Docking and Design with AutoDock. David S. Goodsell Arthur J. Olson The Scripps Research Institute

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1 Docking and Design with AutoDock David S. Goodsell Arthur J. Olson The Scripps Research Institute

2 Rapid automated docking using: Grid-based energy evaluation Torsion-only conformation search

3 AutoDock History 1990: AutoDock is the first software to dock flexible ligands Freely distributed to academics 1992: AutoDock 2 rewritten in C User interface and manual 1997: AutoDock 3 collaboration with Rik Belew Lamarkian GA and empirical free energy force field AutoDockTools GUI 2000: First docking software to be used in a public distributed computing project (FightAIDS@Home, Entropia) 2007: AutoDock 4 distributed under open source license

4 AutoDock 4 Improved Force Field Based on comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding Uses charge-based method for evaluation of desolvation for typical set of atom types Calibrated against 188 diverse protein ligand complexes, tested on a set of 100 complexes of ligands with retroviral proteases. Improved performance over AutoDock3 forcefield Enables incorporation of flexible protein side-chains Implemented in FightAIDS@Home on IBM World Community Grid (>900,000 processors) AutoDockTools GUI for setup, running, and analysis

5 Citations of Docking Software to 2005 AutoDock Sousa, Fernandes &Ramos (2006) Proteins 65, 15.

6 Trends in Docking Software Usage AutoDock

7 AutoDock Licenses AutoDock 3 ( ) ~4,000 licenses AutoDock 4 ( ) under GPL ~10,000 click through licenses-- unique IPs

8 Current Topics for AutoDock Protein Flexibility Coevolution at Atomic Detail Identification of Optimal Binding Sites Improved Free Energy Prediction

9 Addressing Protein Flexibility with the Relaxed Complex Method Molecular dynamics is used to sample the conformational flexibility of the protein Representative snapshots are used in AutoDock to incorporate the protein flexibility during docking 1) Lin, J.-H.*, Perryman, A.L., Schames, J.R., & McCammon, J.A. J. Am. Chem. Soc., 124: 5632 (2002). 2) Lin, J.-H.*, Perryman, A.L., Schames, J.R., & McCammon, J.A. Biopolymers, 68: 47 (2003).

10 Re-examination of False Negatives from Docking using Relaxed Complex Method Experimental assays of NCI diversity set found 9 hits that were missed in the FAAH screen. Original AutoDock results on lowest energy from 77 crystal structures Relaxed Complex 20ns equilibrated MD on 1kzk structure, docked against snapshots every 10th ps (2000 snapshots) Work by Alex Perryman

11 Re-Analysis of FAAH Results Against 77 Xtal Structures: Reduction in False Negative Results Due to Clustering the Runs against Each Crystal Structure Ligand Ave. of Best E s Initial Best E of Largest New All 77Xtals Classification Cluster Classification False Hit False Hit False Hit False False False Hit False False False False False Hit False Hit Used for 1st ranking Ligand name = NSC numbers All values = Estimated Free Energy of Binding [kcal/mol] from AutoDock 4.0 s Scoring Function Clustering was accomplished by automating Ruth Huey s summarize_results.py script (which is distributed with MGL Tools)

12 New Relaxed Complex Results from FAAH: Enhanced Reduction in False Negative Results Due to the Relaxed Complex Method and Clustering the Runs Ligand Ave. of Best E s Initial RC method: Best E New All 77Xtals Classification Biggest Clusters Classification False Hit False Hit False Hit False Hit False Hit False Hit False Hit False Hit False Hit Used for 1st ranking Ligand name = NSC numbers All values = Estimated Free Energy of Binding [kcal/mol] from AutoDock 4.0 s Scoring Function RC references: 1) Lin, J.H., Perryman, A.L., Schames, J.R., and McCammon, J.A. J. American Chemical Society (Communication), 124 (20): (2002). 2) Lin, J.H., Perryman, A.L., Schames, J.R., and McCammon, J.A. Biopolymers, 68 (1): (Peter Kollman memorial issue, 2003). 3) Perryman, A.L., Lin, J.H., and McCammon, J.A. Chemical Biology & Drug Design, 67(5): (2006).

13 Relaxed complex method can also reveal new interactions: HIV protease exosite

14 Co-Evolution at Atomic Detail Examining the coevolution of drug development and viral mutation in HIV protease Panel of 170 HIV protease mutants Library of 1900 compounds from the National Cancer Institute Available Compound Diversity Set + positive controls FightAIDS@Home running on IBM s World Community Grid

15 Raw binding data Ligands Proteases Work by Max Chang and Richard K. Belew

16 Multi-Dimensional (Sammon) Plot spanning mutants and best representative mutant

17 Representative (spanning) Protease Structures

18 Optimal Binding Site discovery through Affinity Mapping Find optimal binding sites in proteins for a given ligand volume Uses affinity maps from AutoDock/AutoGrid Use for finding binding sites, optimal ligand characteristics, exosites, drug optimization, characterizing proteins of unknown function

19 AutoLigand vs. isocontour Contiguous envelope of maximal affinity Often includes lower affinity connections Includes all atom types

20 Algorithmic Approach Fill initial volume flood Find local optimum energies (seed points) Evaluate neighbor grid points add best to list Continue until specified volume is reached Volume migration Compare best neighbor point to worst volume point and if better add new point and remove old if it retains contiguity Continue until no improvement Neighborhood exploration Evaluate lines of points (up to 10) from volume, accept and replace from volume if sum is better Repeat migration and neighborhood search until no improvement Work by Rodney Harris

21 Myc/Max inhibition Albert Beuscher with Vogt Lab Inhibit Myc function by stabilizing Max/Max dimers Find potential binding sites Distinguish unique Max/Max sites VLS against sites with NCI diversity set Tested with FRET and cell based Assays

22 Promising Lead Compound Site 3 Binder FRET shows preferential stability of Max homodimer interferes with Myc-induced oncogenic transformation Myc-dependent cell growth Myc-mediated transcriptional activation

23 Problem of Ranking Docked Results Often find incorrect conformations with slightly more favorable binding energies Found with low frequency in iterated docking (~1%) The correct conformations found more frequently (25-100%) Choosing best energy conformation can give wrong conformation Current force field poorly predicts weakly interacting ligands Eg. APS reductase (adenosine 5 phosphosulfate reductase) Experimentally 5 ADP strong, 3 ADP weak AutoDock predicts both as comparably strong binders

24 2Å RMSD Clustering for 100 dockings to APS reductase 5 AMP ΔG obs = -8.07kcal/mol ΔG AD4 = -8.73kcal/mol n = 61/100 3 AMP ΔG obs = -2.27kcal/mol ΔG AD4 = -9.25kcal/mol n = 1/100

25 Clustering and configurational entropy Hypothesis Frequency of occurrence of a conformation provides information on the energy landscape High frequency indicates more favorable entropy of binding Rosenfeld and Olson (J.Comp. Chem 2003) Distinguished binders from non-binders using clustering Ruvinsky and Kozintsev (J.Comp.Chem, 2005) Evaluate conformational space spanned within clusters Colony Method (Honig, 2002) -- protein loop prediction Energy based upon RMSD from randomly generated conformations to all others Cluster Size Method (Chang et al., J. Comp. Chem. 2008)

26 Local Energy Landscapes Predicted Binding Energy 5 AMP shows wide basin Few bad contacts <0.5Å 3 AMP Narrow basin Bad contacts <0.25Å RMSD

27 Results of Rescoring (RMSD of best scoring from actual structure) lowest best fit best fit lowest AD4energy wrmsd RMSD RK RK Colony Vb RMSD 5'AMP deazaAMP 'ADP 'deoxyAMP 'PMP NmethylAMP aminoAMP aminoAMP phosphoAMP methoxyAMP bmethaps 'deoxyAMP adenosine dimethylamp 'IMP 'deoxyadenosine 'phosphoribose 'AMP 'deoxyadenosine ribose adenine 'IDP /22

28 Acknowledgements TSRI Arthur Olson Garrett Morris Ruth Huey Alex Perryman Stephano Forli Alex Gillet Albert Beuscher Rodney Harris UCSD Rik Belew Max Chang