DomHR : Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy
Zhang, Xiao Yan ; Lu, Long Jian ; Song, Qi ; Yang, Qian Qian ; Li, Da Peng ; Sun, Jiang Ming LU
; Li, Tong Hua
and Cong, Pei Sheng
(2013)
In PLoS ONE
8(4).
- Abstract
Motivation: The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved. Results: In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of... (More)
Motivation: The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved. Results: In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy. Availability: The DomHR is available at http://cal.tongji.edu.cn/domain/.
(Less)
- author
- Zhang, Xiao Yan
; Lu, Long Jian
; Song, Qi
; Yang, Qian Qian
; Li, Da Peng
; Sun, Jiang Ming
LU
; Li, Tong Hua
and Cong, Pei Sheng
- publishing date
- 2013-04-11
- type
- Contribution to journal
- publication status
- published
- in
- PLoS ONE
- volume
- 8
- issue
- 4
- article number
- e60559
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- scopus:84876136942
- pmid:23593247
- ISSN
- 1932-6203
- DOI
- 10.1371/journal.pone.0060559
- language
- English
- LU publication?
- no
- id
- 740c21ef-0dd0-4011-a9f0-8a879e8ef8c9
- date added to LUP
- 2023-05-03 22:08:18
- date last changed
- 2024-01-19 21:48:28
@article{740c21ef-0dd0-4011-a9f0-8a879e8ef8c9,
abstract = {{<p>Motivation: The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved. Results: In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy. Availability: The DomHR is available at http://cal.tongji.edu.cn/domain/.</p>}},
author = {{Zhang, Xiao Yan and Lu, Long Jian and Song, Qi and Yang, Qian Qian and Li, Da Peng and Sun, Jiang Ming and Li, Tong Hua and Cong, Pei Sheng}},
issn = {{1932-6203}},
language = {{eng}},
month = {{04}},
number = {{4}},
publisher = {{Public Library of Science (PLoS)}},
series = {{PLoS ONE}},
title = {{DomHR : Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy}},
url = {{http://dx.doi.org/10.1371/journal.pone.0060559}},
doi = {{10.1371/journal.pone.0060559}},
volume = {{8}},
year = {{2013}},
}