Nov. 2006 B. S. Sadjad 1 Toward Fully Flexible Docking Bashir S. Sadjad [email protected] School...

Nov. 2006 B. S. Sadjad 1

Toward Fully Flexible Docking

Bashir S. Sadjad

[email protected]

School of Computer Science, University of Waterloo, Canada

Simulated Biomolecular Systems, Toronto, Canada


Outline● How does Aspirin work?!● Structure-based rational drug design● What is Docking?● Current approaches and software packages● Why is protein flexibility important?● One step forward in flexible docking ...


What Is Aspirin?

● Acetosal: ● Route of administration: Oral● Structure:

NOTE: Images without reference are taken from public domain (mostly Wikipedia)

C9H

8O

4


How does Aspirin Work?

● A painkiller, also used against fever● Reduce production of Prostaglandin, Thromboxane● Prostaglandin:

● Prostaglandin binds to some trans-membrane proteins of spinal neuron cells causing pain.


How does Aspirin Work? (cont'd)● Cyclooxygenase is inhibited

(breaking the pathway).● Thromboxane pathway:

● Many of drugs interfere in a protein function.

● Aspirin effect is irreversible, we like reversible!


Enzymes (Review)● Enzymes are great catalyzers (may speed up a

reaction by 5 to 17 order of magnitude).● An explanation of how they work:


Enzyme Inhibition● Change of shape or chemical properties of active

site on an enzyme.● Example: blocking active site in HIV protease by

ritonavir.




How Was Aspirin Discovered?● Roots can be traced back to 5th century BC, in

Hippocrates notes regarding a bitter powder extracted from willow bark easing pain.

● Similar references found in other ancient notes.

Willow produces salicin

which acts similarly to

aspirin in the body.● Main discovery was done in 19th century.


Rational Drug Design● The discovery was by trial and error anyway!● How a similar drug may be discovered in 21st

century?– Identify related pathways

– Select target proteins

– Identify active sites and allosteric sites

– Try to inhibit target proteins

– Inhibition may happen by ligand binding


Rational Drug Design (Example)● Zanamivir (a ligand) used for

treatment of Influenza virus.● Inhibits Neuraminidase, an

enzyme on the surface of Influenza virus.

● One of the first rationally designed drugs, by Biota (1989).

● Marketed by GSK (1999).

Neuraminidase crystal structure

(the ligand is NOT Zanamivir)




High Throughput Screening (HTS)● Once the target protein is purified a library of

ligands might be tested against it.● The size of a practical library is in 105 – 106

range.● Ligands active against the target protein can be

selected by automated mechanisms.● This requires significant resources including

expensive labs.


Virtual HTS● How if we can predict the HTS result by a

computer program?● This is Virtual High Throughput Screening.● One way to do this is by simulating the binding

process using properties of involved molecules.● Free Energy of Binding determines the affinity.


Free Energy● Each conformation and binding mode has a

specific free energy:

image from [1]


Docking● Determine the best

binding mode:– An approximation of

free energy is used (scoring function).

– The search engine finds the minimum of the scoring function.

● Carbonic anhydrase and a bound ligand

Image generated by CheVi of SimBioSys (coordinates from 1AZM PDB code)




Classification Criteria● Type of scoring function:

– Force-field based

– Empirical

– Statistical● Search method:

– Systematic

– Stochastic● Degree of ligand-protein flexibility


Scoring● Approximating reality: a bad approximation won't

work even with the best search method.● A general form for an empirical scoring function:

image from [2]


Search● Stochastic and heuristic

– MCDOCK (simulated annealing)

– AutoDock2 (simulated annealing)

– GOLD (genetic algorithm)● Directed

– FlexX (incremental construction)

– eHiTS (exhaustive search)● Combined

– Glide (systematic pose gen. + stochastic optimization)


eHiTS● eHiTS approach [4]:

– Ligand fragmentation

– Fragment rigid-dock

– Fragment matching

– Ligand reconstruction

– Local optimization


Example (Rigid Docking)● Each fragment is

docked in cavity.● For sufficient

accuracy a fine sampling should be done.


Example (Matching)● All fragments are

scored.● A diverse set of

matching poses with high scores are selected.

● A full ligand pose is generated from each matching set.




Protein Structure and Ligand Binding● Protein structure may significantly be changed by

ligand binding.● Calmodulin (a calcium-binding protein):

– Movie: http://molmovdb.org/cgi-bin/morph.cgi?ID=78252-5656

image from [1]


The Allosteric Effect● Binding of

ligands may regulate the protein function.

● Example: binding of oxygen and carbon-dioxide to hemoglobin:


Binding Site Flexibility● Ligand binding changes the binding site of the

protein. This is called induced fit.● In many of protein-ligand complexes in PDB, the

cavity surrounds the ligand with a small open part. ● Rigid treating of binding site (as done by most

docking programs), makes binding energy prediction difficult.


Example (Ligand Binding)● Conformational change at the binding site of

Renin.

image from [1]


Example (Cavity Closure)● L-Arabinose-

binding protein complexed with L-Arabinose. (PDB: 1ABE)


A Note on Structure-Function Assumption

● Amino Acid – Structure – Function assumption.● Consider a highly hydrophilic protein sequence, is

it folded in water? Does it have any functions?● Indeed it is not in a single folded state but it can

be functional! There are functional intrinsically unstructured proteins.

● They may fulfil different tasks and have different fold for each task.


Example (Unstructured)● The pKID domain of CREB protein, complexed

with KIX domain of CREB-binding protein.

image from [3]


Example (Structural Change)● The TAZ1 domain of CREB-binding protein

complexed with two different domains.

image from [3]




Truly Flexible Docking● A truly flexible docking application is in fact a

folding program!● eHiTS is an ab-initio method: folding complexity● Different types of protein mobility:

– Movement of large domains

– Multiple conformations observed in a few residues


Movement of Domains● Patterns of domain movement:

● Ribose-binding protein movie (2DRI, 1URP):– http://molmovdb.org/cgi-bin/morph.cgi?ID=645772-17065

image from [1]


Conformations of a Few Residues● Acetylcholinesterase (PDB: 2ACE, 1EVE, 1VOT, 1ACL)

image from [1]


Truly Flexible Docking● A truly flexible docking application is in fact a

folding program!● eHiTS is an ab-initio method: folding complexity● Different types of protein mobility:

– Movement of large domains

– Multiple conformations observed in a few residues (to be addressed in first step)


Binding Site Side-Chains● Modeling side-

chain flexibility of binding site residues in eHiTS.

● First the candidate chains should be selected.– Solvent exposed?

– More statistics


Binding Site Side-Chains (cont'd)● Same technique of

fragmentation can be applied to side-chains.

● Rigid docking and pose matching with the backbone constraints.


The Problem Size (Difficulty)● Run statistics for a set of 20 PDB codes (all

numbers are averages):– # rigid fragments: 3.05

– # poses tried in RD: 60 million

– # poses accepted in RD: 493,354

– Best Match RMSD: 0.60 A

– Best Match Found: 1.01A

(NOTE: Finding the best match is NP-hard for a general scoring function.)


Pose Match Example● Closest

match for an HIV protease inhibitor (1AAQ):


Training● eHiTS uses a statistical scoring function.● Training is done by known structures.● Pose Match specific training is done by linear

programming modeling and using CLP package.● For receptor flexibility modeling we can either:

– Generate receptor decoys

– Use PDB complexes with same receptor


Goals and Previous Works● Induced fit modeling in Glide [5]:

– Docking into rigid receptor using softened scoring func.

– Receptor active site sampling

– Complex optimization (minor backbone flexibility)● Differences with our approach:

– Simultaneous handling of ligand/receptor flexibility

– Same scoring function (no softened version)● The set of cross-docking data can be used for

training and benchmarking.


Selected References1. S. J. Teague, Implications of Protein Flexibility for Drug Discovery, Nature Reviews (Drug

Discovery), vol. 2, pp. 527-541, 2003.

2. D. B. Kitchen, H. Decornez, J. R. Furr, J. Bajorath, Docking and Scoring in Virtual

Screening for Drug Discovery: Methods and Applications, Nature Reviews (Drug

Discovery), vol. 3, pp. 935-949, 2004.

3. H. J. Dyson, P. E. Wright, Intrinsically Unstructured Proteins and Their Function, Nature

Reviews (Molecular Cell Biology), vol. 6, pp. 197-208, 2005.

4. Z. Zsoldos, D. Reid, A. Simon, B. S. Sadjad, A. P. Johnson, eHiTS: A New Fast, Exhaustive

Flexible Ligand Docking System, J. of Mol. Graphics and Modeling, (to appear – available

online).

5. W. Sherman, T. Day, M. P. Jacobson, R. A. Friesner, R. Farid, Novel Procedure for

Modeling Ligand/Receptor Induced Fit Effects, J. Med. Chem., vol. 49, pp. 534-553, 2006.


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