期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 22, 期 9, 页码 823-836出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2015.0116
关键词
decoy databases; protein folding; sampling methods
类别
资金
- NSF [CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266]
- THECB NHARP [000512-0097-2009]
- Chevron
- IBM
- Intel
- Oracle/Sun
- King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1423111] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0916053] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1217991] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0833199] Funding Source: National Science Foundation
Predicting protein structures and simulating protein folding are two of the most important problems in computational biology today. Simulation methods rely on a scoring function to distinguish the native structure (the most energetically stable) from non-native structures. Decoy databases are collections of non-native structures used to test and verify these functions. We present a method to evaluate and improve the quality of decoy databases by adding novel structures and removing redundant structures. We test our approach on 20 different decoy databases of varying size and type and show significant improvement across a variety of metrics. We also test our improved databases on two popular modern scoring functions and show that for most cases they contain a greater or equal number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions.
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