FILTREST3D: discrimination of structural models using restraints from experimental data

摘要

Automatic methods for macromolecular structure prediction ( fold recognition, de novo folding and docking programs) produce large sets of alternative models. These large model sets often include many native-like structures, which are often scored as false positives. Such native-like models can be more easily identified based on data from experimental analyses used as structural restraints ( e. g. identification of nearby residues by cross-inking, chemical modification, site-directed mutagenesis, deuterium exchange coupled with mass spectrometry, etc.). We present a simple server for scoring and ranking of models according to their agreement with user-defined restraints.