1. Protein-Protein and Protein-Ligand Docking Abhijeet Kadam
TSEC BioTechnology
2. Defination Given two biological molecules determine: Whether
the two molecules interact If so, what is the orientation that
maximizes the interaction while minimizing the total energy of the
complex Goal: To be able to search a database of molecular
structures and retrieve all molecules that can interact with the
query structure
3. Example: HIV-1 Protease Docking to find drug candidates
Active Site (Aspartyl groups)
4. Difficulty in docking Number of possible conformations are
astronomical thousands of degrees of freedom (DOF) Free energy
changes are small Below the accuracy of our energy functions
Molecules are flexible alter each others structure as they
interact
5. Types of Docking studies Protein-Protein Docking Both
molecules usually considered rigid 6 degrees of freedom First apply
steric constraints to limit search space and Next examine
energetics of possible binding conformations Protein-Ligand Docking
Flexible ligand, rigid-receptor Search space much larger Either
reduce flexible ligand to rigid fragments connected by one or
several hinges, or search the conformational space using
monte-carlo methods or molecular dynamics
6. Techniques of docking Surface representation, that
efficiently represents the docking surface and identifies the
regions of interest (cavities and protrusions) Connolly surface
Lenhoff technique Kuntz et al. Clustered-Spheres Alpha shapes
Surface matching that matches surfaces to optimize a binding score:
Geometric Hashing
7. Surface Representation Each atomic sphere is given the van
der Waals radius of the atom Rolling a Probe Sphere over the Van
der Waals Surface leads to the Solvent Reentrant Surface or
Connolly surface
8. Lenhoff technique Computes a complementary surface for the
receptor instead of the Connolly surface, i.e. computes possible
positions for the atom centers of the ligand Atom centers of the
ligand van der Waals surface
9. Kuntz et al. Clustered-Spheres Uses clustered-spheres to
identify cavities on the receptor and protrusions on the ligand
Compute a sphere for every pair of surface points, i and j, with
the sphere center on the normal from point i Regions where many
spheres overlap are either cavities (on the receptor) or
protrusions (on the ligand)
10. Alpha Shapes Formalizes the idea of shape In 2D an edge
between two points is alpha-exposed if there exists a circle of
radius alpha such that the two points lie on the surface of the
circle and the circle contains no other points from the point
set
11. Alpha Shapes: Example Alpha=infinity Alpha=3.0
12. Surface Matching Find the transformation (rotation +
translation) that will maximize the number of matching surface
points from the receptor and the ligand First satisfy steric
constraints Find the best fit of the receptor and ligand using only
geometrical constraints then use energy calculations to refine the
docking Select the fit that has the minimum energy
13. Geometric Hashing Building the Hash Table: For each triplet
of points from the ligand, generate a unique system of reference
Store the position and orientation of all remaining points in this
coordinate system in the Hash Table Searching in the Hash Table
Determine those entries that received more than a threshold of
votes, such entry corresponds to a potential match For each
potential match recover the transformation T that results in the
best least-squares match between all corresponding triplets
Transform the features of the model according to the recovered
transformation T and verify it. If the verification fails, choose a
different receptor triplet and repeat the searching.
14. Example Docking Programs DOCK (I. D. Kuntz, UCSF) AutoDOCK
(A. Olson, Scripps) RosettaDOCK (Baker, U Wash., Gray, JHU)
15. DOCK DOCK works in 5 steps: Step 1Step 1 Start with
coordinates of target receptor Step 2 Generate molecular surface
for receptor Step 3 Fill active site of receptor with spheres
potential locations for ligand atoms Step 4 Match sphere centers to
ligand atoms determines possible orientations for the ligand Step 5
Find the top scoring orientation
16. Other Docking programs AutoDock designed to dock flexible
ligands into receptor binding sites Has a range of powerful
optimization algorithms RosettaDOCK models physical forces Creates
a large number of decoys degeneracy after clustering is final
criterion in selection of decoys to output
17. A Protein-Protein Docking Algorithm (Gray & Baker)
Goal: to predict protein-protein complexes from the coordinates of
unbound monomer components. Two steps: A - low-resolution Monte
Carlo search and B - final optimization using Monte Carlo
minimization. Up to 105 independent simulations produce decoys that
are ranked using an energy function. The top-ranking decoys are
clustered for output.
18. Docking protocol
19. Docking protocol: Step 1 RANDOM START POSITIONRANDOM START
POSITION Creation of a decoy begins with a random orientation of
each partner and a translation of one partner along the line of
protein centers to create a glancing contact between the
proteins
20. Docking protocol: Step 2 LOW-RESOLUTION MONTE CARLO
SEARCHLOW-RESOLUTION MONTE CARLO SEARCH Low-resolution
representation: N, C , C, O for the backbone and a centroid for the
side-chain One partner is translated and rotated around the surface
of the other through 500 Monte Carlo move attempts The score terms:
A reward for contacting residues, a penalty for overlapping
residues, an alignment score, residue environment and
residue-residue interactions
21. Docking protocol: Step 3 HIGH-RESOLUTION
REFINEMENTHIGH-RESOLUTION REFINEMENT Explicit side-chains are added
to the protein backbones using a rotamer packing algorithm, thus
changing the energy surface An explicit minimization finds the
nearest local minimum accessible via rigid body translation and
rotation Start and Finish positions are compared by the Metropolis
criterion
22. Docking protocol: Step 3 Before each cycle, the position of
one protein is perturbed by random translations and by random
rotations To simultaneously optimize the side-chain conformations
and the rigid body position, the side-chain packing and the
minimization operations are repeated 50 times
23. Docking protocol: Step 3 COMPUTATIONAL EFFICIENCY The
packing algorithm usually varies the conformation of one residue at
a time; rotamer optimization is performed once every eight cycles
Periodically filter to detect and reject inferior decoys without
further refinement
24. Docking protocol: Step 4 CLUSTERING &
PREDICTIONSCLUSTERING & PREDICTIONS Repeat search to create
approximately 105 decoys per target Cluster best 200 decoys by a
hierarchical clustering algorithm using RMSD The clusters with the
most members become predictions, ranked by cluster size
25. Docking protocol: Results
26. Problems in Docking The computational molecular docking
problem is far from being solved. There are two major bottle-necks:
The algorithms handle limited flexibility Need selective and
efficient scoring functions