Flexible-Protein Docking
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Transcript of Flexible-Protein 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 what’s 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)
protein RMSD / Å cocomplexes
RMSD / Å 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
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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 – Gbarrier
– 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
N-terminal receiver domain
Central catalytic domain
DNA binding domain
Experimental evidence for population shift
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Asp54
Phosphate
Changing nitrogen levels promote the activity of NtrB kinase
NtrB kinase phosphorylates NtrC at aspartate 54 in the receiver domain
Protein conformational change
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Asp54
Phosphate
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 < 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 (< 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