Transcript AutoDock Tutorial

Using
AutoDock With
AutoDockTools:
A Tutorial
Ruth Huey
&
Garrett M. Morris
7/15/2015
AutoDock & ADT Tutorial
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What is Docking?
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“Best ways to put two molecules
together.”
Three steps:
(1) Definition of the structure of the
target molecule.
(2) Location of the binding site.
(3) Determination of the binding
mode.
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What is Docking?

“Best ways to put two molecules
together.”
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“Best ways to put two molecules
together.”
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(plural) Experimental structure may be
amongst one of several predicted
solutions.
“Best ways to put two molecules
together.”
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Need to quantify or rank solutions;
Scoring function or force field.
Need a Search method
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Questions…
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Search
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Scoring
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What is it?
Dimensionality
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What is it?
When/why and which search?
Why is this important?
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Spectrum of Search
Breadth and Detail
Search Breadth
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Local
 Molecular
Mechanics
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Short - Medium
 Monte Carlo
Simulated
Annealing
 Brownian
Dynamics
 Molecular
Dynamics

Global
 Docking
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Level-of-Detail
 Atom types
 Terms of force field
 Bond stretching
 Bond-angle
bending
 Torsional
potentials
 Polarizability
terms
 Implicit solvation
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Two Kinds of
Search
Systematic
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Exhaustive.
Deterministic.
Dependent on
granularity of
sampling.
Feasible only for lowdimensional
problems
DOT, 6D search.
AutoDock & ADT Tutorial
Stochastic
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Random.
Outcome varies.
Repeat to improve
chances of success
Feasible for higherdimensional
problems
AutoDock, < ~40D
search.
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Stochastic Search
Methods
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Simulated Annealing (SA)*
Evolutionary Algorithms (EA)
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Others
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Genetic Algorithm (GA)*
Tabu Search (TS)
Hybrid Global-Local Search

Lamarckian GA (LGA)*
*In AutoDock
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How an SA works…
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One copy of the ligand (Population = 1)
Starts from a random or specific
postion/orientation/conformation (=state)
Constant temperature annealing cycle
(Accepted & Rejected Moves)
Temperature reduced before next cycle
Stops at maximum cycles
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How a GA Works…
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Start with a random population (50-200)
Perform Crossover (Sex, two parents > 2 children) and Mutation (Cosmic
rays, one individual gives 1 mutant
child)
Compute fitness of each individual
Proportional Selection & Elitism
New Generation begins if total energy
evals or maximum generations reached
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Search Parameters
Simulated Annealing
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Initial temperature
(K)
Temperature
reduction factor (K1cycle)
Termination criteria:
 accepted moves
 rejected moves
 cycles
AutoDock & ADT Tutorial
Genetic Algorithm &
Lamarckian GA
 Population size
 Crossover rate
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Mutation rate
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Local search
 energy evals
Termination criteria:
 energy evals
 generations
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AutoDock 3
Scoring Function
DGbinding = DGvdW + DGelec + DGhbond +
DGdesolv + DGtors
•
•
•
•
•
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DGvdW
12-6 Lennard-Jones potential
DGelec
Coulombic with Solmajer-dielectric
DGhbond
12-10 Potential with Goodford Directionality
DGdesolv
Stouten Pairwise Atomic Solvation Parameters
DGtors
Number of rotatable bonds
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AutoDock
Atom Types
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Atom type codes
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Small number of types:
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CNOSHXM
N with 12-10 can accept H-bonds
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Protein/Macromolecule types:
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First character of atom name
Use ‘n’ with 12-6 for “N-H”
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Dimensionality of
Molecular Docking
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Degrees of Freedom (DOF)
Position or Translation
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Orientation or Quaternion
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(qx, qy, qz, qw) = 4
Rotatable Bonds or Torsions
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(x,y,z) = 3
(tor1, tor2, … torn) = n
Total DOF, or Dimensionality,
D=3+4+n
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Multidimensional
Treasure Hunt…
Dimensions
Landscape
Divide into 2
Treasure
Chances?
$
0.5
1
¥
2
£
3
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AutoDock & ADT Tutorial
0.25
0.125
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Sampling
Hyperspace
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Say we are hunting in D-dimensional
hyperspace…
We want to sample each of the D
dimensions N times.
The number of “evals” needed, e, is:
e = ND
\ N = e1/D
 For example,
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e=106, D=6, N=10.0 samples
e=106, D=36, N=~1.5 samples
More dimensions, tougher it gets.
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Next, AutoDock…
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Now for some specifics about
AutoDock…
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AutoDock
Introduction
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Automated docking of flexible ligands to
proteins.
Global search algorithms:
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Local search algorithm:
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Solis & Wets (Morris et al. 1998)
Hybrid global-local search algorithm:
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Simulated Annealing (Goodsell et al. 1990)
Distributed SA (Morris et al. 1996)
Genetic Algorithm (Morris et al. 1998)
Lamarckian GA (Morris et al. 1998)
Empirical free energy function
estimates Ki (Std. dev. ~2 Kcal mol-1)
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AutoDock Software
AutoDock & AutoGrid
AutoDockTools/ADT
1990
2000
Number crunching
Visualizing, set-up
Command-line.
awk, shell & python scripts.
Text editors
GUI, PMV, \ Python;
front- & back-end.
GUI-less, self-logging &
rescriptable
Python, interpreted
C & C++, compiled
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AutoDock Citations
500
450
400
# Citations
350
300
250
200
150
100
50
0
NIH/NIGMS
P01
GM48870
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1991 1992 1993 19
94
1995 1996 1997 19
98
Year
All Cit ations
Goodsell 199 0
Morris 1996
Morris 1998
1999 2000 2001 20
02
Source: ISI Science Citation Index
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AutoDock User Licenses
2000
1500
1000
500
# Ftp Requests
2500
P01
GM48870
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2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
NIH/NIGMS
1991
C um NA V
1992
0
Year
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Docking & AutoDock
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What problem does AutoDock solve?
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Flexible ligands (4.0 flexible protein).
What range of problems is feasible?
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Depends on the search method:
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When is AutoDock not suitable?
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LGA > GA >> SA >> LS
SA : trajectories, D = ~8 torsions.
LGA : D = ~20-30 torsions.
No 3D structures available;
Modelled structure of poor quality;
Too many (torsions, atoms, types);
Target protein too flexible.
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Flexible Docking
Step-By-Step
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Set up the ligand & macromolecule.
Pre-compute AutoGrid Maps for all atom
types in your set of ligands.
Perform dockings of ligand to target
using AutoDock, in parallel if possible.
Cluster dockings.
Visualize results.
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Things you need to
do before AutoDock
Ligand:
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Add all hydrogens, compute Gasteiger
charges, and unite non-polar H
Distinguish aliphatic and aromatic
carbons
Choose root & rotatable bonds
Macromolecule:
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Add polar H, assign Kollman charges
Assign Stouten solvation parameters
Compute AutoGrid maps
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AutoGrid:
Why Use Grid Maps?
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AutoGrid computes grid maps
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Representation of macromolecule
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Ligand ‘probe’ samples force field
One map for each ligand atom type
AutoDock uses trilinear interpolation
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Regular orthogonal lattice of points
to compute interaction energy between
ligand and target
Non-bonded energy is pre-calculated
Saves time: ~100x faster than traditional
NB-list method
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AutoGrid Grid Box
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Grid box depends on:
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Orientation with respect to protein.
Where should I center the grid box?
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Center on ligand;
Center on macromolecule;
Pick atom;
Type in x-, y- and z-coordinates.
Spacing (0.2Å-1.0Å: default 0.375Å).
Specify an Even Number of x-, y-, zpoints (222 - 126126126).
% makebox mol.gpf > mol.gpf.box.pdb
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Modeling Hydrogens
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AutoDock uses ‘United Atom’ model
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Why? Reduces number of atoms
Need to:
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Add polar Hs. Remove non-polar Hs.
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Replace missing atoms (disorder).
Fix hydrogens at chain breaks.
Need to consider pH:
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Acidic & Basic residues, Histidines.
Other molecules in receptor:
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Both Ligand & Macromolecule
Waters; Cofactors; Metal ions.
Molecular Modelling elsewhere.
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Partial Charges
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Partial Atomic Charges:
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Peptides & Proteins; DNA & RNA
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Organic compounds; Cofactors
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Gasteiger (PEOE);
MOPAC (MNDO, AM1, PM3);
Gaussian (6-31G*).
Integer total charge per residue.
Non-polar hydrogens:
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Always merge
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Kollman United Atom (Table);
(unless using All Atom,‘H’ & ‘h’);
Can override &merge just selected H
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Carbons
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Solvation Free Energy
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Need to rename ligand aromatic ‘C’ to ‘A’.
ADT determines if ligand is a peptide:
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Based on Stouten method.
Treats aliphatic (‘C’) and aromatic (‘A’)
carbons differently.
If so, uses a look-up dictionary.
If not, inspects geometry of ‘C’s in rings.
Renames ‘C’ to ‘A’ if flat enough.
Can adjust ‘flatness’ criterion (15° detects
more rings than default 7.5°).
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Ligand Flexibility
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Set Root of Torsion Tree:
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By interactively picking, or
Automatically.
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Interactively Pick Rotatable Bonds:
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No ‘leaves’;
No bonds in rings;
Can freeze:
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Smallest ‘largest sub-tree’.
Peptide/amide/selected/all;
Can set the number of active torsions
that move either the most or the fewest
atoms
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AutoDock File
Formats
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Prepare 4 Input Files
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Run AutoGrid
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Needs: PDBQS, GPF (depends on PDBQ)
Makes: grid maps
Run AutoDock
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Ligand - PDBQ
Macromolecule - PDBQS
AutoGrid Parameter File - GPF
AutoDock Parameter File - DPF
Needs: maps, PDBQ, DPF
Performs: dockings & clustering
Analyze Results
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Setting Up Your
Environment
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At TSRI:
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Modify .cshrc
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% cd tutorial
% adt1
Web Addresses
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% source /tsri/python/share/bin/initadtcsh
To start AutoDockTools, type:
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setenv PATH (/mgl/apps/bin/$archosv:/tsri/python:$path)
% limit stacksize unlimited
ADT Tutorial, every time you open a Shell or Terminal,
type:
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Change PATH & stacksize:
www.scripps.edu/pub/olson-web/doc/autodock/
www.scripps.edu/~sanner/python/
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Choose the Docking
Algorithm
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SA.dpf -> Simulated Annealing
GA.dpf -> Genetic Algorithm
LS.dpf -> Local Search
Solis-Wets (SW)
 Pseudo Solis-Wets (pSW)
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GALS.dpf -> Genetic Algorithm
with Local Search, i.e. Lamarckian
GA
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Run AutoGrid
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Check: Enough disk space?
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Maps are ASCII, but can be ~2-8MB !
Start AutoGrid from the Shell:
% autogrid3 –p mol.gpf –l mol.glg &
% autogrid3 -p mol.gpf -l mol.glg ; autodock3 -p mol.dpf -l mol.dlg
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Follow the log file using:
% tail -f mol.glg
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Type <Ctrl>-C to break out of the ‘tail
-f’ command
Wait for “Successful Completion” before
starting AutoDock
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Run AutoDock
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Do a test docking, ~ 20 000 evals
Do a full docking, if test is OK, ~
250 000 to 50 000 000 evals
From the Shell:
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% autodock3 –p yourFile.dpf –l yourFile.dlg &
Expected time? Size of docking log?
Distributed computation
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At TSRI, SGI PowerChallenge Cluster
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% submit.py stem 20
% recluster.py stem 20 during 3.5
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Analyzing AutoDock
Results
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In ADT, you can:
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Read & view a single DLG, or
Read & view many DLG results
files in a single directory
Re-cluster docking results by
conformation & view these
Outside ADT, you can re-cluster
several DLGs
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Useful in distributed docking
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% recluster.py stem 20 [during|end] 3.5
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Viewing Conformational
Clusters by RMSD
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List of available RMSD tolerances
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Histogram of conformational clusters
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Number in cluster versus energy
Pick a cluster
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Separated by spaces
makes a list of the conformations in that
cluster;
makes these the current sequence for
states player.
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Acknowledgments
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7/15/2015
Art J. Olson
David S.
Goodsell
Michel Sanner
William
Lindstrom
Sophie Coon
Daniel Stoffler
AutoDock & ADT Tutorial
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Rik Belew (UCSD)
Bill Hart (Sandia)
Scott Halliday
Chris Rosin

Flavio Grynszpan
(TSRI)
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Many patient ADT
users
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