TY - GEN
T1 - Improving decoy databases for protein folding algorithms
AU - Lindsey, Aaron
AU - Yeh, Hsin-Yi (Cindy)
AU - Wu, Chih-Peng
AU - Thomas, Shawna
AU - Amato, Nancy M.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This work is supported in part by NSF awards CRI-0551685,CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266 by THECBNHARP award 000512-0097-2009, by Chevron, IBM, Intel,Oracle/Sun and by Award KUS-C1-016-04, made by KingAbdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2014
Y1 - 2014
N2 - Copyright © 2014 ACM. 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 17 different decoy databases of varying size and type and show significant improvement across a variety of metrics. We also test our improved databases on a popular modern scoring function and show that they contain a greater number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions.
AB - Copyright © 2014 ACM. 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 17 different decoy databases of varying size and type and show significant improvement across a variety of metrics. We also test our improved databases on a popular modern scoring function and show that they contain a greater number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions.
UR - http://hdl.handle.net/10754/598583
UR - http://dl.acm.org/citation.cfm?doid=2649387.2660839
UR - http://www.scopus.com/inward/record.url?scp=84920731870&partnerID=8YFLogxK
U2 - 10.1145/2649387.2660839
DO - 10.1145/2649387.2660839
M3 - Conference contribution
SN - 9781450328944
SP - 717
EP - 724
BT - Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14
PB - Association for Computing Machinery (ACM)
ER -