期刊
MOLECULAR THERAPY-NUCLEIC ACIDS
卷 17, 期 -, 页码 396-404出版社
CELL PRESS
DOI: 10.1016/j.omtn.2019.06.004
关键词
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资金
- National Natural Science Foundation of China [61672184, 61732012, 61822306, 61573118]
- Fok Ying-Tung Education Foundation for Young Teachers in the Higher Education Institutions of China [161063]
- Shenzhen Overseas High Level Talents Innovation Foundation [KQJSCX20170327161949608]
- Scientific Research Foundation in Shenzhen [JCYJ20180306172207178, JCYJ20170307150528934, JCYJ20170811153836555]
Accurate identification of intrinsically disordered proteins/regions (IDPs/IDRs) is critical for predicting protein structure and function. Previous studies have shown that IDRs of different lengths have different characteristics, and several classification-based predictors have been proposed for predicting different types of IDRs. Compared with these classification-based predictors, the previously proposed predictor IDP-CRF exhibits state-of-the-art performance for predicting IDPs/IDRs, which is a sequence labeling model based on conditional random fields (CRFs). Motivated by these methods, we propose a predictor called IDP-FSP, which is an ensemble of three CRF-based predictors called IDP-FSP-L, IDP-FSP-S, and IDP-FSP-G. These three predictors are specially designed to predict long, short, and generic disordered regions, respectively, and they are constructed based on different features. To the best of our knowledge, IDP-FSP is the first predictor that combines a sequence labeling algorithm with IDRs of different lengths. Experimental results using two independent test datasets show that IDP-FSP achieves better or at least comparable predictive performance with 26 existing state-of-the-art methods in this field, proving the effectiveness of IDP-FSP.
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