Journal article
EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models
Journal of molecular biology, Vol.428(6), pp.1394-1405
27/03/2016
PMID: 26804571
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Source: InCites
Abstract
Protein engineering and characterisation of non-synonymous single nucleotide variants (SNVs) require accurate prediction of protein stability changes (ΔΔGu) induced by single amino acid substitutions. Here, we have developed a new prediction method called Evolutionary, Amino acid, and Structural Encodings with Multiple Models (EASE-MM), which comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. The new method is applicable to single-domain monomeric proteins and can predict ΔΔGu with a protein sequence and mutation as the only inputs. EASE-MM yielded a Pearson correlation coefficient of 0.53–0.59 in 10-fold cross-validation and independent testing and was able to outperform other sequence-based methods. When compared to structure-based energy functions, EASE-MM achieved a comparable or better performance. The application to a large dataset of human germline non-synonymous SNVs showed that the disease-causing variants tend to be associated with larger magnitudes of ΔΔGu predicted with EASE-MM. The EASE-MM web-server is available at http://sparks-lab.org/server/ease.
Details
- Title
- EASE-MM: Sequence-Based Prediction of Mutation-Induced Stability Changes with Feature-Based Multiple Models
- Creators
- Lukas Folkman - Griffith UniversityBela Stantic - Griffith UniversityAbdul Sattar - Griffith UniversityYaoqi Zhou - Griffith University
- Publication Details
- Journal of molecular biology, Vol.428(6), pp.1394-1405
- Publisher
- Elsevier Ltd
- Grant note
- National Health and Medical Research Council of Australia: 1059775, 1083450 Australian Research Council's Linkage Infrastructure, Equipment and Facilities funding scheme: LE150100161 Australian GovernmentAustralian Research Council through the ICT Centre of Excellence programGriffith University eResearch Services Team
Helpful discussions with Yuedong Yang and Jaroslav Bendl are gratefully acknowledged. This work was supported in part by National Health and Medical Research Council (1059775 and 1083450) of Australia and Australian Research Council's Linkage Infrastructure, Equipment and Facilities funding scheme (project number LE150100161) to Y.Z. NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy, and the Australian Research Council through the ICT Centre of Excellence program. The authors also gratefully acknowledge the support of the Griffith University eResearch Services Team and the use of the High Performance Computing Cluster Gowonda to complete this research. This work was implemented using the GNU Parallel program [49].
- Identifiers
- 991013348039702368
- Copyright
- © 2016 Elsevier Ltd. All rights reserved.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article