Protein Docking: Combining Symbolic Descriptions of Molecular Surfaces and Grid-Based Scoring Functions

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Contents

With the growing number of known 3D protein structures, computing systems, that can predict where two protein molecules interact with each other are becoming of increasing interest. A system is presented, integrating preprocessing like the computation of molecular surfaces, segmentation, and searching for complementarity in the general framework of a pattern analyzing semantic network (ERNEST). The score of coarse symbolic computations is used by the problem independent control strategy of ERNEST to guide a more detailed analysis considering steric clash and judgements based on grid--based surface representations. Successfull examples of the docking system are discussed that compare well with other approaches.