flexible-protein docking

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


• 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!


• 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 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

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


• 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!)


• 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 – 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!

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

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

Asp54

Phosphate

Phosphorylation promotes conformational change in the receiver domain



• 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


– Similar parts of the protein structures are merged

– Dissimilar parts of the protein are treated as separate alternatives


– 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…


• 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


• 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


• Flexible protein docking

• Experimental structure found

• A number of other structures are isoenergetic

• Cannot uniquely identify the experimental structure


• 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


• 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


• 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


• 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


• 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!


• 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

flexible-protein docking

Documents

proteinprotein binding

docking of trypsin

docking studies

review of receptor flexibility

associated protein movements

consequences of protein

smallprotein flexibility

ligand conformation