Statistical characterization of protein ensembles.

Journal Article (Journal Article)

When accounting for structural fluctuations or measurement errors, a single rigid structure may not be sufficient to represent a protein. One approach to solve this problem is to represent the possible conformations as a discrete set of observed conformations, an ensemble. In this work, we follow a different richer approach, and introduce a framework for estimating probability density functions in very high dimensions, and then apply it to represent ensembles of folded proteins. This proposed approach combines techniques such as kernel density estimation, maximum likelihood, cross-validation, and bootstrapping. We present the underlying theoretical and computational framework and apply it to artificial data and protein ensembles obtained from molecular dynamics simulations. We compare the results with those obtained experimentally, illustrating the potential and advantages of this representation.

Full Text

Duke Authors

Cited Authors

  • Rother, D; Sapiro, G; Pande, V

Published Date

  • January 2008

Published In

Volume / Issue

  • 5 / 1

Start / End Page

  • 42 - 55

PubMed ID

  • 18245874

Electronic International Standard Serial Number (EISSN)

  • 1557-9964

International Standard Serial Number (ISSN)

  • 1545-5963

Digital Object Identifier (DOI)

  • 10.1109/tcbb.2007.1061


  • eng