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Aydin Z, Azginoglu N, Bilgin HI, Celik M. Developing structural profile matrices for protein secondary structure and solvent accessibility prediction. Bioinformatics 2019; 35:4004-4010. [DOI: 10.1093/bioinformatics/btz238] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2018] [Revised: 02/17/2019] [Accepted: 03/29/2019] [Indexed: 11/13/2022] Open
Abstract
Abstract
Motivation
Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target.
Results
In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests.
Availability and implementation
The DSPRED method can be accessed by visiting the PSP server at http://psp.agu.edu.tr. Source code and binaries are freely available at https://github.com/yusufzaferaydin/dspred.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zafer Aydin
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | - Nuh Azginoglu
- Department of Computer Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey
| | | | - Mete Celik
- Department of Computer Engineering, Erciyes University, Kayseri, Turkey
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