Taylor, CC, Mardia, KV, Di Marzio, M and Panzera, A (2012) Validating protein structure using kernel density estimates. Journal of Applied Statistics, 39 (11). 2379 - 2388 . ISSN 0266-4763Full text available as:
Available under licence : See the attached licence file.
Measuring the quality of determined protein structures is a very important problem in bioinformatics. Kernel density estimation is a well-known nonparametric method which is often used for exploratory data analysis. Recent advances, which have extended previous linear methods to multi-dimensional circular data, give a sound basis for the analysis of conformational angles of protein backbones, which lie on the torus. By using an energy test, which is based on interpoint distances, we initially investigate the dependence of the angles on the amino acid type. Then, by computing tail probabilities which are based on amino-acid conditional density estimates, a method is proposed which permits inference on a test set of data. This can be used, for example, to validate protein structures, choose between possible protein predictions and highlight unusual residue angles.
|Copyright, Publisher and Additional Information:||This is an Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of Applied Statitics,July 2012, © Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/02664763.2012.710898].|
|Institution:||The University of Leeds|
|Academic Units:||The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)|
|Depositing User:||Symplectic Publications|
|Date Deposited:||15 Nov 2012 10:20|
|Last Modified:||10 Jun 2014 11:47|
|Publisher:||Taylor & Francis|
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