Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.
Abstract
A geometric recognition algorithm was developed to identify molecular surface complementarity. It is based on a purely geometric approach and takes advantage of techniques applied in the field of pattern recognition. The algorithm involves an automated procedure including (i) a digital representation of the molecules (derived from atomic coordinates) by threedimensional discrete functions that distinguishes between the surface and the interior; (ii) the calculation, using Fourier transformation, of a correlation function that assesses the degree of molecular surface overlap and penetration upon relative shifts of the molecules in three dimensions; and (iii) a scan of the relative orientations of the molecules in three dimensions. The algorithm provides a list of correlation values indicating the extent of geometric match between the surfaces of the molecules; each of these values is associated with six numbers describing the relative position (translation and rotation) of the molecules. The procedure is thus equivalent to a sixdimensional search but much faster by design, and the computation time is only moderately dependent on molecular size. The procedure was tested and validated by using five known complexes for which the correct relative position of the molecules in the respective adducts was successfully predicted. The molecular pairs were deoxyhemoglobin and methemoglobin, tRNA synthetasetyrosinyl adenylate, aspartic proteinasepeptide inhibitor, and trypsintrypsin inhibitor. A more realistic test was performed with the last two pairs by using the structures of uncomplexed aspartic proteinase and trypsin inhibitor, respectively. The results are indicative of the extent of conformational changes in the molecules tolerated by the algorithm.
 Publication:

Proceedings of the National Academy of Science
 Pub Date:
 March 1992
 DOI:
 10.1073/pnas.89.6.2195
 Bibcode:
 1992PNAS...89.2195K