Determining protein structure from electron-density maps using pattern matching

TEXTAL is an automated system for building protein structures from electron -density maps. It uses pattern recognition to select regions in a database of previously determined structures that are similar to regions in a map of unknown structure. Rotation-invariant numerical values, called features, o f the electron density are extracted from spherical regions in an unknown m ap and compared with features extracted around regions in maps generated fr om a database of known structures. Those regions in the database that match best provide the local coordinates of atoms and these are accumulated to f orm a model of the unknown structure. Similarity between the regions in the database and an uninterpreted region is determined firstly by evaluating t he numerical difference in feature values and secondly by calculating the e lectron-density correlation coefficient for those regions with similar feat ure values. TEXTAL has been successful at building protein structures for a wide range of test electron-density maps and can automatically model entir e protein structures in a few hours on a workstation. Models built by TEXTA L from test electron-density maps of known protein structures were accurate to within 0.60-0.7 Angstrom root-mean-square deviation, assuming prior kno wledge of C-alpha positions. The system represents a new approach to protei n structure determination and has the potential to greatly reduce the time required to interpret electron-density maps in order to build accurate prot ein models.