S. Vajda, 2005

Computational Mapping of Proteins for Exploring the Role of Binding Site Plasticity in Molecular

Recognition and Information Transfer

Karl H. Clodfelter and Sandor Vajda

Program in Bioinformatics and Department of Biomedical Engineering, Boston University

S. Vajda, 2005

The concept of protein mapping

Mapping of a protein means surrounding it by molecular probes – small molecules or functional groups –in order to determine the most favorable binding positions.

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Experimental approaches to protein mapping

Multiple Solvent Crystal Structure (Mattos & Ringe, 1996; Astex Technology, UK)

Identification of solvent binding sites by NMR(Liepinsh & Otting, 1997)

Structure-Activity Relationships (SAR by NMR)(Shuker, Hajduk, Meadows & Fesik, 1996; Abott)

Finding small ligands by disulphide tethering(Wells and co-workers; Sunesis Pharmaceuticals)

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Multiple solvent crystal structures

C. Mattos and D. RingeLocating and characterizing binding

sites on proteinsNature Biotech. 14: 595-599 (1996)

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Schema for our computational approach

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The CS-Map algorithm

Step 1: Rigid body search: Move (rigid) ligand molecules using a multi-start nonlinear simplex method in the electrostatic and desolvation field of the protein toward positions with low values of the simplified free energy expression

Gs = Eelec + Gdes + Vexcwhere

Eelec = (xi)qi Quasi-Coulombic approximation Gdes Calculated using a pairwise structure-based potential Vexc Excluded volume term, >0 only if there is overlap.

200 to 300 initial positions distributed on the protein surface, 30 simplex runs from each with random initial orientation of the simplex, resulting in 6000 to 9000 minima.

Step 2: Flexible search: Minimize the complexes derived in Step 1 using the more accurate free energy function

G = Eelec + G*des + Evdw

where G*des includes the solvent-solute van der Waals interactions.

Eelec + G*des Analytical Continuum Electrostatics: Generalized Born model

Evdw 6-12 Lennard-Jones potential

The local minimization allows for the flexibility of the probe molecules. Performed at 6000 to 9000 local minima.

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Step 3: Cluster the solutions on the basis of pairwise distances. Remove small clusters (with less than 15-20 minima).

Step 4: Rank clusters on the basis of average free energy: Calculate the (Boltzmann) average free energy of each clusters by

<G>i = pj Gj where

pj = exp(-Gj/RT)/{exp(- Gj/RT)}

Step 5: Identification of consensus sites: Use the 5 lowest free energy clusters of each ligand. Consensus sites are the positions at which several of such clusters of different probes overlap. The consensus sites ranked on the basis on the number of clusters included.

Step 6: Subclustering: Divide the clusters at the consensus sites into sub-clusters in order to determine the the residues that interact with the probes (via. the different rotational/translational states within a given cluster).

The CS-Map algorithm: continuation

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Progression of Type II Diabetes

Adapted from Saltiel, A.R. Cell (2001) 104;517-529

Can treat earlier with PPAR partial agonist

Current use of PPAR full agonists

Application 1: Mapping the PPAR ligand-binding domain

Peroxisome Proliferator Activated Receptor

S. Vajda, 2005Adapted from Olefsky JM & Saltiel AR, TEM (2000) 11, 362-367

Distinct biological response for each compound class

Peroxisome Proliferator Activated Receptor

Full Agonist

Antagonist

Partial Agonist

Full Agonist Partial Agonist

Antagonist

Different PPAR modulators induce distinct receptor conformations

Full Agonist Partial AgonistAntagonist

Application 1: Mapping the PPARligand-binding domain.

Co-activatorPPAR/RXR Complex

Transcription Complex

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Structure and “hot spots” of PPAR-

Site Description

P1 Head group of agonists, interacting with helix H12

P2 Overlapping the middle of agonists

P3 Upper distal end of the binding site, reached only by the partial agonist

P4 Hydrophobic pocket close to the entrance

B Surface pocket in the back, overlapping the dimerization region

F Surface pocket of unknown role

C1 Surface pocket, possibly contributing to cofactor binding

C2 Overlapping with the binding site of the co-activator peptide SRC-1

E1 Pocket defined by the lower ends of helices H3, H7, and H10

E2 Putative ligand entrance between H2’ and the -sheet

H12

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Observations to explain

1. PPARbinds a broad range of compounds, but only narrow classes ofmolecules act as strong agonists.

2. Agonist binding stabilizes the entire domain.

3. Structurally similar PPARg ligands can have significantly different pharmacological profiles. Rosiglitazone and pioglitazone are currently prescribed in clinical practice as insulin sensitizing agents, and have similar effects on normalization of glycemic levels. However, rosiglitazone decreases HDL whereas pioglitazone significantly raises HDL.

4. PPAR binds to DNA as a heterodimer with the retinoid X receptor (RXR). Allosteric communication occurs between the receptor partners, affecting ligand binding.

Sheu, S-H., Kaya, T., Waxman D. J., and Vajda, S. Exploring the binding site structure of the PPAR- ligand binding domain by computational solvent mapping. Biochemistry, 44, 1193-1209, 2005

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Unbound structure Structure with farglitazar (1fm9)

C2

C1

P2P3

P4

C2

C1

E1

P1P3

P4

E2

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Summary table of mapping results for 12 PPAR- structures

Chain

site

1prg

(a)

1prg

(b)

4prg

(a)

4prg

(b)

2prg

(a)

1fm

6 (d

)

1fm

9 (d

)

1k74

(d)

1i7i

(a)

1i7i

(b)

1knu

(a)

1nyx

(a)

7 9 6 9 8 7 6 9

P1

9 11 3 5 P2

9 9 8 6 5 7 3 3 5 6 4 4 P3

7 4 2 3 5 5 5 4 6 6 C1

2 2 3 6 6 3 3 4 5 6 C2

KEY:ApostructurePartial AgonistAgonist – RosiglitazoneAgonist – Other (Note that the boxed set of structures contain identical/similar bound ligands)

Number of clusters in each pre-defined pocket

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Conclusions I

1. PPARbinds a broad range of compounds, but activation is selective: Only strong agonists can open pocket P1 and interact with H12.

2. Agonist binding stabilizes the entire molecule: P3 is wide open in the unbound state, but closes down substantially upon ligand binding.

3. Structurally similar PPAR ligands can have significantly different pharmacological profiles:There are several co-activator binding regions (C1, C2, and F), and their shapes are affected by the structural details of the bound agonist, recruiting different coactivators.

4. Allosteric communication occurs between the receptor partners, affecting ligand binding:Pocket B is in the dimerization domain. Residue E448 is part of this pocket, whereas H449 is one of the major hydrogen-bonding partners in pocket P1, suggesting direct communication.

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Application 2: Cytochrome P450s

In bacteria, cytochrome P450 enzymes have relatively well defined substrates.

In mammals and fish, many cytochrome P450 enzymes metabolize a broad range of environmental chemicals, and may bind several molecules simultaneously.

Despite the low sequence identity between CYPs from different organisms, the tertiary structure is highy conserved.

What is the origin of broad substrate specificity?

Superposition of human CYP 2C9 (1OG5.pdb) and CYP 450 BM3 (2BMH.pdb) from Bacillus megaterium

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BM3 structure with bound palmitic acid (1FAG)

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Unbound BM3 structure: Chain A of 1BU7

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P450 2C5 bound to diclofenac (1NR6)

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Unliganded P450 2C5 (1DT6)

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Mapping of warfarin-bound and unliganded 2C9 structures

Warfarin-bound Unliganded

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Mapping of 2C9 in the presence of warfarin

The binding of the first ligand creates a tighter, better binding pocket.

The origin of drug-drug interactions.

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Conclusions II

• P450s with high substrate specificity have pre-defined surfaces for substrate binding that are identifiable by small molecule mapping

• P450s with low substrate specificity apparently have too few distinct structural features, with well-defined pockets formed only upon substrate binding.

• This suggests that the broad substrate specificity of these enzymes is at least partially achieved by an induced fit mechanism.

• The binding of a ligand to mammalian P450s can create a tighter, higher affinity site for a second ligand, which may help to explain the prevalence of drug-drug interactions involving P450s.

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Summation

• Small molecule mapping has the ability to reveal the mechanisms in the nuclear receptor, PPARγ, for the transfer of information from ligand binding to co-activator binding and therefore differential gene expression

• Molecular recognition in the active sites of Cytochrome P450s depends on localized conformational changes on the protein surface induced by the presence of the ligand

• Future investigation of ligand-protein interactions will require methods that can accurately account for these conformational changes in both the protein and the ligand

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Credits

Dr. Sheldon Dennis Michael SilbersteinShu-Hsien Sheu David LanciaDr. Tamas Kortvelyesi Spencer Thiel

Dr. Dagmar Ringe (Brandeis University)Dr. David Waxman (Boston University)Dr. Patrick Griffin (ExSAR Corporation)

Boston University Superfund Program (ES07381)National Institute of Health National Institute of Environmental HealthNational Science FoundationExSAR Corporation

S. Vajda, 2005