Computational Molecular Biology Protein - Ligand And...

Computational Molecular Biology

Protein - Ligand

And Protein - Protein Docking Methods

Prof.  Alejandro  Giorge1  Dr.  Francesco  Musiani  

Part 1: Protein - Ligand Docking Methods

What is the protein-ligand docking (molecular docking)

Goal: Given a protein structure, predict its ligands and where they bind

Applications:

v Function prediction v Drug design v Mechanisms

Protein-ligand docking: QUESTIONS

v  Where will the ligand bind?

v  Which ligand will bind?

v  How will the ligand bind?

v  When?

v  Where?

v  Why?

In other words: given a protein and a ligand, determine the poses and conformations

of the ligand minimizing the total energy of the protein-ligand complex

In practice…

Challenges…

Predicting ligand-binding energies by searching in the space

of possible poses and conformations

Challenges…

Predicting ligand-binding energies by searching in the space

of possible poses and conformations

Relative position (3 degrees of freedom) Relative orientation (3 degrees of freedom)

Rotatable bonds in ligand (N degrees of freedom)

Challenges…

Predicting ligand-binding energies by searching in the space

of possible poses and conformations

Relative position (3 degrees of freedom) Relative orientation (3 degrees of freedom)

Rotatable bonds in ligand (N degrees of freedom) Rotatable bonds in protein (M degrees of freedom)

Challenges…

Searching poses & conformations v Systematic search v Molecular dynamics v Simulated annealing v Genetic algorithms

v  Incremental construction v Rotamer libraries

Scoring functions

v Molecular mechanics v Empirical functions v Knowledge-based

Results & Discussion

v Clustering

Intra- and Inter-molecular forces

Intramolecular Forces (covalent) v Bond lengths v Bond angles

v Dihedral angles

Intermolecular Forces (non covalent) v Electrostatics

v Dipolar interactions v Hydrogen bonding v Hydrophobicity v van der Waals v Pi-stacking

Intra- and Inter-molecular forces

Coulombic interactions…

Arg  

Ligand  

Hydrogen bonds

Trypsin  and  substrate   Mannitol  Dehydrogenase  and  NAD  

Salt bridges

Salt bridges and ligand binding

Binding  of  napsagatran  to  thrombin  

Pi-Stacking Interactions: end to face

Pi-Stacking interactions: face to face

Cation-pi interactions…

Interactions with metal ions

Hydrophobicity

Binding  pocket  becames  «interior»  upon  compexa6on  with  ligand  

Big  penality:  charged  or  polar  groups  buried  but  umpaired    

Energe6c  contribu6on  is  propor6onal  to  the  size  of  the  surface  buried  upon  ligand  binding  (e.g.  –CH3  group  (25  Å2):  3  to  6  kcal/mol)  

Solvation and desolvation

ΔG  (binding,  vacuo)  

ΔG  (binding,  soluFon)  

ΔG  (soluFon  (E+I))  

ΔG  (soluFon  (EI))  

Solvation and desolvation

Solvation and desolvation

v  Rupture  of  H-­‐bonds  within  water  matrix   v  Reform  H-­‐bonds  

v  Reorganize  water  molecules  at  surface   v  Bury  a  hydrophobic  pocket  surface  v  Loose  degrees  of  freedom   v  Some  water  molecules  released  

ΔG  (binding,  vacuo)  

ΔG  (binding,  soluFon)  

ΔG  (soluFon  (E+I))  

ΔG  (soluFon  (EI))  

Scoring functions

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]

Empirical scoring functions

Empirical scoring functions

Macromolecular docking: empirical scoring function

Van  der  Waals  

H-­‐bond  

ElectrostaFcs  

SolvaFon  

Torsional  angles  

VdW   H-­‐bond   Elec  

Energy  

Distance  Distance   Distance  

Computing scoring functions

Computing scoring functions

v Systematic search

v Molecular dynamics

v Simulated annealing

v Genetic algorithms

v Incremental construction

v Rotamer libraries

Searching poses & conformations

Systematic search

Uniform sampling of search space: Relative position (3 Degrees of Freedom (DoF))

Relative orientation (3 DoF) Rotable bonds in ligand (m DoF) Rotable bonds in protein (n DoF)

Search space dimensions: 3 + 3 + m + n

Systematic search

Uniform sampling of search space: Exhaustive, deterministic

Quality dependent on granularity of sampling Feasible only for low-dimensional problems

Simulated annealing

Monte Carlo search of parameter space: v Start from a random or specific state (position, orientation, conformation)

v Make a random state changes, accepting up-hill moves with probability dictated by “temperature”

v Reduce temperature after each move

v Stop after temperature gets very small

Genetic algorithm

Genetic search of parameter space: v Start with a random population of states

v Perform random crossovers and mutations to make children

v Select children with highest scores to populate the next generation

v Repeat for a number of iterations

Part 2: Protein - Protein Docking Methods

Just to fix some ideas…

Basis of protein –protein complex formation

-  Shape  of  the  interacFon  surfaces  

-­‐   ElectrostaFcs  charaterisFcs  of  surface  residues  -­‐   FuncFonal  residues  

Macromolecular docking

The term macromolecular docking includes several computational techniques which have the aim of calculate models of the complexes between two or more macromolecules (protein-protein, protein-DNA, protein-RNA, etc.) Objective: prediction of the tridimensional structure of a complex between two marcomolecules. Techniques: - Rigid docking - Flexible docking

Macromolecular docking

Atomic coordinates of protein A

Atomic coordinates of protein B

DOCKING

Protein complex model structure

Macromolecular docking: methods

Macromolecular  docking  algorithms    Are  characterized  by  four  steps:    1-­‐  calcula6on  of  an  appropriate  representaFon  of  the  macromolecules  together  with  the  defini6on  of  the  degrees  of  freedom  of  the  calcula6on;    2-­‐  an  algorithm  able  to  explore  the  space  of  conforma6ons  with  the  highest  possible  completeness  and  efficiency;    3-­‐  a  scoring  funcFon  able  to  evaluate  the  quality  of  the  predic6ons    4-­‐  a  clustering  algorithm  

Macromolecular docking: methods

Systematic search: the two macromolecules are calculated in as many orientations as possibile. Guided search: complex formation is guided by an appropriate scoring function.

Knowledge-based search: similar to guided search, but the calculation makes use of external information (i.e. experimental data, bioinformatic predictions, etc.) to guide the calculation.

Macromolecular docking: general scheme

Protein  representaFon  using  the  

molecular  surface                

Different  probes  produce  Different  surfaces  

Coordinates of macromolecules A and B

Representations of A and B

Exploration of conformational space

Candidate complexes

AB model complex

Refining

Macromolecular docking: general scheme

Case  1:  sistemaFc  search    

The  conformaFonal  space  is  divided  into  segments.    

This  can  be  achieved  with  a  grid  representaFon  of  the  space  

Coordinates of macromolecules A and B

Representations of A and B

Exploration of conformational space

Candidate complexes

AB model complex

Refining

Macromolecular docking: scoring function

Macromolecule  A  (ρ  <<  0)  

Macromolecule  B  (0  >  δ  >  1)  

SCORING  FUNCTION  

a=1  

a<<0  

b=1  

0>b>1  

c  >  0  

Macromolecular docking: scoring function

a=1  

a<<0  

b=1  

0>b>1  

c  <<  0  

Macromolecular docking: general scheme

Coordinates of macromolecules A and B

Representations of A and B

Exploration of conformational space

Candidate complexes

AB model complex

Refining

Case  2:  guided  search    

The  search  for  the  minima  of  the  scoring  funcFon  is  made  inducing  a  

perturbaFon  on  the  iniFal  orientaFon.  

This  ‘move’  is  accepted  or  refused  on  the  basis  of  the  employed  

algorithm.  

Macromolecular docking: empirical scoring function

Van  der  Waals  

H-­‐bond  

ElectrostaFcs  

SolvaFon  

Torsional  angles  

VdW   H-­‐bond   Elec  

Energy  

Distance  Distance   Distance  

Macromolecular docking: general scheme

Both  in  the  exploraFon  of  the  conformaFonal  space  and  in  the  refining  step  it  is  possibile  to  

include  some  external  informaFon  (knowledge-­‐based  informaFon)  

Coordinates of macromolecules A and B

Representations of A and B

Exploration of conformational space

Candidate complexes

AB model complex

Refining

Macromolecular docking: Haddock

Dominguez,  C.;  Boelens,  R.;  Bonvin  A.M.J.J.  (2003)  J.  Am.  Chem.  Soc.  125,  1731-­‐1737.  de  Vries,  S.J.  et  al.(2007)  Proteins:  Struc.  Funct.  &  Bioinforma;c  69,  726-­‐733  (2007).  

Mem

bran

e  

Periplasm  

Cytoplasm  

Photosynthe6c  reac6on  center  from  T.  tepidum  (1EYS)  

HP1  

HP2  LP1  

LP2  

Nogi,  T.  et  al.  (2000)  Proc.  Nat.  Acad.  Sci.  USA  97:13561.  

Puta6ve  HiPIP  interac6on  site  

Macromolecular docking: examples (THC – HiPIP)

Tetra-­‐heme  (THC)  

Macromolecular docking: examples (THC – HiPIP)

Venturoli,  G.  et  al.  (2004)  Biochemistry    43:437-­‐445  Ciurli,  S.;  Musiani,  F.  (2005)  Photosynth.  Res.  85:115-­‐131  

THC   HiPIP  

Macromolecular docking: examples (THC – HiPIP)

THC  

HiPIP  

Venturoli,  G.  et  al.  (2004)  Biochemistry    43:437-­‐445  Ciurli,  S.;  Musiani,  F.  (2005)  Photosynth.  Res.  85:115-­‐131  

THC   HiPIP  

Macromolecular docking: examples (THC – HiPIP)

THC  

HiPIP  

Venturoli,  G.  et  al.  (2004)  Biochemistry    43:437-­‐445  Ciurli,  S.;  Musiani,  F.  (2005)  Photosynth.  Res.  85:115-­‐131  

Hope,  H.P.  (2000)    Biochim.  Biophis.  Acta  1456:5-­‐26  

Macromolecular docking: examples (Cytochrome f - plastocyanin)

S(Met)

Cu S(Cys)(His)N(His)N

PC

NN

NN

OH OOH O

SS

Fe

Cys 24

Cys 21

FeN

N

N

N

NH2

NH

N

His 25

Tyr 1

Cyt f

Musiani,  F.;  Dikiy,  A.;  Semenov,  A.Y.;  Ciurli,  S.  (2005)    J.  Biol.  Chem.  280:18833-­‐18841.    

Macromolecular docking: examples (Cytochrome f - plastocyanin)

Bioinformatic predictions: multi-domain protein conformations

Del  Campo,  C.;  Agries6,  F.;  Danielli,  A.;  Roncara6,  D.;  Musiani,  F.;  Ciurli,  S.  ;  Scalrato,  V.  (2012)  in  prepara6on  

Bioinformatic predictions: protein – DNA docking

Del  Campo,  C.;  Agries6,  F.;  Danielli,  A.;  Roncara6,  D.;  Musiani,  F.;  Ciurli,  S.  ;  Scalrato,  V.  (2012)  in  prepara6on