FlexibleProtein Docking

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Flexible-Protein Docking

• Dr Jonathan Essex
• School of Chemistry
• University of Southampton

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Southampton
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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

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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

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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!

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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 whats missing?

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The scoring function

• Existing functions inadequate
• Too simplified, for reasons of computational
  expediency
• Solvation and entropy often inadequately treated
• Possible solutions?
• More physics

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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

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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

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The rigid receptor hypothesis

• Erickson, J.A. et al. J. Med. Chem. 47, 45-55
  (2004)

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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

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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

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Protein flexibility in drug design

• Teague, S.J. Nature Reviews 2, 527-541 (2003)
• Effect of ligand binding on free energy

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Protein flexibility in drug design

• Multiple conformations of a few residues
• Acetylcholinesterase
• Phe330 flexible acts as a swinging gate

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Protein flexibility in drug design

• Movement of a large number of residues
• Acetylcholinesterase (again!)

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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

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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!)

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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!

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Nitrogen Regulatory Protein C (NtrC) plays a
central role in the bacterial metabolism of
nitrogen
Experimental evidence for population shift
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Changing nitrogen levels promote the activity of
NtrB kinase
Protein conformational change
NtrB kinase phosphorylates NtrC at aspartate 54
in the receiver domain
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Phosphorylation promotes conformational change in
the receiver domain
Protein conformational change
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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)

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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

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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)

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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

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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

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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

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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

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FlexE

• United protein description
• Similar parts of the protein structures are
  merged
• Dissimilar parts of the protein are treated as
  separate alternatives

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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

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FlexE

• Evaluation
• 10 proteins, 105 crystal structures
• RMSD lt 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

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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?

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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

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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

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Soft-Receptor Modelling
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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

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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

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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

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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)

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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

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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

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Arabinose Binding Protein

• Flexible protein docking
• Experimental structure found
• A number of other structures are isoenergetic
• Cannot uniquely identify the experimental
  structure

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Arabinose Binding Protein

• Flexible protein docking
• Most successful structure with experiment
  (transparent)
• Most successful structure, experiment, and
  isoenergetic mode

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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

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FAB Fragment

• Two isoenergetic binding modes
• Closest seed Isoenergetic seed

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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

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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

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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

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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

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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

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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

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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

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Flexible protein docking a case study

• Predicted ligand geometries reproduced (lt 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!

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Flexible protein docking a case study

• New ligands found by flexible receptor docking
• Receptor conformational energy needs to be
  considered

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Conclusion

• Rigid receptor approximation not universal
• Two main approaches to modelling receptor
  flexibility
• Ensemble
• Induced fit
• Further validation of these methods needed

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Acknowledgements

• Flexible Docking
• Richard Taylor, Phil Jewsbury, Astra Zeneca
• Practical
• Donna Goreham, Sebastien Foucher