CASP9 assessment of free modeling target predictions

Authors

  • Lisa Kinch,

    Corresponding author
    1. Howard Hughes Medical Institute, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    • UT Southwestern, Biochemistry, 5323 Harry Hines Blvd, Dallas, TX 75390
    Search for more papers by this author
    • Lisa Kinch and Shuo Yong Shi authors contributed equally to this work.

  • Shuo Yong Shi,

    1. Department of Biochemistry, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    Search for more papers by this author
    • Lisa Kinch and Shuo Yong Shi authors contributed equally to this work.

  • Qian Cong,

    1. Department of Biochemistry, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    Search for more papers by this author
  • Hua Cheng,

    1. Department of Biochemistry, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    Search for more papers by this author
  • Yuxing Liao,

    1. Department of Biochemistry, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    Search for more papers by this author
  • Nick V. Grishin

    1. Howard Hughes Medical Institute, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    2. Department of Biochemistry, University of Texas, Southwestern Medical Center, Dallas, Texas 75390-9050
    Search for more papers by this author

  • The authors state no conflict of interest.

Abstract

We present an overview of the ninth round of Critical Assessment of Protein Structure Prediction (CASP9) “Template free modeling” category (FM). Prediction models were evaluated using a combination of established structural and sequence comparison measures and a novel automated method designed to mimic manual inspection by capturing both global and local structural features. These scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. Moreover, we discuss a few examples of free modeling targets to highlight the progress and bottlenecks of current prediction methods. Notably, a server prediction model for a single target (T0581) improved significantly over the closest structure template (44% GDT increase). This accomplishment represents the “winner” of the CASP9 FM category. A number of human expert groups submitted slight variations of this model, highlighting a trend for human experts to act as “meta predictors” by correctly selecting among models produced by the top-performing automated servers. The details of evaluation are available at http://prodata.swmed.edu/CASP9/. © Proteins 2011; © 2011 Wiley-Liss, Inc.

Ancillary