Susan - Stanford University

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Transcript Susan - Stanford University

ClusPro: an automated docking and
discrimination method for the prediction
of protein complexes
Stephen R. Comeau, David W.Gatchell, Sandor Vajda,
and Carlos J. Camacho
Bioinformatics Graduate Program and Department of
Biomedical Engineering, Boston University
Presentation by: Susan Tang
Background & Motivation
Docking = process of starting with a set of coordinates for two distinct
molecules and generating a model of the bound complex
Numerous methods which perform protein- protein docking exist today
Fourier correlation approach (Ritchie and Kemp, 2000) enabled the
generation of billions of possible docked conformation via defined scoring
Problem: Many false-positives (good surface complementarity) that are far
from the native complex
Motivation: Need to develop methods to filter and rank the docked
conformations such that near-native complexes can be identified
ClusPro: an automated, fast rigid-body docking and discrimination algorithm
1) Rapidly filters docked conformations
2) Ranks the conformations using clustering of computed pairwise RMSD
Input and Method Outline
2,000 docked
conformations for 48
receptor-ligand pairs
Free Energy
2,000 conformations
w/ low desolvation or
electrostatic energies
Via Clustering
Top 10 Clusters
Compare with
Native Structure
Part I: Free-Energy Filtering
Goal: to identify docked conformations having good surface
complementarity by selecting those w/ lowest desolvation and electrostatic
Surface complementarity is an important criteria due to the observation that
proteins tend to bury large surface areas after complex formation
Electrostatic and desolvation potentials (capturing the free energy of
association) are used independently since different binding mechanisms are
governed by different ratios of electrostatic/desolvation contributions
500 structures w/ lowest values of desolvation free energy retained
1500 structures w/lowest electrostatic energy retained
Electrostatics more sensitive to small coordinate perturbations  noisy
Cannot combine desolvation and electrostatics due to the noisy behavior of
electrostatics potential
Part II: Clustering based on Pairwise RMSD
By examining free energy landscapes of partially solvated receptor-ligand
complexes: native binding site is expected to be characterized by a local minima
having greatest width
In other words, the most probable conformation is expected to be surrounded by
lots of other low-energy conformations
Goal: to use a hierarchical clustering method to select and rank docked
conformations having the most “neighbors” given a defined cluster radius (in terms
of C-alpha RMSD)
Need to define fixed molecule (receptor) and flexible molecule (ligand)
Define a set of relevant ligand residues to be within 10 Angs of any atom in receptor
For each docked conformation X, calculate its pairwise ligand RMSD with 1999 other
Pairwise ligand RMSD = deviations between coordinates of X’s defined set of
ligand residues and corresponding coordinates of another conformation
Cluster the set of 2000 docked conformations using a 2000 by 2000 matrix of RMSD
values, and a cluster radius constraint of 9 Angs RMSD from the center
Pick largest cluster  rank cluster center  remove conformations within this
cluster from matrix
Pick next largest cluster -> rank cluster center  remove conformations within this
cluster from matrix  keep iterating until matrix is empty
Result I:
• Tested the discrimination step of the method on a benchmark set of 48
interacting protein pairs (2000 docked conformations each)
• In 31/48 protein pairs, top 10 predictions include at least one near-native
complex (average RMSD of 5 angs from native structure)
Result II:
- Tested method in the CAPRI (Critical Assessment of Predictions of
Interactions) experiment and generated predictions for 9 target complexes
- Round 3 (automated server): ClusPro prediction ranked as #3 for Target 8
ClusPro Web Server
User Input: PDB files of the 2 protein structures that user would like to
analyze in terms complex formation
Output: 10 (default) top predictions of docked conformations closest to
native structure
First, docking of the 2 proteins is performed using 2 established FFT-based
docking programs (DOT and ZDOCK)
Then, filtering and discrimination is performed
Server allows for customization of parameters:
– Clustering radius
Smaller protein  smaller radius maybe more suitable
– Relative number of desolvation and electrostatic best hits used during
– Number of predictions to generate (1-30)
Protein Drug Discovery
Although small molecule drugs are more prevalent therapeutics in current drug
discovery, protein drugs is a rapidly growing area in pharmaceuticals
It is true that protein therapeutics can be much more costly (in terms of R&D and
synthesis) than small-molecule therapeutics, but protein therapeutics can deliver
biological mechanisms that are not possible with small-molecule therapeutics
Multiple blockbuster protein drugs are currently on the market
Conservative estimation: there exist between 3,000 and 10,000 possible drug
Many of these new targets offer great opportunities for the development of protein
In 2002, drug companies sold nearly $33 billion in protein drugs
Rising at an average annual growth rate (AAGR) of 12.2%, this market is expected
to reach $71 billion in 2008.
Examples of popular classes of drug targets:
1) G-protein-coupled receptors
Compounds will be screened for their ability to inhibit (antagonist) or stimulate
(agonist) the receptor
2) Protein kinases
Compounds will be screened for their ability to inhibit the kinase
Application to Protein Drug Discovery
Ideal Drug: demonstrate high specificity and high affinity for the target protein
In order to evaluate the affinity of the potential drug with the target, you must first
predict what the binding interface looks like, and the relative positions of the potential
drug and target
ClusPro is the first integrated automated server that incorporates both docking and
discrimination steps for structural predictions of protein-protein complexes
Using ClusPro, one can generate many relative orientation/conformations of the 2
proteins  filter using desolvation + electrostatics potentials  discriminate via
clustering  find the best fit (closest to native structure from x-ray crystallography
results) between the 2 proteins
Top ranked predictions of ClusPro  further manual refinement and discrimination using
existing biochemical constraints and analysis to eliminate false positives  test binding
affinity of promising protein pairs in vitro  lead compounds used as starting points for
drug development/optimization
Can use ClusPro to screen databases of various existing, recombinant, or de novo
proteins for their interaction to a protein target of interest
ClusPro can be used to predict either:
– How a protein drug may bind (either inhibit or stimulate) a receptor
– How 2 proteins bind, and based on the structural details of the interaction 
design/screen for a drug that can inhibit that interaction