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.
Bibliographical noteKAUST Repository Item: Exported on 2021-11-03
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, and IIS-0917266; by THECB NHARP award 000512-0097-2009; by Chevron, IBM, Intel, Oracle/Sun; and by award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). A preliminary version of this work appeared in Lindsey et al. (2014).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.