Protein-Ligand Docking Methods

Home , AutoDock, CHARMM, Docking (molecular)

Review Goal: • Given a protein structure, Protein-Ligand predict its ligand bindings Docking Methods Applications: • Function prediction • Drug discovery Thomas Funkhouser • etc. Princeton University CS597A, Fall 2007

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Review Protein-Ligand Docking Questions: Questions: • Where will the ligand bind? • Where will the ligand bind? • Which ligand will bind? • Which ligand will bind? • How will the ligand bind? • How will the ligand bind? • When? • When? • Why? • Why? • etc. • etc.

Ligand

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Protein-Ligand Docking Protein-Ligand Docking Goal 1: Goal 2: • Given a protein and a ligand, • Predict the binding affinity of any protein-ligand complex determine the pose(s) and conformation(s) minimizing the total energy of the protein-ligand complex Virtual Screening High Throughput (Computational Docking & Scoring) Screening

http://www.molsoft.com/

1 Protein-Ligand Docking Protein-Ligand Docking Challenges: Challenges: • Predicting energetics of protein-ligand binding Predicting energetics of protein-ligand binding • Searching space of possible poses & conformations • Searching space of possible poses & conformations

Protein-Ligand Docking Protein-Ligand Docking Challenges: Challenges: • Predicting energetics of protein-ligand binding • Predicting energetics of protein-ligand binding • Searching space of possible poses & conformations • Searching space of possible poses & conformations § Relative position (3 degrees of freedom) § Relative position (3 degrees of freedom) § Relative orientation (3 degrees of freedom) § Relative orientation (3 degrees of freedom) Rotatable bonds in ligand (n degrees of freedom) § Rotatable bonds in ligand (n degrees of freedom) Rotatable bonds in protein (m degrees of freedom)

Side chain movements Large-scale movements

2 Outline Outline Introduction Introduction Scoring functions Scoring functions • Molecular mechanics • Molecular mechanics • Empirical functions • Empirical functions • Knowledge-based • Knowledge-based Searching poses & conformations Searching poses & conformations • Systematic search • Systematic search • Molecular dynamics • Molecular dynamics • Simulated annealing • Simulated annealing • Genetic algorithms • Genetic algorithms • Incremental construction • Incremental construction • Rotamer libraries • Rotamer libraries Results & Discussion Results & Discussion

Scoring Functions Scoring Functions Goal: estimate binding affinity for given Goal: estimate binding affinity for given protein, ligand, pose, and conformations protein, ligand, pose, and conformations

HIV-1 protease complexed with an hydroxyethylene isostere inhibitor [Marsden04]

Scoring Functions Scoring Functions

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Binding Equations: Binding Equations: [R'L'] [R'L'] Ka K = K -1= Ka K = K -1= R + L R'L' a d [R][L] R + L R'L' a d [R][L] Kd Kd Free Energy Equations:

o o o o ∆G = -RTln(Ka) ∆G = ∆H - T∆S

3 Scoring Functions Scoring Functions

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Binding Equations: Binding Equations: [R'L'] [R'L'] Ka K = K -1= Ka K = K -1= R + L R'L' a d [R][L] R + L R'L' a d [R][L] Kd Kd Free Energy Equations: Free Energy Equations:

o o o o o o o o ∆G = -RTln(Ka) ∆G = ∆H - T∆S ∆G = -RTln(Ka) ∆G = ∆H - T∆S

-2 -12 Ka : 10 – 10 M enthalpy entropy ∆Go : -10 to -70 kcal/mol enthalpy entropy

Factors Affecting ∆Go Scoring Functions Intramolecular Forces Intermolecular Forces Molecular mechanics force fields: (covalent) (noncovalent) • CHARMM [Brooks83] • Bond lengths • Electrostatics • AMBER [Cornell95] • Bond angles • Dipolar interactions • Dihedral angles • Hydrogen bonding Empirical methods: • Hydrophobicity • ChemScore [Eldridge97] • van der Waals • GlideScore [Friesner04] • AutoDock [Morris98] Knowledge-based methods ∆H and ∆S work against each other in many situations. • PMF [Muegge99] • Bleep [Mitchell99] • DrugScore [Gohlke00]

Scoring Functions Molecular Mechanics Force Fields Molecular mechanics force fields: CHARMM: • CHARMM [Brooks83] • AMBER [Cornell95] Empirical methods: • ChemScore [Eldridge97] • GlideScore [Friesner04] • AutoDock [Morris98] Knowledge-based methods • PMF [Muegge99] • Bleep [Mitchell99] • DrugScore [Gohlke00]

[Brooks83]

4 Molecular Mechanics Force Fields Scoring Functions AMBER: Molecular mechanics force fields: • CHARMM [Brooks83] • AMBER [Cornell95] Empirical methods: • ChemScore [Eldridge97] • GlideScore [Friesner04] • AutoDock [Morris98] Knowledge-based methods • PMF [Muegge99] • Bleep [Mitchell99] • DrugScore [Gohlke00]

[Cornell95]

Empirical Scoring Functions Empirical Scoring Functions ChemScore: GlideScore:

∆ ∆α + ∆ ∆ + ∆ Chbond −neut−neut ƒ g( r)h( ) Gbind = G0 Gbind = ∆ ∆α + ∆ ∆ ∆α + C − − ƒ g( r)h( ) Ghbond ƒ g1( r)g2 ( ) hbond neut ch arg ed iI ∆ ∆α + Chbond −ch arg ed −ch arg ed ƒ g( r)h( ) ∆G f r + metal ƒ ( aM ) ∆α + aM Cmax −metal−ion ƒ f (rlm )h( ) ∆ + + Glipo ƒ f (riL ) Clipo−lipo ƒ f (rlr ) iL [Marsden04] C H + ∆ rotb rotb Grot H rot Cpolar− phobVpolar− phob + Ccoul Ecoul + CvdW EvdW solvationterms

[Eldridge97] [Friesner04]

Empirical Scoring Functions Scoring Functions AutoDock 3.0: Molecular mechanics force fields: • CHARMM [Brooks83] ∆ ∆ ∆ ∆ Gbinding = GvdW + Gelec + Ghbond + • AMBER [Cornell95] ∆ ∆ Gdesolv + Gtors ∆ Empirical methods: GvdW 12-6 Lennard-Jones potential • ChemScore [Eldridge97] ∆ Gelec • GlideScore [Friesner04] Coulombic with Solmajer-dielectric • AutoDock [Morris98] ∆ Ghbond 12-10 Potential with Goodford Directionality Knowledge-based methods ∆ Gdesolv Stouten Pairwise Atomic Solvation Parameters • PMF [Muegge99] ∆ Gtors • Bleep [Mitchell99] Number of rotatable bonds • DrugScore [Gohlke00]

[Huey & Morris, AutoDock & ADT Tutorial, 2005]

5 Knowledge-Based Scoring Functions Scoring Functions DrugScore: Molecular mechanics force fields: • CHARMM [Brooks83] »ƒ∆W (SAS,SAS ) +ÿ » ÿ … i 0 Ÿ • AMBER [Cornell95] ∆ = γ ∆ + −γ × ki W …ƒƒ Wi j (r)Ÿ (1 ) … Ÿ , ∆Wj SAS SAS … ki l j ⁄Ÿ …ƒ ( , 0 ) Ÿ Empirical methods: l j ⁄ • ChemScore [Eldridge97] How to compute • GlideScore [Friesner04] score efficiently? Protein-Ligand Solvent Atom Accessible • AutoDock [Morris98] Distance Surface Term Terms Knowledge-based methods • PMF [Muegge99] • Bleep [Mitchell99] • DrugScore [Gohlke00]

[Gohlke00]

Computing Scoring Functions Computing Scoring Functions Point-based calculation: Grid-based calculation: • Sum terms computed at positions of ligand atoms • Precompute “force field” for each term of scoring function (this will be slow) for each conformation of protein (usually only one) • Sample force fields at positions of ligand atoms p X Accelerate calculation of scoring function by 100X i r

p Xi r

[Huey & Morris]

Outline Systematic Search Introduction Uniform sampling of search space • Relative position (3) Scoring functions The search space • Relative orientation (3) • Molecular mechanics has dimensionality n m • Empirical functions • Rotatable bonds in ligand (n) 3 + 3 + r + r • Knowledge-based • Rotatable bonds in protein (m) Searching poses & conformations • Systematic search • Molecular dynamics Rotations Translations • Simulated annealing • Genetic algorithms • Incremental construction • Rotamer libraries

tstep rstep Results & Discussion FRED [Yang04]

6 Systematic Search Systematic Search Uniform sampling of search space Uniform sampling of search space • Exhaustive, deterministic • Exhaustive, deterministic • Quality dependent on granularity of sampling • Quality dependent on granularity of sampling • Feasible only for low-dimensional problems • Feasible only for low-dimensional problems § Example: search all rotations – Wigner-D-1

Maximum Correlation n o i

Rotations t

Translations a l e r r

X o C Rotation Correlation in Rotational Frequency Correlation Domain

Molecule 3D Grid Spherical Harmonic Functions Decompositions tstep rstep FRED [Yang04]

Molecular Mechanics Simulated Annealing Energy minimization: Monte Carlo search of parameter space: • Start from a random or specific state • Start from a random or specific state (position, orientation, conformation) (position, orientation, conformation) • Move in direction indicated • Make random state changes, accepting up-hill moves by derivatives of energy function with probability dictated by “temperature” • Stop when reach • Reduce temperature after each move local minimum • Stop after temperature gets very small

[Marai04] AutoDock 2.4 [Morris96]

Genetic Algorithm Incremental Extension Genetic search of parameter space: Greedy fragment-based construction: • Start with a random population of states • Partition ligand into fragments • Perform random crossovers and mutations to make children • Select children with highest scores to populate next generation • Repeat for a number of iterations

Gold [Jones95], AutoDock 3.0 [Morris98] FlexX [Rarey96]

7 Incremental Extension Incremental Extension Greedy fragment-based construction: Greedy fragment-based construction: • Partition ligand into fragments • Partition ligand into fragments • Place base fragment (e.g., with geometric hashing) • Place base fragment (e.g., with geometric hashing) • Incrementally extend ligand by attaching fragments

FlexX [Rarey96] FlexX [Rarey96]

Rotamer Libraries Rigid Docking Rigid docking of This is just like matching binding sites (complement) many conformations: • Can use same methods we used for matching and • Precompute all low-energy conformations indexing point, surface, and/or grid representations • Dock each precomputed conformations as rigid bodies Ligand

Ligand Predicted Possible Conformations Rigid Docking

Best Match

Points Surface Grid Protein Glide [Friesner04]

Outline Discussion Introduction Scoring functions • Molecular mechanics • Empirical functions • Knowledge-based Searching poses & conformations ? • Systematic search • Molecular dynamics • Simulated annealing • Genetic algorithms • Incremental construction • Rotamer libraries Results & Discussion

8 References

[Brooks83] Bernhard R. Brooks, Robert E. Bruccoleri, Barry D. Olafson, David J. States, S. Swaminathan, Martin Karplus, "CHARMM: A program for macromolecular energy, minimization, and dynamics calculations," J. Comp. Chem, 4, 2, 1983, pp. 187-217. [Eldridge97] M.D. Eldridge, C.W. Murray, T.R. Auton, G.V. Paolini, R.P. Mee, "Empirical scoring functions. I: The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes," J. Comput.- Aided Mol. Des., 11, 1997, pp. 425-445. [Friesner04] R.A. Friesner, J.L. Banks, R.B. Murphy, T.A. Halgren, J.J. Klicic, D.T. Mainz, M.P. Repasky, E.H. Knoll, M. Shelley, J.K. Perry, D.E. Shaw, P. Francis, P.S. Shenkin, "Glide: A New Approach for Rapid, Accurate Docking and Scoring.," J. Med. Chem, 47, 2004, pp. 1739-1749. [Gohlke00] H. Gohlke, M. Hendlich, G. Klebe, "Knowledge-based scoring function to predict protein-ligand interactions," J. Mol. Biol., 295, 2000, pp. 337-356. [Huey & Morris] Ruth Huey and Garrett M. Morris, "Using AutoDock With AutoDockTools: A Tutorial", http://www.scripps.edu/mb/olson/doc/autodock/UsingAutoDockWithADT.ppt [Jones97] G. Jones, P. Willett, R.C. Glen, A.R. Leach, R. Taylor, "Development and Validation of a Genetic Algorithm for Flexible Docking," J. Mol. Biol., 267, 1997, pp. 727-748. [Marai04] Christopher Marai, "Accommodating Protein Flexibility in Computational Drug Design," Mol Pharmacol, 57, 2, 2004, p. 213-8. [Marsden04] Marsden PM, Puvanendrampillai D, Mitchell JBO and Glen RC., "Predicting protein ligand binding affinities: a low scoring game?", Organic Biomolecular Chemistry, 2, 2004, p. 3267-3273. [Mitchell99] J.B.O. Mitchell, R. Laskowski, A. Alex, and J.M. Thornton, "BLEEP - potential of mean force describing protein- ligand interactions: I. Generating potential", J. Comput. Chem., 20, 11, 1999, 1165-1176. [Morris98] Morris, G. M., Goodsell, D. S., Halliday, R.S., Huey, R., Hart, W. E., Belew, R. K. and Olson, A. J. "Automated Docking Using a Lamarckian Genetic Algorithm and and Empirical Binding Free Energy Function", J. Computational Chemistry, 19, 1998, p. 1639-1662. [Muegge99] I. Muegge, Y.C. Martin, "A general and fast scoring function for protein-ligand interactions: A simplified potential approach," J. Med. Chem., 42, 1999, pp. 791-804. [Rarey96] M. Rarey, B. Kramer, T. Lengauer, G. Klebe, "A Fast Flexible Docking Method using an Incremental Construction Algorithm," Journal of Molecular Biology, 261, 3, 1996, pp. 470-489.