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Sparse Estimation for Structural Variability

  • Raghavendra Hosur
  • Rohit Singh
  • Bonnie Berger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)

Abstract

Proteins are dynamic molecules that exhibit a wide range of motions; often these conformational changes are important for protein function. Determining biologically relevant conformational changes, or true variability, efficiently is challenging due to the noise present in structure data. In this paper we present a novel approach to elucidate conformational variability in structures solved using X-ray crystallography. We first infer an ensemble to represent the experimental data and then formulate the identification of truly variable members of the ensemble (as opposed to those that vary only due to noise) as a sparse estimation problem. Our results indicate that the algorithm is able to accurately distinguish genuine conformational changes from variability due to noise. We validate our predictions for structures in the Protein Data Bank by comparing with NMR experiments, as well as on synthetic data. In addition to improved performance over existing methods, the algorithm is robust to the levels of noise present in real data. In the case of Ubc9, variability identified by the algorithm corresponds to functionally important residues implicated by mutagenesis experiments. Our algorithm is also general enough to be integrated into state-of-the-art software tools for structure-inference.

Keywords

Structural Variability Sparse Estimation Lasso Regression Neural Information Processing System True Variability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Raghavendra Hosur
    • 1
    • 3
  • Rohit Singh
    • 1
  • Bonnie Berger
    • 1
    • 2
  1. 1.Computer Science and Artificial Intelligence LaboratoryMIT, Massachusetts Institute of TechnologyCambridge
  2. 2.Dept. Of MathematicsMITUSA
  3. 3.Dept. Of Materials Science and Eng.MITUSA

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