;
Tatsuya AKUTSU
6
;
Geoffrey I WEBB
7
;
A Ian SMITH
1
,
8
;
Tatiana MARQUEZ-LAGO
9
;
Jian LI
10
;
Jiangning SONG
1
,
11
,
12
Author Information
1. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
2. Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.
3. School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA.
4. College of Information Engineering, Northwest A&F University, Yangling 712100, China.
5. College of Information Engineering, Northwest A&F University, Yangling 712100, China.
6. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
7. Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.
8. ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
9. School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA. Electronic address: tmarquezlago@uabmc.edu.
10. Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia. Electronic address: Jian.Li@monash.edu.
11. Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
12. ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia. Electronic address: Jiangning.Song@monash.edu.
- Publication Type:Journal Article
- Keywords:
Cleavage site prediction;
Conditional random field;
Machine learning;
Protease;
Structural determinants
- From:
Genomics, Proteomics & Bioinformatics
2020;18(1):52-64
- CountryChina
- Language:English
-
Abstract:
Proteases are enzymes that cleave and hydrolyse the peptide bonds between two specific amino acid residues of target substrate proteins. Protease-controlled proteolysis plays a key role in the degradation and recycling of proteins, which is essential for various physiological processes. Thus, solving the substrate identification problem will have important implications for the precise understanding of functions and physiological roles of proteases, as well as for therapeutic target identification and pharmaceutical applicability. Consequently, there is a great demand for bioinformatics methods that can predict novel substrate cleavage events with high accuracy by utilizing both sequence and structural information. In this study, we present Procleave, a novel bioinformatics approach for predicting protease-specific substrates and specific cleavage sites by taking into account both their sequence and 3D structural information. Structural features of known cleavage sites were represented by discrete values using a LOWESS data-smoothing optimization method, which turned out to be critical for the performance of Procleave. The optimal approximations of all structural parameter values were encoded in a conditional random field (CRF) computational framework, alongside sequence and chemical group-based features. Here, we demonstrate the outstanding performance of Procleave through extensive benchmarking and independent tests. Procleave is capable of correctly identifying most cleavage sites in the case study. Importantly, when applied to the human structural proteome encompassing 17,628 protein structures, Procleave suggests a number of potential novel target substrates and their corresponding cleavage sites of different proteases. Procleave is implemented as a webserver and is freely accessible at http://procleave.erc.monash.edu/.
