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© Oxford University Press

An automated computer vision and roboticsbased technique for 3-D flexible biomolecular docking and matching

Bilha Sandak 1, Ruth Nussinov 2,3,* and Haim J. Wolfson 1

1Computer Science Department School of Mathematical Sciences, Sackler Faculty of Exact Sciences. Tel Aviv University Tel Aviv 69978, Israel
2Laboratory of Mathematical biology, PRI Dynacorp, NCI-FCRF Bldg 469, rm 151, Frederick, MD 21712, USA
3Sackler Institute of Molecular Medicine, Faculty of Medicine, Tel Aviv University Tel Aviv 69978, Israel

*Corresponding author (at USA)

The generation of binding modes between two molecules, also known as molecular docking, is a key problem in rational drug design and biomolecular recognition. Docking a ligand, e.g., a drug molecule or a protein molecule, to a protein receptor, involves recognition of molecular surfaces as molecules interact at their surface. Recent studies report that the activity of many molecules induces conformational transitions by ‘hinge-bending’, which involves movements of relatively rigid parts with respect to each other. In ligand–receptor binding, relative rotational movements of molecu–lar substructures about their common hinges have been observed. For automatically predicting flexible molecular interactions, we adapt a new technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulated objects. These type of objects consist of rigid parts which are connected by rotary joints (hinges). Our approach is based on an extension and generalization of the Geometric Hashing and Generalized Hough Transform paradigm for rigid object recognition. Unlike other techniques which match each part individually, our approach exploits forcefully and efficiently enough the fact that the different rigid parts do belong to the same flexible molecule. We show experimental results obtained by an implementation of the algorithm for rigid and flexible docking. While the ‘correct’, crystal–bound complex is obtained with a small RMSD, additional, predictive ‘high scoring’ binding modes are generated as well. The diverse applications and implications of this general, powerful tool are discussed

Received on June 20, 1994; accepted on November 15, 1994

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