Decoy Database Improvement for Protein Folding

Hsin-Yi (Cindy) Yeh, Aaron Lindsey, Chih-Peng Wu, Shawna Thomas, Nancy M. Amato

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)823-836
Number of pages14
Issue number9
StatePublished - 2015
Externally publishedYes


Dive into the research topics of 'Decoy Database Improvement for Protein Folding'. Together they form a unique fingerprint.

Cite this