Protein structure modeling and refinement by global optimization in CASP12.

Journal: Proteins
Published Date:

Abstract

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded.

Authors

  • Seung Hwan Hong
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • InSuk Joung
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Jose C Flores-Canales
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Balachandran Manavalan
    Department of Physiology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Qianyi Cheng
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Seungryong Heo
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Jong Yun Kim
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Sun Young Lee
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Mikyung Nam
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Keehyoung Joo
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • In-Ho Lee
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Sung Jong Lee
    Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, South Korea.
  • Jooyoung Lee
    Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea.