Improving decoy databases for protein folding algorithms

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationProceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics - BCB '14
PublisherAssociation for Computing Machinery (ACM)
Pages717-724
Number of pages8
ISBN (Print)9781450328944
DOIs
StatePublished - 2014
Externally publishedYes

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