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Molecular Docking G. Schaftenaar
Docking Challenge ,[object Object],[object Object],[object Object]
Two main tasks of Docking Tools ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Rigid-body docking   algorithms
Introducing flexibility: Whole molecule docking ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Monte Carlo ,[object Object],[object Object],[object Object],[object Object],[object Object]
Molecular Dynamics ,[object Object],[object Object],[object Object],[object Object]
Simulated Annealing Finding a global minimium by lowering the temperature during the Monte Carlo/MD simulation
Genetic Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introducing flexibility:  Fragment Based Methods ,[object Object],[object Object],[object Object],[object Object],[object Object]
Placing Fragments and Rigid Molecules ,[object Object],[object Object],[object Object],[object Object]
Geometric hashing ,[object Object],[object Object]
 
Pose-Clustering ,[object Object],[object Object],[object Object]
Clique-Detection ,[object Object],[object Object],[object Object]
Scoring Functions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Shape & Chemical Complementary Scores ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Empirical Scoring ,[object Object],[object Object],[object Object]
Empirical scoring Loss of entropy during binding Hydrogen-bonding Ionic interactions Aromatic interactions Hydrophobic interactions rot rot N G G G       0         bonds H neutral hb R f G . ,          . , int ionic io R f G         int arom arom R f G . ,         . . , cont lipo lipo R f G 
Force Field Scoring (Dock)             lig i prot j i ij ij ij ij nonbond r B r A E c 6 12 ,[object Object],[object Object],[object Object],[object Object], ij j r q q
Knowledge-based Scoring Function ,[object Object],[object Object],[object Object],Boltzmann-Like Statistics of Interatomic Contacts.       l p ref l p F P P      , exp ,  
Distribution of interatomic distances is converted  into energy functions by inverting Boltzmann’s law. F P (N,O)
Potential of Mean Force (PMF)                 ij seg i corr Vol B ij r r f T k r F  _ ln   r ij seg  Number density of atom pairs of type  ij at atom pair distance  r Number density of atom pairs of type  ij in reference sphere with radius R ij bulk  ij bulk 
Consensus Scoring ,[object Object],[object Object],[object Object],[object Object],[object Object]
Virtual screening by Docking ,[object Object],[object Object],[object Object]
Concluding remarks Although the reliability of docking methods is  not so high, they can provide new suggestions for  protein-ligand interactions that otherwise  may be overlooked Scoring functions are the Achilles’ heel  of docking programs . False positives rates can be reduced using several scoring functions in a consensus-scoring strategy
Docking programs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FLEXX ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How DOCK works ,[object Object],C avities in the receptor  are  used to  define spheres  (blue) ; the centr e s  are  potential locations for ligand atoms. thioketal in the HIV1-protease active site   Sphere centr e s are  m atched to ligand  atoms, to determine possible orientations  for the ligand.  10 4  orientations generated
GOLD (Genetic Optimisation  for Ligand Docking) Performs automated docking with  full acyclic ligand flexibility, partial  cyclic ligand flexibility and partial  protein flexibility in and around  active site. Scoring: includes H-bonding term,  pairwise dispersion potential  (hydrophobic interactions),  m olecular   and mechanics term for  internal energy. ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Dock Sem

  • 1. Molecular Docking G. Schaftenaar
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  • 8. Simulated Annealing Finding a global minimium by lowering the temperature during the Monte Carlo/MD simulation
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  • 19. Empirical scoring Loss of entropy during binding Hydrogen-bonding Ionic interactions Aromatic interactions Hydrophobic interactions rot rot N G G G       0         bonds H neutral hb R f G . ,          . , int ionic io R f G         int arom arom R f G . ,         . . , cont lipo lipo R f G 
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  • 22. Distribution of interatomic distances is converted into energy functions by inverting Boltzmann’s law. F P (N,O)
  • 23. Potential of Mean Force (PMF)                 ij seg i corr Vol B ij r r f T k r F  _ ln   r ij seg  Number density of atom pairs of type ij at atom pair distance r Number density of atom pairs of type ij in reference sphere with radius R ij bulk  ij bulk 
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  • 26. Concluding remarks Although the reliability of docking methods is not so high, they can provide new suggestions for protein-ligand interactions that otherwise may be overlooked Scoring functions are the Achilles’ heel of docking programs . False positives rates can be reduced using several scoring functions in a consensus-scoring strategy
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Editor's Notes

  1. Explain docking is fitting ligand into the receptor, steric and electrostatic match