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Flexible-Protein Docking Dr Jonathan Essex School of Chemistry University of Southampton.

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Presentation on theme: "Flexible-Protein Docking Dr Jonathan Essex School of Chemistry University of Southampton."— Presentation transcript:

1 Flexible-Protein Docking Dr Jonathan Essex School of Chemistry University of Southampton

2 Southampton

3 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

4 Existing small-molecule docking Taylor, R.D. et al. J. Comput. Aided Mol. Des. 16, (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

5 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!

6 Existing small-molecule docking And how well do they work? –Jones, G. et al. J. Mol. Biol. 267, (1997) –In re-docking studies, achieved a 71 % success rate This is probably typical of most of these methods So what’s missing?

7 The scoring function Existing functions inadequate –Too simplified, for reasons of computational expediency –Solvation and entropy often inadequately treated Possible solutions? –More physics

8 The rigid receptor hypothesis Murray, C.W. et al. J. Comput. Aided Mol. Des. 13, (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

9 The rigid receptor hypothesis Erickson, J.A. et al. J. Med. Chem. 47, (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

10 The rigid receptor hypothesis Erickson, J.A. et al. J. Med. Chem. 47, (2004) proteinRMSD / Å cocomplexes RMSD / Å apo % self % average % apo trypsin thrombin HIV1-p

11 Models of Protein-Ligand Binding Goh, C.-S. et al. Curr. Opin. Struct. Biol. 14, (2004) Review of receptor flexibility for protein- protein interactions

12 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

13 Protein flexibility in drug design Teague, S.J. Nature Reviews 2, (2003) Effect of ligand binding on free energy

14 Protein flexibility in drug design Multiple conformations of a few residues –Acetylcholinesterase Phe330 flexible – acts as a swinging gate

15 Protein flexibility in drug design Movement of a large number of residues –Acetylcholinesterase (again!)

16 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

17 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 P450 cam substrates, camphor versus thiocamphor (two orientations, different to camphor!)

18 Experimental evidence for population shift Binding kinetics –Binding to low-population conformation should yield slow kinetics –  G barrier –Observed for p38 MAP kinase - mobile loop Rates of association vary between 8.5 x 10 5 and 4.3 x 10 7 M -1 s -1, depending on whether conformational change involved –Slow kinetics can make experimental comparison between assays difficult –Slow kinetics can improve ADME properties!

19 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

20 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

21 Asp54 Phosphate Phosphorylation promotes conformational change in the receiver domain Protein conformational change

22 NtrC – active and inactive conformations apparent P-NtrC – protein shifted towards activated conformation Volkman, B.F. et al. Science 291, (2001)

23 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

24 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, (2002)

25 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

26 FlexE Claussen, H. et al. J. Mol. Biol. 308, (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

27 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

28 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

29 FlexE United protein description –Similar parts of the protein structures are merged –Dissimilar parts of the protein are treated as separate alternatives

30 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

31 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

32 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?

33 Soft-Docking Knegtel, R.M.A. et al. J. Mol. Biol. 266, (1997) Build interaction grids within DOCK that incorporate the effect of more than one protein structure Effectively soften and average the different structures

34 Soft-Receptor Modelling Österberg, F. et al. Proteins 46, (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

35 Soft-Receptor Modelling

36 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

37 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…

38 Induced-Fit Docking Methods Molecular dynamics methods: –Mangoni, R. et al. Proteins 35, (1999) –Separate thermal baths used for protein and ligand to facilitate sampling Multicanonical molecular dynamics: –Nakajima, N. et al. Chem. Phys. Lett. 278, (1997) –Bias normal molecular dynamics to yield a flat energy distribution

39 Induced-Fit Docking Methods Monte Carlo methods –Apostolakis, J. et al. J. Comput. Chem. 19, (1998) Hybrid Monte Carlo and minimisation method. Poisson- Boltzmann continuum solvation used –ICM, Abagyan, R. et al. J. Comput. Chem. 15, (1997) Conventional MC, plus side-chain moves from a rotamer library Minimisation again required VS - J. Mol. Biol. 337, (2004)

40 Induced-Fit Docking Methods FDS Taylor, R. et al. J. Comput. Chem. 24, (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

41 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

42 Arabinose Binding Protein Flexible protein docking Experimental structure found A number of other structures are isoenergetic Cannot uniquely identify the experimental structure

43 Arabinose Binding Protein Flexible protein docking Most successful structure with experiment (transparent) Most successful structure, experiment, and isoenergetic mode

44 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

45 FAB Fragment Two isoenergetic binding modes Closest seedIsoenergetic seed

46 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

47 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

48 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

49 Flexible protein docking – a case study Wei, B.Q. et al. J. Mol. Biol. 337, (2004) Use experimental structures Like FlexE, flexible regions move independently, and are able to recombine Modified version of DOCK used

50 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

51 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

52 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

53 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!

54 Flexible protein docking – a case study New ligands found by flexible receptor docking Receptor conformational energy needs to be considered

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

56 Acknowledgements Flexible Docking –Richard Taylor, Phil Jewsbury, Astra Zeneca Practical –Donna Goreham, Sebastien Foucher


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