CoRe-CMAP Toolkit

David Snyder, Department of Chemistry, College of Science and Health, William Paterson University
Additional contributors to this project include Rebecca Vera, Melissa Synsmir and Sarah Ojinnaka.
Proteins are molecular machines whose function depends on how they flex and bend. This toolkit implements the CoRe-CMAP (Conformationally Restraining Contact MAP) model for predicting protein flexibility from sequence-based inferences of protein disorder as well as secondary and tertiary structure. The CoRe-CMAP model uses secondary structure predictions to fill in short range contacts typically missing in a CASP formatted predicted contact map and disorder prediction to identify which contacts are likely to be conformationally restraining. Contact-map based prediction of Lipari-Szabo Order parameters and Gaussian Network Modeling both follow collation of the map of conformationally restraining contacts. Required input data is a per-residue prediction of likelihood of disorder, a secondary structure prediction and a CASP formatted predicted contact map (tertiary structure prediction). An example data set (based on BMRB entry #15536) is provided. The author and additional contributors are currently preparing a paper describing our results using this software to predict Lipari-Szabo order parameters.