Your Federal Quarterly Tax Payments are due April 15th Get Help Now >>

Flexible-Protein Docking by kellena91

VIEWS: 51 PAGES: 56

									Flexible-Protein Docking

     Dr Jonathan Essex
     School of Chemistry

   University of Southampton
Southampton
                 Programme
• Existing small-molecule docking
   – Typical approximations, and outcomes

• Evidence for receptor flexibility, and
  consequences

• Methods for accommodating protein flexibility
  in docking:
   – The ensemble approach

   – The induced fit approach
 Existing small-molecule docking
• Taylor, R.D. et al. J. Comput. Aided Mol. Des.
  16, 151-166 (2002)

• Many docking algorithms (some 127
  references in this 2002 review!)

• Most docking algorithms:
  – Rigid receptor hypothesis
     • Limited receptor flexibility in, for example, GOLD – polar
       hydrogens
 Existing small-molecule docking
• Most docking algorithms:
  – Range of ligand sampling methods
     • Pattern matching, GA, MD, MC…

  – Treatment of intermolecular forces:
     • Simplified scoring functions: empirical, knowledge-based
       and molecular mechanics

     • Very simple treatment of solvation and entropy, or
       completely ignored!
 Existing small-molecule docking
• And how well do they work?
  – Jones, G. et al. J. Mol. Biol. 267, 727-748 (1997)

  – In re-docking studies, achieved a 71 % success
    rate

• This is probably typical of most of these
  methods

• So what’s missing?
          The scoring function

• Existing functions inadequate
  – Too simplified, for reasons of computational
    expediency

  – Solvation and entropy often inadequately treated

• Possible solutions?
  – More physics
   The rigid receptor hypothesis
• Murray, C.W. et al. J. Comput. Aided Mol.
  Des. 13, 547-562 (1999)
  – Docking to thrombin, thermolysin, and
    neuraminidase

  – PRO_LEADS – Tabu search

  – In self docking, ligand conformation correctly
    identified as the lowest energy structure – 76 %

  – For cross-docking – 49 % successful

  – Some of the associated protein movements very
    small
   The rigid receptor hypothesis
• Erickson, J.A. et al. J. Med. Chem. 47, 45-55
  (2004)
  – Docking of trypsin, thrombin and HIV1-p

  – Self-docking, docking to a single structure that is
    closest to the average, and docking to apo
    structures

  – Docking accuracy declines on docking to the
    average structure, and is very poor for docking to
    apo

  – Decline in accuracy correlated with degree of
    protein movement
    The rigid receptor hypothesis
 • Erickson, J.A. et al. J. Med. Chem. 47, 45-55
   (2004)
protein     RMSD / Å RMSD / Å    %        %       %
           cocomplexes apo      self   average   apo

trypsin       0.15      1.6      67      60        37


thrombin      0.31      1.0      36      27        9


HIV1-p        0.73      2.0      50      35        4
   Models of Protein-Ligand Binding
• Goh, C.-S. et al.
  Curr. Opin. Struct.
  Biol. 14, 104-109
  (2004)

• Review of receptor
  flexibility for protein-
  protein interactions
Models of Protein-Ligand Binding
• This paper classifies protein-protein binding
  in terms of these models

• Induced fit assumed if there is no
  experimental evidence for a pre-existing
  equilibrium of multiple conformations

• Note that strictly this is an artificial distinction
   – Statistical mechanics – all states are accessible
     with a non-zero probability

   – For induced fit, probability of observing bound
     conformation without the ligand may be very small
     Protein flexibility in drug design

• Teague, S.J. Nature
  Reviews 2, 527-541
  (2003)

• Effect of ligand
  binding on free
  energy
      Protein flexibility in drug design

• Multiple
  conformations of a
  few residues
  – Acetylcholinesterase

     • Phe330 flexible –
       acts as a
       swinging gate
  Protein flexibility in drug design
• Movement of a large number of residues
  – Acetylcholinesterase (again!)
     Protein flexibility in drug design
• Table 1 in Teague
  paper lists
  pharmaceutically
  relevant flexible targets
  (some 30 systems!)

• Consequences of
  protein flexibility for
  ligand design
   – One site, several ligand
     binding modes possible
  Protein flexibility in drug design
• Consequences
  – Allosteric inhibition

  – Binding often remote from active site – NNRTIs

• Proteins in metabolism and transport
  – Promiscuous
     • Bind many compounds, in many orientations

     • E.g P450cam substrates, camphor versus thiocamphor
       (two orientations, different to camphor!)
Experimental evidence for population
                shift
• Binding kinetics
   – Binding to low-population conformation should
     yield slow kinetics – DGbarrier

   – Observed for p38 MAP kinase - mobile loop
      • Rates of association vary between 8.5 x 105 and 4.3 x
        107 M-1s-1, depending on whether conformational change
        involved

   – Slow kinetics can make experimental comparison
     between assays difficult

   – Slow kinetics can improve ADME properties!
Experimental evidence for population
                shift
 Nitrogen Regulatory Protein C (NtrC) plays a central role in the
 bacterial metabolism of nitrogen



 N-terminal                                           DNA
  receiver                                           binding
  domain                                             domain




                       Central catalytic
                           domain
    Protein conformational change

 Changing nitrogen levels promote the activity of NtrB kinase

         Phosphate



                     Asp54




NtrB kinase phosphorylates NtrC at aspartate 54
             in the receiver domain
   Protein conformational change

Phosphorylation promotes conformational change in the
receiver domain


            Phosphate



                    Asp54
    Protein conformational change

• NtrC – active and
  inactive conformations
  apparent
• P-NtrC – protein shifted
  towards activated
  conformation
• Volkman, B.F. et al.
  Science 291, 2429-33
  (2001)
                   Summary
• Protein flexibility important in ligand design

• Two basic mechanisms
   – Selection of a binding conformation from a pre-
     existing ensemble – population shift

   – Induced fit – binding to a previously unknown
     conformation

   – Thermodynamically, these mechanisms are
     identical

• Evidence for population shift from binding
  kinetics, and protein NMR
      Docking methods for
 incorporating receptor flexibility
• Ensemble docking
  – Docking to individual protein structures, or parts of
    protein structures – “ensemble docking”

  – Docking to a single average structure – “soft
    docking”

• Induced fit modelling

• Carlson, H.A. Curr. Opin. Chem. Biol. 6, 447-
  452 (2002)
           Ensemble docking
• Generate an ensemble of structures, and
  dock to them

• Experimentally derived structures
  – NMR or X-ray structures

• Computationally derived structures
  – Molecular dynamics

  – Simulated annealing

  – Normal mode propagation
                      FlexE
• Claussen, H. et al. J. Mol. Biol. 308, 377-395
  (2001)

• Extension of the FlexX algorithm:
  – Preferred conformations for ligands identified

  – Simplified scoring function adopted – based on
    hydrogen bonds, ionic interactions etc.

  – Break ligand into base fragments by severing
    acyclic single bonds
                      FlexE
• Extension of the FlexX algorithm:
  – Base fragments placed in active site by
    superposing interaction centres

  – Incrementally reconstruct ligand onto base
    fragments

  – Test each partial solution and continue with the
    best for further reconstruction
                       FlexE
• United protein description
  – Use a set of protein structures representing
    flexibility, mutations, or alternative protein models

  – Assumes that overall shape of the protein and
    active site is maintained across the series

  – FlexE selects the combination of partial protein
    structures that best suit the ligand

  – Flexibility given by FlexE is therefore defined by
    the protein input structures
                      FlexE
• United protein description
  – Similar parts of the protein structures are merged

  – Dissimilar parts of the protein are treated as
    separate alternatives
                      FlexE
• United protein description
  – Some combinations of the structural features are
    incompatible and not considered

  – As the ligand is constructed, the optimum protein
    structure is identified

  – Combination strategy for the protein may result in
    a structure not present in the original data set
                           FlexE
• Evaluation
   – 10 proteins, 105 crystal structures

   – RMSD < 2.0 Å, within top ten solution, 67 % success

   – Cross-docking with FlexX gave 63 %

   – FlexE faster than cross-docking with FlexX

• Aldose reductase - very flexible active site
   – FlexE docking successful (3 ligands)

   – Using only one rigid protein structure would not have
     worked
           Ensemble docking
• Advantages:
  – Well-defined computational problem

  – Computational cost generally scales linearly with
    number of structures (potential combinatorial
    explosion)

  – Can use either experimental information, or
    structures derived from computation

• Disadvantages:
  – What happens if the appropriate bound receptor
    conformation is not present in the ensemble?
                Soft-Docking

• Knegtel, R.M.A. et al. J. Mol. Biol. 266, 424-
  440 (1997)

• Build interaction grids within DOCK that
  incorporate the effect of more than one
  protein structure

• Effectively soften and average the different
  structures
       Soft-Receptor Modelling
• Österberg, F. et al. Proteins 46, 34-40 (2002)

• Similar approach applied to Autodock grids
  – Energy-weighted grid

  – Boltzmann-type weighting applied to reduce the
    influence of repulsive terms

• Combined grids performed very well – HIV
  protease
Soft-Receptor Modelling
          Soft-Receptor Modelling
• Advantages
  – Low computational cost – use of single averaged protein
    model

  – Can use experimental or simulation derived structures

• Disadvantages
  – Cope with large-scale motion?

  – How reliable is this “averaged” representation?

  – Mutually exclusive binding regions could be
    simultaneously exploited

  – Active sites enlarged
   Induced-Fit Docking Methods

• Allow protein conformational change at the
  same time as the docking proceeds

• Taking some of these algorithms, in no
  particular order…
   Induced-Fit Docking Methods
• Molecular dynamics methods:
  – Mangoni, R. et al. Proteins 35, 153-162 (1999)
  – Separate thermal baths used for protein and
    ligand to facilitate sampling

• Multicanonical molecular dynamics:
  – Nakajima, N. et al. Chem. Phys. Lett. 278, 297-
    301 (1997)
  – Bias normal molecular dynamics to yield a flat
    energy distribution
   Induced-Fit Docking Methods
• Monte Carlo methods
  – Apostolakis, J. et al. J. Comput. Chem. 19, 21-37
    (1998)
     • Hybrid Monte Carlo and minimisation method. Poisson-
       Boltzmann continuum solvation used

  – ICM, Abagyan, R. et al. J. Comput. Chem. 15,
    488-506 (1997)
     • Conventional MC, plus side-chain moves from a rotamer
       library

     • Minimisation again required

     • VS - J. Mol. Biol. 337, 209-225 (2004)
    Induced-Fit Docking Methods
• FDS Taylor, R. et al. J. Comput. Chem. 24,
  1637-1656 (2003)

• Flexible ligand/flexible protein docking
   – large side chain motions, rotamer library

• Solvation included “on the fly”
   – continuum solvation model – GB/SA

• Soft-core potential energy function
   – anneal the potential to improve sampling
              Arabinose Binding Protein
• Rigid protein docking



• Low energy structures are
  essentially identical to the X-
  ray structure



• Dock starting from
  experimental result, does not
  return to it
             Arabinose Binding Protein
• Flexible protein docking

• Experimental structure found

• A number of other structures
  are isoenergetic

• Cannot uniquely identify the
  experimental structure
Arabinose Binding Protein
             • Flexible protein docking


             • Most successful structure
               with experiment
               (transparent)




             • Most successful structure,
               experiment, and
               isoenergetic mode
           Monte Carlo Docking
• 15 complexes studied
• Rigid receptor
  – 13/15 identified X-ray binding mode
  – 8/15 were the unique, lowest energy structures
  – 3/15 were part of a cluster of low-energy binding modes

• Flexible receptor
  – 11/15 identified X-ray binding mode
  – 3/15 were the unique, lowest energy structure
  – 6/15 were part of a cluster of low-energy binding modes
             FAB Fragment
• Two isoenergetic binding modes




  Closest seed            Isoenergetic seed
                  Conclusion
• Rigid protein docking as successful as other
  methods, but much more expensive

• Flexible protein docking does find X-ray
  structures, but does not uniquely identify
  them
   – Refine scoring function?

• Using this methodology, need to consider a
  number of structures

• Further validation required
                    Summary
• Two main approaches for modelling receptor
  flexibility
  – Use of multiple structures (experimental or
    theoretical) either independently, or averaged in
    some way – ensemble approach

  – Allow the receptor to adopt conformations under
    the influence of the ligand – induced fit approach
                   Summary
• Ensemble is the more widely employed – less
  expensive, but limited somewhat by the
  composition of the ensemble

• Induced fit should overcome this
  disadvantage of ensemble methods

• Induced fit methods can have significant
  sampling problems
  – not computationally limited

  – search space large, and increasing as extra
    degrees of freedom added
      Flexible protein docking –
             a case study
• Wei, B.Q. et al. J. Mol. Biol. 337, 1161-1182
  (2004)

• Use experimental structures

• Like FlexE, flexible regions move
  independently, and are able to recombine

• Modified version of DOCK used
           Flexible protein docking –
                  a case study
• Receptor decomposed
  into three parts
   – Green – rigid

   – Blue and red – two
     flexible parts

• Ligand scored against
  each component

• Best-fit protein
  conformation
  assembled from these
  components
     Flexible protein docking –
            a case study
• Scoring function
  – Electrostatic (potential from PB), van der Waals

  – Solvation (scaled AMSOL result according to
    buried surface area)

• Large ligands favoured for large cavities
  – Penalty for forming the larger cavity introduced
     Flexible protein docking –
            a case study
• In screening, enrichment improved compared
  to docking against individual conformations

• ACD screened against L99A M102Q mutant
  of T4L
  – 18 compounds that were predicted to bind and
    change cavity conformation, tested

  – 14 found to bind

  – X-ray structures obtained on 7
      Flexible protein docking –
             a case study
• Predicted ligand geometries reproduced (<
  0.7 Å)

• In five structures, part of observed cavity
  changes reproduced

• In two structures, receptor conformations not
  part of original data set, and therefore not
  reproduced!
      Flexible protein docking –
             a case study




• New ligands found by flexible receptor
  docking

• Receptor conformational energy needs to be
  considered
                  Conclusion

• Rigid receptor approximation not universal

• Two main approaches to modelling receptor
  flexibility
  – Ensemble

  – Induced fit

• Further validation of these methods needed
          Acknowledgements

• Flexible Docking
  – Richard Taylor, Phil Jewsbury, Astra Zeneca

• Practical
  – Donna Goreham, Sebastien Foucher

								
To top