PREDITOR a web server for predicting protein torsion angle restraints.pdf

PREDITOR: a web server for predicting protein torsion angle restraints Mark V. Berjanskii1, Stephen Neal2 and David S. Wishart1,2,3,* 1Department of Computing Science, 2Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada and 3NRC National Institute for Nanotechnology (NINT), Edmonton, AB, Canada Received February 14, 2006; Revised March 7, 2006; Accepted April 18, 2006 ABSTRACT Every year between 500 and 1000 peptide and protein structures are determined byNMR and deposited into the Protein Data Bank. However, the process of NMR structure determination continues to be a manually intensive and time-consuming task. One of the most tedious and error-prone aspects of this process involves the determination of torsion angle restraints including phi, psi, omega and chi angles. Most meth- ods require many days of additional experiments, painstaking measurements or complex calcula- tions. Here we wish to describe a web server, called PREDITOR, which greatly accelerates and simplifies this task. PREDITOR accepts sequence and/or chemical shift data as input and generates torsion angle predictions (with predicted errors) for phi, psi, omega and chi-1 angles. PREDITOR combines sequence alignment methods with advanced chem- ical shift analysis techniques to generate its torsion angle predictions. The method is fast (,40 s per pro- tein) and accurate, with 88% of phi/psi predictions being within 30
of the correct values, 84% of chi-1 predictionsbeingcorrectand99.97%ofomegaangles being correct. PREDITOR is 35 times faster and up to 20% more accurate than any existing method. PREDITOR also provides accurate assessments of the torsion angle errors so that the torsion angle constraints can be readily fed into standard structure refinement programs, such as CNS, XPLOR, AMBER and CYANA. Other unique features to PREDITOR include dihedral angle prediction via PDB structure mapping, automated chemical shift re-referencing (to improve accuracy), prediction of pr