Protein Docking and Interactions Modeling

1
Protein Docking and Interactions Modeling

• CS 374
• Maria Teresa Gil Lucientes
• November 4, 2004

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Overview of the lecture

• Introduction to molecular docking
• Definition
• Types
• Some techniques
• Programs
• Algorithm for Protein-Protein docking based in
  paper
• Protein-Protein Docking with Simultaneous
  Optimization of Rigid-body Displacement and
  Side-chain Conformations
• Jeffrey J. Gray, Stewart Moughon, Chu Wang, Ora
  Schueler-Furman
• Brian Kuhlman, Carol A. Rohl and David Baker
• J. Mol. Biol. (2003) 331, 281-299

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What is Docking?
Docking attempts to find the best matching
between two molecules
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a more serious definition

• 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

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Why is docking important?

• It is of extreme relevance in cellular biology,
  where function is accomplished by proteins
  interacting with themselves and with other
  molecular components
• It is the key to rational drug design The
  results of docking can be used to find inhibitors
  for specific target proteins and thus to design
  new drugs. It is gaining importance as the number
  of proteins whose structure is known increases

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Example HIV-1 Protease
Active Site (Aspartyl groups)
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Example HIV-1 Protease
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Why is this difficult?

• Both molecules are flexible and may alter each
  others structure as they interact
• Hundreds to thousands of degrees of freedom (DOF)
• Total possible conformations are astronomical

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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 the 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

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Some techniques

• 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

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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

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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
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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)

j
i
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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

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Alpha Shapes Example
Alphainfinity
Alpha3.0 Å
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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
• Selet the fit that has the minimum energy

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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
• For each triplet of points from the receptor,
  generate a unique system of reference
• Search the coordinates for each remaining point
  in the receptor and find the appropriate hash
  table bin For every entry there, vote for the
  basis

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Geometric Hashing

• 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.

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Docking Programs

• More information in http//www.bmm.icnet.uk/smit
  hgr/soft.html
• The programs are
• DOCK (I. D. Kuntz, UCSF)
• AutoDOCK (Arthur Olson, The Scripps Research
  Institute)
• RosettaDOCK (Baker, Washington Univ., Gray, Johns
  Hopkins Univ.)

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DOCK

• DOCK works in 5 steps
• Step 1 Start with crystal coordinates of target
  receptor
• Step 2 Generate molecular surface for receptor
• Step 3 Generate spheres to fill the active site
  of the receptor The spheres become potential
  locations for ligand atoms
• Step 4 Matching Sphere centers are then matched
  to the ligand atoms, to determine possible
  orientations for the ligand
• Step 5 Scoring Find the top scoring orientation

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DOCK Example
1
2

• HIV-1 protease is
• the target receptor
• Aspartyl groups are
• its active side

3
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DOCK

• DOCK works in 5 steps
• Step 1 Start with crystal coordinates of target
  receptor
• Step 2 Generate molecular surface for receptor
• Step 3 Generate spheres to fill the active site
  of the receptor The spheres become potential
  locations for ligand atoms
• Step 4 Matching Sphere centers are then matched
  to the ligand atoms, to determine possible
  orientations for the ligand
• Step 5 Scoring Find the top scoring orientation

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DOCK Example
1
2

• HIV-1 protease is
• the target receptor
• Aspartyl groups are
• its active side

3
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DOCK

• DOCK works in 5 steps
• Step 1 Start with crystal coordinates of target
  receptor
• Step 2 Generate molecular surface for receptor
• Step 3 Generate spheres to fill the active site
  of the receptor The spheres become potential
  locations for ligand atoms
• Step 4 Matching Sphere centers are then matched
  to the ligand atoms, to determine possible
  orientations for the ligand
• Step 5 Scoring Find the top scoring orientation

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DOCK Example
1
2

• HIV-1 protease is
• the target receptor
• Aspartyl groups are
• its active side

3
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DOCK

• DOCK works in 5 steps
• Step 1 Start with crystal coordinates of target
  receptor
• Step 2 Generate molecular surface for receptor
• Step 3 Generate spheres to fill the active site
  of the receptor The spheres become potential
  locations for ligand atoms
• Step 4 Matching Sphere centers are then matched
  to the ligand atoms, to determine possible
  orientations for the ligand
• Step 5 Scoring Find the top scoring orientation

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DOCK Example
4
5

• Three scoring schemes Shape scoring,
  Electrostatic scoring
• and Force-field scoring
• Image 5 is a comparison of the top scoring
  orientation of the
• molecule thioketal with the orientation found
  in the crystal
• structure

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DOCK

• DOCK works in 5 steps
• Step 1 Start with crystal coordinates of target
  receptor
• Step 2 Generate molecular surface for receptor
• Step 3 Generate spheres to fill the active site
  of the receptor The spheres become potential
  locations for ligand atoms
• Step 4 Matching Sphere centers are then matched
  to the ligand atoms, to determine possible
  orientations for the ligand
• Step 5 Scoring Find the top scoring orientation

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DOCK Example
4
5

• Three scoring schemes Shape scoring,
  Electrostatic scoring
• and Force-field scoring
• Image 5 is a comparison of the top scoring
  orientation of the
• molecule thioketal with the orientation found
  in the crystal
• structure

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Other Docking programs

• AutoDock
• AutoDock was designed to dock flexible ligands
  into receptor binding sites
• The strongest feature of AutoDock is the range of
  powerful optimization algorithms available
• RosettaDOCK
• It models physical forces and creates a very
  large number of decoys
• It uses degeneracy after clustering as a final
  criterion in decoy selection

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CAPRI Challenge (2002)
The 7 CAPRI Docking Targets

• At least one docking partner presented in its
  unbound form
• Participants permitted 5 attempts for each
  target

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CAPRI Challenge
Participants Algorithms
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Results CAPRI Challenge
This were the results for the different
predictors and targets
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A Protein-Protein Docking Algorithm (Gray Baker)

• Our goal is to try to predict protein-protein
  complexes from the coordinates of the unbound
  monomer components.
• The method is divided in two steps A
  low-resolution Monte Carlo search and a final
  optimization using Monte Carlo minimization.
• Up to 105 independent simulations are carried
  out, and the resulting decoys are ranked using
  an energy function.
• The top-ranking decoys are clustered to select
  the final predictions.

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Docking protocol
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Docking protocol Step 1

• RANDOM 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

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Docking protocol
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Docking protocol Step 2

• LOW-RESOLUTION MONTE CARLO SEARCH
• One partner is translated and rotated around the
  surface of the other through 500 Monte Carlo move
  attempts
• We use a low-resolution representation N, C?, C,
  O for the backbone and a centroid for the
  side-chain
• The score is based in the correctness of each
  decoy A reward contacting residues, a penalty
  for overlapping residues, an alignment score,
  residue environment and residue-residue
  interacions terms

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Docking protocol
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Docking protocol Step 3

• HIGH-RESOLUTION REFINEMENT
• Explicit side-chains are added to the protein
  backbones using a rotameter 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

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Docking protocol
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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

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Docking protocol Step 3

• COMPUTATIONAL EFFICIENCY
• The packing algorithm usually varies the
  conformation of only one residue at a time A
  combinatorial rotamer optimization is performed
  only once every eight cycles
• A filter is employed periodically to detect
  inferior decoys and and reject them without
  further refinement

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Docking protocol
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Docking protocol Step 4

• CLUSTERING PREDICTIONS
• The search procedure is repeated to create
  approximately 105 decoys per target
• The 200 best-scoring decoys are then clustered on
  the basis of the root-mean-squared distance
  (rmsd) using a hierarchical clustering algorithm
• The clusters with the most members are selected
  as the final predictions and ranked according to
  cluster sizes

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Docking protocol Results
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Conclusions

• The so-called computational molecular docking
  problem is far from being solved. There are two
  major bottle-necks
• The algorithms can handle only a limited extent
  of backbone flexibility
• The availability of selective and efficient
  scoring functions

and Thanks!!
Questions??