PI: Karplus, Martin Title: Modeling atomic structure of the EmrE multidrug pump to design inhibitor 

peptides

Received: 06/14/2013 FOA: PA11-262 Council: 01/2014

Competition ID: ADOBE-FORMS-B2 FOA Title: NIH SMALL RESEARCH GRANT PROGRAM (PARENT R03)

1 R03 AI111416-01 Dual: Accession Number: 3599020

IPF: 3212901 Organization: HARVARD UNIVERSITY

Former Number: Department: Chemistry and Chemical Biology

IRG/SRG: ZRG1 MSFD-N (08)F AIDS: N Expedited: N

Subtotal Direct Costs Animals: N New Investigator: N

(excludes consortium F&A) Humans: N Early Stage Investigator: N

Year 1: Clinical Trial: N

Year 2: Current HS Code: 10

HESC: N

Senior/Key Personnel: Organization: Role Category:

Martin Karplus Ph.D. Harvard University PD/PI 

Victor Ovchinnikov Ph.D Harvard University Co-Investigator

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Specific Aims

Many aromatic compounds are used as antibiotic, antiseptic, and antineoplastic agents in the control of pathogens.

In turn, bacteria have evolved complex mechanisms for active expulsion of such compounds from their cytoplasm, which leads to the phenomenon of drug resistance. The increasing emergence of organisms resistant to

even the most powerful antibiotics is among the most serious problems for the treatment of infectious diseases

in the developing world, such as tuberculosis and malaria. The rapid rise of carbapenem-resistant Enterobacteriacea (CRE) such as Klebsiella has recently prompted actions from the CDC and the HHS Department.1 2

The EmrE transporter in E. coli, a member of the widespread small multidrug resistance (SMR) family of

membrane-bound transporters, actively pumps toxic compounds out of the cytoplasm and thus contributes to

the drug resistance. The aims of this proposal are (1) to develop an atomic-level model of the EmrE transporter

and (2) to design peptide analogs that inhibit the formation of the active dimer in the bacterial membrane. The

approach has the potential to enhance current antibacterial therapies for a wide range of pathogens, and also to

test the applicability of synthetic peptides to target specific oligomerization motifs in membrane proteins.

Aim 1. To construct and validate an atomic-level model of the EmrE membrane protein. EmrE is a small antisymmetric homodimer (110 residues per monomer) with eight transmembrane helices whose low-resolution

Cα -only structure is known. The Cα structures will be used with available modeling protocols will be used to

construct a series of all-atom models. To improve the models, they will be subjected to molecular dynamics

simulations with replica exchange in the presence of an explicit lipid membrane and water molecule environment. The free energy of the refined structural candidates will be computed with the confinement method,

and the ones of the lowest free energy will be validated by comparison with experimentally determined ligand

dissociation constants and residue pKa values.

Aim 2. To design peptide analogues that inactivate the EmrE transporter by preventing dimerization. Because

three transmembrane helices from each monomer (TM1, TM2, and TM3) form the drug-binding site, and the

remaining two helices (TM4) make contact in the low-resolution structure, peptide analogs will be designed

to mimic TM4 as dimerization inhibitors resistant to proteolysis. The initial inhibitor model will be identical

to TM4, and the starting structures of the EmrE monomer-inhibitor complex will be taken from the all-atom

dimer determined in Aim 1. The peptide sequence will be optimized on the basis of the interaction energies

between the EmrE monomer and the peptide. Proteolysis-resistant peptide analogs will be modeled to have

similar shape and charge distribution as the highest-affinity peptides. In particular, all-hydrocarbon stapled αhelical peptides, whose synthesis has been worked out, will be used to replace the natural peptide. Opti-mum

sidechain distributions that lead to strong binding interaction with the EmrE monomer model will be

determined iteratively by MD simulations. The best candidate peptide analogs will be synthesized and tested

experimentally.

1 The Centers for Disease Control and Prevention. CDC: Action needed now to halt spread deadly bacteria[Press Release], 2013. URL http://www.cdc.gov/media/releases/2013/p0305_deadly_bacteria.html.

2 The Department of Health and Human Services is awarding GlaxoSmithKline up to $200 million to develop

new antibiotics. URL http://www.gsk.com/media/press-releases/2013/glaxosmithkline-awarded-up-to–200-

million-by-u-s–government-to.html

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Specific Aims Page 23
 
Figure 1: Left panel: Three multidrug transporters from E. coli coordinate efflux of drugs from the cytoplasm

into the medium (adapted from Ref. 1). Middle panel: Low-resolution Xray crystal structure of ligand-bound

dimeric 2 EmrE . The transport of aromatic drugs such as TPP+ is coupled to the influx of two protons.3 Transmebrane helices 4 (TM4), which appear to be important for dimerization,4,5 are shown in magenta. Right panel: A

hydrocarbon-stapled α-helical peptide (adapted from Ref. 6). The hydrocarbon staple is in red; in the notation

Xn , X refers to the stereochemistry at the α-carbon and n gives the length of the alkenyl chain.

Research Strategy

Significance

Antimicrobial multidrug resistance poses serious challenges in the treatment of many infectious diseases. A

ubiquitous mechanism by which multidrug resistance is conferred to bacteria involves the active efflux of a

broad variety of cytotoxic compounds by transmembrane multidrug resistance proteins.7 This efflux is driven

either by ATP hydrolysis 8(e.g. in the ABC-cassette superfamily of proteins), or by the electrochemical transmembrane potential.9 The multidrug transport complex AcrAB/TolC in E. coli provides a major drug extrusion

pathway from the periplasm (Fig. 1). There is strong evidence, however, that the AcrAB/TolC system cannot

confer significant resistance to compounds such as ethidium and acriflavine without single-component ‘suppliers’ (e.g. MdfA or EmrE) that move aromatic compounds into the periplasm from the cytoplasm.1 The various suppliers have overlapping specificities, and collectively form a multidrug resistance network of proteins

that renders bacteria effectively immune to a broad range of compounds.10–12 The small multidrug resistance

(SMR) family of membrane-bound transporters is ubiquitous in bacteria, including the pathogens M. Tuberculo9,13,14 sis, P. Aeruginosa, B. Pertussis, N. Meningitis, B. Anthracis, S. aureus among others, but continues to receive

less attention than other transporter families (e.g. ABC cassette proteins). Like many bacterial multidrug transporters (e.g. AcrB and MdfA), SMR proteins use the electrochemical potential of proton influx to actively pump

toxic compounds from the cytoplasm. SMR pumps confer resistance to a wide variety of quarternary ammonium compounds (QACs),15 some of which are used as disinfectants (e.g. benzalkonium chloride), and also promote resistance to antibiotics such as ampicillin, erythromycin and tetracycline. 16,17 SMR genes are often found

on plasmids and transposons that contain other genes responsible for antibiotic resistance (to, e.g., β-lactams,

cephalosporins and dihydrofolate inhibitors,13,18) implying that the most dangerous antibiotic-resistant bacteria also tend to be resistant to common disinfectants. Although the E. coli EmrE multidrug transporter from

the SMR family has been the subject of experimental studies (see Ref. 1 for a review) atomic-level structural

information that is sufficient for a rational design of SMR inhibitors is not available. Such structures have been

very difficult to obtain by experimental methods alone because of the generally high mobility of SMR proteins

inside the lipid membrane. Recent studies of EmrE have produced low-resolution structures, which show that

the 110 residue EmrE protein is a loose four-helix bundle with an active site formed by the active dimer.2,19,20

On the basis of a low-resolution Xray structure,2it was proposed that certain synthetic peptides could disrupt

EmrE dimerization, inhibiting its activity.5 Although a transmembrane helical fragment of EmrE was indeed

found to reduce the rate of drug efflux, the reduction was moderate (60%) and disappeared after ∼20 min due

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 24
 
to peptide degradation (an effective D-peptide resistant to proteolysis was not reported in the study). In the

proposed research, molecular simulation tools will be used to carry out rational in silico design of SMR inhibitor

peptides. In the first stage, low resolution X-ray crystal structures of the EmrE transporter,2 data from cryoelectron microscopy (EM)19,20 and from NMR solution experiments,21 will be used together with recent developments in physics-based computer modeling of protein22,23 and membrane thermodynamics 24–26 to construct

and validate an atomic-resolution model of dimeric EmrE inside the lipid membrane and in complex with various drugs. Although a computer model of EmrE was published in 2004,27 it was found to be in conflict with

subsequent EM, Xray and NMR data.2,21,28 In the second stage, the dimeric structures will be used to create starting models of EmrE-peptide inhibition complexes. The models will be optimized using directed in silico residue

mutagenesis to generate peptides inhibitors with the highest affinity for EmrE. The optimized peptides will

be stabilized to resist proteolysis using all-hydrocarbon staples,

6 and the optimal models will be tested experimentally. Stapled peptides have high α-helical propensities,29 and have been used to disrupt protein-protein

interactions important for the progression of cancer.6,30–32 Because of the ubiquity of SMR proteins in bacteria,

high-affinity peptide-analog inhibitors have the potential to be effective against a broad range of pathogens,

especially in combination with antibiotic drugs. The structure of EmrE has similarities to the internal repeats

in larger membrane proteins, such as aquaporins 33 and neurotransmitter transporters,34 suggesting how the

latter proteins may have arisen via gene duplication and mutation. The results of the proposed modeling study

are therefore expected to be of general interest, and to be relevant for other membrane proteins as well.

Innovation

The novelty of the proposed work is that it brings to bear the state of the art protein and peptide modeling

methodology, some of which was developed in the applicant’s laboratory, to produce a high-resolution model

of a small flexible membrane protein, the EmrE multidrug transporter, and to use it for the design of proteolytically stable peptides as inhibitors. For highly mobile small membrane proteins, experimental methods such as

X-ray Crystallography and Nuclear Magnetic Resonance (NMR) have been unable to determine atomic-level

structures. A rigorous computational methodology is proposed here to generate and analyze high-resolution

structures of the EmrE transporter using as the starting point existing low-resolution Xray2structures and cryoEM19,20 models. Although various structure prediction and refinement programs have been used before to

predict optimal protein confirmations,35–39 ensembles of structures are needed to understand the function of

membrane proteins 40,41 as well as other large biomolecules.42 The use of extensive equilibration by Molecular

Dynamics (MD) simulations of membrane proteins in a realistic lipid environment,26 with enhanced sampling

methods that preserve the canonical ensemble (REMD43 or RLES44) is expected to produce high-quality sets of

atomistic structures that exist in the membrane environment. Indeed, the increasing accuracy of MD simulations has recently been demonstrated with the in silico folding of small proteins.45 Additionally, to the knowledge of this applicant, validation based on free energy simulations to rank the modeled structures, has not been

performed previously.

Motivated by recent experiments,4,5 we propose to design peptide inhibitors of EmrE dimerization. Although

peptides and peptide analogs have been used previously to disrupt dimerization in membrane proteins (the

E. coli aspartate receptor46) as well as to inhibit HIV cell entry47 and to disrupt protein-protein interactions in

tumor cells,6,30–32 this is the first rigorous computational proposal to design peptide SMR inhibitors. Because

SMR transporters are widely distributed in bacteria, and because they appear to have a conserved dimerization

motif5,13,48 such inhibitors promise to be effective against many pathogens, especially in combination with other

therapies.

Although the computational approach outlined here is challenging and involves extensive computer simulations, recent advances in the efficiency of molecular dynamics (MD) simulation of proteins (e.g. using Graphical

Processing Units [GPUs]49–51) and in protein structure prediction methods 37–39 make computational modeling

an important tool to supplement experimental studies. This applicant has contributed to the foundation of MD

simulations in biology, has decades of experience with the computational methods to be used in the study, and

continues to oversee the development of novel simulation methodologies.22,23,52,53 Thus, the proposed study,

in spite of its novelty, has an excellent chance of success in the two-year time period. Moreover, the inhibitor

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 25
 
peptide analogs designed in silico will be synthesized and tested by experimental collaborators. In particular,

the laboratories of Dr. Gregory Verdine (Harvard University) and Dr. Charles Deber (University of Toronto)

have agreed to synthesize the designed peptides and to carry-out efflux inhibition assays (letters of support

attached).

Approach

This section is organized to address sequentially the specific objectives of the research proposal. As indicated in

the timeline (provided at the end), the approach is based on the expectation that the design of several peptide

inhibitor candidates will be completed in less than two years. At least some of these candidates will be synthesized and tested within the two-year period by our experimentalist collaborators. Computational modeling

software programs will be used to create and validate the atomic resolution models of the EmrE transporter

protein. Many of these programs were developed by the applicant and his co-workers and former students. In

particular, the CHARMM program,52,54 a workhorse for the development and application of many biomolecular simulation methods, is under continuous development in the applicant’s laboratory.

Aim 1. Preparation, refinement and validation of high-resolution structures

The modeling strategy proposed for this Aim is based on the emerging realization55–57 that traditional structure

prediction methods for proteins can be combined with limited experimental information and with new molecular simulation methods to produce high-quality atomic structures. In the planned research, we will make

use of low-resolution Xray structures that do not include coordinates of the residue sidechains. 2 Trial sets of

sidechain coordinates can be generated with several promising algorithms,37–39 and further refined by molecular dynamics simulations using restraints to the electron density map.56,58,59 The best structures obtained from

this procedure will be taken as the starting point for free-energy calculations of substrate binding, and for MD

simulations of the conformational change associated with drug transport.

1a. Computer side-chain modeling and refinement.

I). Two low-resolution ∼4Å structure of dimeric EmrE complexed with Tetraphenylphosphonium (TPP+)

are available from the Protein Data Bank (PDB accession codes 3B5D and 3B62).2 Only Cα carbon atoms

are provided at the low resolution. The remaining structure with PDB code 3B61 in the absence of ligand

will not be used because the packing of transmembrane helices appears unrealistic. Starting from the Cα

coordinates, coordinates of the protein backbone will be generated using the program CHARMM. 52,54

Since most of the structure is α-helical (as expected for membrane proteins), the backbone coordinates

will be generated by specifying the φ, ψ, and ω dihedral angles, which can be done using the internal

coordinate facility in CHARMM. Additional restraints on the φ and ψ dihedral angles will be applied

as needed to keep the corresponding values within the allowed Ramachandran region. The resulting

backbone-only models will be energy-minimized using a specially adapted CHARMM energy function. 60

II). The backbone-only models will be the starting point for generating coordinates of the residue sidechains.

Several algorithms that have been developed for sidechain modeling will be used to generate initial trial

models of complete structures. These are SCWRL,37 OPUS-Rota,61 Self-Consistent Mean Field theory

(SCMF),35,62 and ROSETTA39. We note that modeling tools that rely on homology modeling cannot be

used because there are no appropriate homologs of EmrE with known atomic structures. The modeled

structures will be energy-minimized with the program CHARMM using an implicit membrane model to

simulate the effects of a lipid environment.24 Any chirality errors that arise in the modeling and minimization stages will be corrected at this stage.

1b. Molecular dynamics simulations for refinement of structures.

I). The modeled structures obtained in 1a will be inserted in pre-equilibrated patches of DPMC lipid bilayers using the CHARMM-gui interface.63 Although other membrane compositions may be used, EmrE is

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 26
 
known to be functional in DPMC membranes.1 Explicit water molecules and ions will be added to the

protein-membrane complex to mimic the cytoplasm and the periplasm.

II). MD simulations to refine the structures will be carried out according to the protocols listed below.

(i) Equilibrium MD simulations will be carried out with the program AceMD50 using Graphical Processing Units (GPUs). The anticipated size of the simulation systems is ∼50,000 atoms, and the expected speed

of the MD simulation is 30 nanoseconds per day. Several hundred nanoseconds will probably be required

to reach steady-state behaviour of the simulation system.

(ii) Temperature replica exchange MD simulations 43 will be carried out using the program NAMD.64

(NAMD is significantly faster than CHARMM for straightforward MD but more limited in terms of simulation options.) Simulations will be conducted at several temperature values ranging from 300K (physiological) to about 600K. Only the physiological (300K) simulations will be used for structure evaluation;

the higher-temperature simulation exchange simulation coordinates with the 300K systems, so as to accelerate the effective rate and search capabilities of the MD simulation.

(iii) Locally Enhanced Sampling (LES)65,66 simulations will be performed with the program CHARMM

using implicit solvent representation for the membrane and cytoplasm24, and with the program NAMD

with explicit solvent. The use of variable restraints allows the transformation of the artificial replicated

LES ensemble into the correct ensemble at the physiological temperature.44

1c. Evaluation of refined and equilibrated structures. After the structures simulated by MD in the previous

step attain steady state (i.e. when the total energy and coordinates are fluctuating randomly in the vicinity

of the corresponding averages in response to the thermostat), the following procedure will be used to

determine the optimal structures.

(i) The coordinates will be checked to determine whether they fit into the experimental electron density

(EM) map.28 In the case that the coordinates do not fit completely, additional MD simulations with restraints to the EM map will be performed.56,58,59 Final structures whose coordinates are consistent with

the EM map will be retained for further analysis.

(ii) The structures will be removed from the explicit lipid membrane environment and placed into an implicit membrane model24,25 in CHARMM. The removal of explicit lipid and water molecules simplifies

the separation of the free energy of protein solvation from the intramolecular free energy of the protein.

The solvation free energy (FE) of each structure is provided by the implicit solvent model, and the configurational free energy of the protein structure will be estimated from a confinement analysis.53

(iii) In case that several distinct structures have very similar FE values, all of them will be retained for

further validation and analysis (1d and 1e below).

1d Free energy simulations of ligand binding to EmrE. The mechanism by which EmrE confers drug resistance involves active extrusion of aromatic drug compounds from the E. coli cytoplasm in exchange for

two protons.1 At the atomic level, the active site EmrE dimer alternately binds two protons and a drug

molecule (e.g. TPP+, or Ethidium). To achieve drug efflux from the cytoplasm, the affinities of EmrE for

the drug molecules (reported in experiments as dissociation [KD] constants2,67,68) have to be tuned to the

cross-membrane pH gradient. To establish the physiological relevance of the structures modeled in step

1, we will use free energy simulation to compute the FE of ligand binding to EmrE.

I). Coordinates of the ligands TPP+2 and ethidium69 will be submitted to CHARMM-CGENFF server70 to

generate force field parameters, which are necessary to perform energy calculations. The server produces

trial parameters automatically which can be improved iteratively using alternating quantum and molecular mechanical calculations. This is a standard procedure in computational chemistry, and will be carried

out according to the CHARMM parametrization protocol.60,70

II). Using the ligand force field parameters obtained in step I and the binding pose of the TPP+ ligand

found in the low-resolution structure 2, the TPP+ and ethidium ligands will be reversibly “annihilated” using alchemical FE simulation.71–74 The FE calculations will be performed with the MD program NAMD, 64

and the analysis and postprocessing of simulation data will be done using the Visual Molecular Dynamics

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 27
 
(VMD) software.75 The alchemical annihilation simulations will be performed both in the active site of

EmrE, and in the aqueous and lipid solvent environments to compute the FE difference of ligand binding.

1e Determination of the pKa’s of the amino acid residues involved in drug transport. Mutagenesis and

pH titration experiments have established that residue Glu14 is essential for proton-coupled transport

of EmrE substrates.76–78 Using Poisson-Boltzmann solvation theory,79–81 we will estimate the free energy

difference between protonated and deprotonated Glu14 of both monomers in the dimeric structure, and

compare the value with that obtained from pKa values in the literature (7.3 – 8.5). In addition, the pKa

of the Asp-14 mutant will be calculated and compared with the experimental value (5.6). For further

validation, the more computationally expensive alchemical FE simulations will be performed to compute

the pKa shifts inside the protein relative to the pKa in solution. Specifically, Glu-14 will be alchemically

protonated both the protein and in the solvent; the FE difference between the two processes is proportional

to the pKa shift. The pKa calculations are a particularly stringent test of the quality of the structure of the

active site, because the large experimental deviation of the Glu-14 pKa inside the EmrE drug binding

site (∼ 8) from that in bulk solvent (∼ 4.5) requires a correct and precise arrangement of the neighboring

residues.

Aim 2. Design of peptides for the inhibition of dimerization

In the low-resolution Cα dimer structure of EmrE, transmembrane helices 4 (TM4) are in close proximity for

direct interaction2(see Fig. 1). In this Aim, proteolysis-resistant peptide analogs will be designed to mimic TM4

(sequence 84DLPAIIGMMLICAGVLIINLLS105 ) for use as dimerization inhibitors.

2a Initial model of inhibition peptide. I). Two EmrE-peptide models will be obtained from the optimal

all-atom dimer model obtained in stage 1. In the first model, all parts of the first EmrE monomer except

transmembrane helix 4 (TM4) will be deleted. In the second model, all parts of the second monomer

except TM4 will be deleted. The two models are expected to be structurally close but not identical and

will provide better simulation statistics than a single model. Partial deletion of one of the monomers will

require re-equilibration of the lipid membrane around the monomer-peptide complex, which will be done

by MD simulations, as described in 1b above.

II). An initial 100ns long MD simulation will performed using each of the starting structures to collect

statistics of the interaction energy between the peptide and the monomer, and between the peptide and

the lipid membrane. The MD calculations will be performed with the program AceMD50 in parallel using

a dedicated GPU for each calculation. An additional MD simulation of the single TM4 peptide embedded

in the lipid bilayer will be performed for 100ns. This simulation will be used to normalize interaction

energies, as described in the next step. The MD simulation trajectory with the lowest average interaction

energy will be the starting point of the directed in silico mutagenesis optimization performed in step 2b.

2b Optimization of peptide sequence. The optimization protocol will use Monte Carlo sampling as follows. A

peptide residue with a side chain atom within a cutoff distance d from the EmrE monomer will be se-lected

at random. The distance d will be initially set to 3Å, but will be tuned to reduce the computational

complexity of the problem by selecting the residues that are expected to make the largest contributions

to the binding affinity (measured by the interaction energy). The selected residue will be mutated randomly to a nonpolar residue (i.e. selected from AFGILMPV) and the MD simulations of the EmrE-peptide

complex and of the peptide alone embedded in the lipid will be repeated for 100ns. The average interaction energy between the mutated peptide and the EmrE monomer and membrane will be computed and

compared to the corresponding interaction energy from the previous simulation. Because the binding

of inhibition peptides occurs in the membrane, the interaction energy between the peptide and the lipid

molecules must be taken into account for the free and the bound peptide. The change in the interaction

energy between the peptide and the membrane will be computed from the MD trajectories of the mutated

and the original peptides, both simulated in the membrane. The essential interaction energy differences

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 28
 
will be calculated based on the equation

∆ E = E complex 

mutated− Ecomplex

original − (Epeptide mutated − E

peptide original ), (1)

where Ecomplex denotes the average interaction energy between the peptide and the EmrE monomer and

membrane, Epeptide denotes the average interaction energy between the peptide and membrane in the

(normalization) simulation without EmrE, and the subscripts original and mutated refer to the structure of

the peptide before and after the trial mutation. The quantity ∆E will be used in a Metropolis Monte-Carlo

(MC) acceptance-rejection test, in which the probability of accepting the trial mutation is computed as

Paccept =

1 if ∆E ≤ 0

exp(−β∆E) if ∆E > 0,

(2)

where the quantity β (inverse temperature) will be varied to explore different peptide compositions, increasing from the physiological value at 300K β=1/(kBT ) ≃1.7(kcal/mol)−1

; higher temperature (T ) values will be used to increase the range of sampled mutations. If the Monte Carlo move is accepted, the

new structure becomes the ‘original’ structure; otherwise the new structure is discarded. The above procedure is repeated until the inhibitor peptide composition does not change after a specified number of

trial moves.

The above procedure uses the interaction energy differences instead of the free energy differences. However, the difference between the enthalpy and the free energy (the entropic contribution) to the binding

free energy difference is expected to be small because the mutations are not likely to change significantly

the flexibility of the binding interface and the calculations involve the differences between the bound and

free helix. To quantify the error of neglecting the entropy contribution, we will compute the binding

free energy and enthalpy difference resulting from a trial mutation use confinement analysis 53,82 with an

implicit membrane model.24,25

2c Construction of peptide analogs. The EmrE-peptide complexes with the lowest interaction energies

from step 2b will be the starting point for constructing proteolysis-resistant peptide analogs by adding

all-hydrocarbon peptides ‘staples’ (see Fig. 1). Stapled peptides appear to be ideal candidates for the

present problem because of their generally high α-helical propensity,29,83 resistance to proteolysis,6 and

hydrophobicity of the staple, which is compatible with a high membrane permeability. 83 Successful design of a stapled peptide requires that the staple does not interfere with the intended contact interface

between the peptide and the protein. For this reason, only those trial staple positions will be considered

that are away from the binding interface. On the basis of the low-resolution experimental structure of

EmrE,

2

the following positions in TM4, indicated in bold, appear to be well suited for the placement of a

staple: 84DLPAIIGMMLICAGVLIINLLS105 (conserved residue positions are underlined). However, optimal positions will be determined from the model in step 2b. All residue pairs separated by 3, 4 and 7

positions will be considered for stapling (subject to the above selection constraint). The displacements

correspond to one or two turns of the α-helix (see e.g. Fig. 1 for a two-turn staple). The sidechains in the

chosen residues will be replaced with the appropriate hydrocarbon staple,6,29 and the stapled peptide will

be simulated by MD in complex with EmrE and also in isolation from EmrE, as described in step 2b. The

stapled peptide candidates will be ranked on the basis of the interaction energy differences between the

peptide and the protein, and peptide and lipid, as described in step 2b, as well as the α-helical propensity

of the peptide.29 The highest scoring stapled peptides will be retained for experimental characterization.

Project Timeline

In the first year, most of the effort will focus on the development of the atomic-resolution EmrE structure. We

anticipate that approximately six months will be required to produce several refined structures (Aims 1a–1c).

The remaining Aims 1c-1d (validation of the refined structures) are expected to require an additional four to six

months. The remaining time (up to 1 year) will be devoted to the optimization of the peptide sequence and of

the peptide staple position (Aim 2), which will involve iterative interactions with the experimental groups.

 

Principal Investigator/Program Director (Last, first, middle): Karplus, Martin 

Research Strategy Page 29
 
References

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10.1016/j.bbapap.2008.12.018.

[2] Yen-Ju Chen, Owen Pornillos, Samantha Lieu, Che Ma, Andy P Chen, and Geoffrey Chang. X-ray structure

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