Eric Hajjar permeation properties of antibiotics through porins - - PowerPoint PPT Presentation

CNR-SLACS: Sardinian LAboratory for Computational Materials Science University of Cagliari, Dept of Physics

Eric Hajjar, Amit Kumar, Enrico Spiga, Francesca Collu, Atilio Vargiu, Paolo Ruggerone and Matteo Ceccarelli

How molecular simulations can help understand permeation properties of antibiotics through porins Towards ’Antibiotic in-silico drug design’ methodology and proof of concept.

Eric Hajjar

CNR-SLACS: Sardinian LAboratory for Computational Materials Science University of Cagliari, Dept of Physics

Successful stories using this methodology; methods, presentation of previous and novel results.

Amit Kumar,

Thursday talk

• Molecular simulations of cephalosporins diffusion through OmpF
• Searching for similar patterns on the translocation of fluoroquinolones through OmpF
• Methodologies and perspectives in the simulation of antibiotics translocation
• Molecular Simulation of Penicillin's Diffusion through Bacterial Poring OmpF
• Kinetic monte carlo study of translocation

Wednesday talk 5 POSTERS

Study of a common antibiotic in wild type and a natural strain of OmpF mutation  identification of the determinants for translocation.

• 1. Introduction / Biological problems
• 2. Methods / Theoretical solutions

Outline

• 5. Conclusions, Perspectives, Work in progress

From classic Molecular Modeling to advanced Metadynamic algortithm  Our strategy

• 4. Extending methodology to cephalosporins
• 3. Antibiotic in silico drug design / Proof of concept

Focus on Gram-negative bacteria:

• Pathogenic for humans, increased antibiotic resistance
• Outer Membrane rich with lipopolysaccharides (LPS) and porins.

inhibit growth of bacterias cell wall

BACTERIA ANTIBIOTICS

Biological Problem

resistance

Many ways for Bacteria to resist to Antibiotics

• Production of B-lactamases
• Under-expressing porins
• Over-expressing efflux pumps

"The global increase in resistance to antimicrobial drugs has created a public health problem of potentially crisis proportions." American Medical Association (AMA):

Problem of Bacterial resistance to Antibiotics

Need of a new way to design antibiotics focus on the molecular basis of antibotic transport and bacterial resistance. / a bottom-up approach:

• Mutating porins to affect antibiotic uptake

Antibiotics are transported in bacteria via Outer Membrane Proteins

• OmpF and OmpC are the most abundant in Gram-negative bacteria
• Have been thoroughly investigated by many techniques
• General diffusion proteins (poor substrate selectivity, often open)

Antibiotics have to diffuse passively through some general porins at the

• uter membrane

OMPF described in literature as ”general diffusion protein”

OmpF: X-ray structure in 1992. View on OmpF monomer:

• Beta Barrel, 8 loops which are extracellular

...except L3: folds back inside Constriction region with a few described important residues.

Is OmpF just a ”tube channel” ?

a) There is a constriction region: with the L3 that folds back inside (radius ~ 7Å) b) There is a particular electrostatic field, above / at / bellow the constriction region ! Z-slices of the electrostatic potential

Is OmpF just a ”tube channel” ?

a) There is a constriction region: with the L3 that folds back inside (radius ~ 7Å) b) There is a particular electrostatic field, above / at / bellow the constriction region !

Maybe not

Justify the approach: focus on the molecular basis of antibotic transport, to design antibiotics with improved permeation properties

In this rational drug-design scenario molecular simulations can have an important role.

A molecule is: composed of atoms and springs between them

Energy: any arrangement of atoms and molecules in the system E ~ f (atomic positions)

Molecular modeling

Broad range of computational methods with associated analysis tools

θ éq θ θ éq θ

réq réq r φ

The potential energy function (E) is a sum of terms

• Strategy:

Calculates the time dependent behavior of a molecular system

F =ma=m. dv dt =m. d 2x dt2

F =−dE dr

• Result: trajectory, specifies how the positions of the atoms vary with time

Molecular Dynamic (MD)

Calculate forces on each atom Iterative integration of Newton’s laws (over dt).

Energy landscapes

As the size of the molecule increases:

The case of butane

bigger steric effects; more complex energy landscape

Simple (trivial) energy landscape Advanced (realistic) energy landscape

and

in search for the minum in energy The most stable conformation of a molecule is the one with the lowest energy

In our case we want to use computational simulations to study ANTIBIOTICS translocation through PORINS

Antibiotics: Our Systems of study The Porin systems:

• Wild Type OmpF (and OmpC in preparation)
• OmpF-variants:

R42A, R82A, R132A, D113A, D113N, E117A. according to litterature and discussions with partners different famillies with different properties

Thursday’s talk

Cephepime Cefpirome,

cephalosporins

Ceftizoxime Ampicillin Penicillin-G Carbenicillin

penicillins fluoroquinolones

Ciprofloxacine Norfloxacine Moxifloxacine Enrofloxacine Cefpodoxime Cefetamet Thursday’s talk

Antibiotics of different charge, size, hydrophobicity...interest !

LIMITATIONS of MD for studying antibiotic translocation

The case of Antibiotics passage through porins

• Experimentally, from electrophysiology experiments on ‘BLM’, the time of

this process is ~100 µs (Winterhalter & Bezrukov).

• This time exceeds typical simulations time

Our strategy to overcome this problem: Accelerated Molecular Dynamic (MD) simulations

Using MD, free energy barriers are difficult to cross

~ the process is classified as rare event Reaction Coordinates Reaction Coordinates

METADYNAMICS (Laio and Parrinello, PNAS 2002)

Very efficient method to sample free energy landscapes / accelerate evolution of the system

Construct a bias potential that discourages the system from revisiting configurations that have already been explored. Free Energy landscape is being filled up by metadynamic

Accelerated Molecular Dynamic

Thursday’s talk

Building Complexes

Simulations of antibiotic translocations

Solvation

(cubic pre-eq. water box, counter ions)

Analysis Metadynamic run

Quantitative: free energy landscape of translocation process Qualitative: Inventory of the interactions, area, atomic fluctuations.

+

with proper reaction coordinates (samples millions of conformations) OMP antibiotic Monomer / Trimer Wild Type / Mutants 4 CPU ~ 40 days FF field parametrization detergent molecule / lipid bilayer

Start with a common antibiotic study its tranlocation through OmpF WT and Mutants Propose a better antibiotic in accordance with the findings Verify / Extend the hypothesis 1 2 3

III) Proof of concept

Towards in-silico design of antibiotics

Ampicillin translocation through OmpF

Contour plot of the free energy surface (FES) for the Amp-OmpF WT simulation

Highly populated (deep) minimum in energy at Z {-1:1) and Θ {120:160}. The energy (ΔG) to overcome this barrier is ~8kcal.

Constriction zone

Thursday’s talk

Visual Analysis ?

 complicated and untractable to analyse trajectories with the eyes

 We used various computational methods

Inventory of interactions

Some ways of quantifying the translocation process

Cross sectional area calculation

Thursday’s talk Simulation time Atoms of Antibiotic

I II III IV V VI

• Quantify the barrier between each minima,
• Launch standard equilibrium MD for each of them

Identify the conformations corresponding to the free energy minima

extract minima

Conformations along MD simulations of Minima’s

D F At constriction region Strong binding site D113 Hbonds to antibiotic / slows down escape D113 induce repulsion

Simulation along Mini-I&II along Mini-III & IV along Mini-V

above constriction region At the constriction region below constriction region

Amp- OmpF WT

The N+ group of Amp slows down its diffusion due to interactions with D113

III) Proof of concept

Towards in-silico design of antibiotics Start with a common antibiotic study its tranlocation through OmpF WT and Mutants Propose a better antibiotic in accordance with the findings Verify / Extend the hypothesis 1 2 3

• Amp successfully translocate through OmpF-D113A
• A single mutation changes drastically strength and localization of

minima on the FES

Simulations of Ampicillin with OMPF-D113A

WT D113A

Amp find the proper configuration to translocate faster as there is no repulsion with A113.

Conformations along MD simulations of Minima’s

stronger hydrophobic interactions with the porin. above constriction region At the constriction region below constriction region Quickly and passively translocate

III) Proof of concept

Towards in-silico design of antibiotics PenG, lacks the Nterm + group The N+ group of Amp slows down its diffusion due to interactions with D113 Start with a common antibiotic study its tranlocation through OmpF WT and Mutants Propose a better antibiotic in accordance with the findings Verify / Extend the hypothesis 1 2 3 The mutation D113A strengthen the hydrophobic character which helps diffusion

along standard MD of Mini-II

Above the constriction region;

• PenG avoids the repulsion (of its COO-) with

D113 as it adopts a particular “folded” structure.

Translocation of PenG is optimal !

 Importance of the flexibility of the antibiotic

 Does not require interactions on Nterm side, but is directed by its COO-

along standard MD of Mini-IV

Below the constriction region;

Translocation of PenG is optimal !

• PenG phenyl group is attracted by hydrophobic

pocket while COO- interact with basic cluster.

• PenG undergo a 180degree rotation, then quickly

translocate in this orientation with this phenyl down.

 Importance of the hydrophobicity of the antibiotic

map of the antibiotics interactions

– Importance of charge distribution

Outcome

Ampicillin

(N1) E117, E113, F118, G119 (O & OXT) R42, R82, R132, K16, Y40 (O5) R42, R82 (N3) E113 (O2) R168, K80, R82, R132, R168, R167, Y102 (O5)K80, R82, R132 (N3) E113 (O & OXT) S125, R42, R82, R132, K16

Penicillin-G

III) Proof of concept Towards in-silico design of antibiotics

Cephalosporins, (CFR) PenG, lacks the Nterm + group The N+ group of Amp slows down its diffusion due to interactions with D113

Start with a common antibiotic study its tranlocation through OmpF WT and Mutants Propose a better antibiotic in accordance with the findings Verify / Extend the hypothesis 1 2 3

Simulations of Cefpirome (CFR) translocation

I II III IV V VI VII

WT D113A Binding site at constriction region Presence of binding sites Below the constriction region

CFR-WT

• The antibiotic take advantage of a great flexibility to quickly slide down

D113 plays moderate role in the binding as CFR diffuse down CFR is now favorably interacting with two walls of hydrophobic pockets Below the constriction region Mini-V at the constriction region: Mini-VI Mini-VII

just above constriction region From there: CFR released far from basic cluster wall, escape much faster than WT...

CFR- OmpF(D113)

Strong central binding site Mutation enhance (hydrophobicity) the binding site above the constriction region Mini-VI Mini-VI and downward

• Being able to adopt a wide range of conformations

is an advantage to translocate.

• Several partners for making hydrogen bonds.
• Particular position of basic residues: ”a staircase for translocation”

Importance of Hydrophobicity:

• Several hydrophobic pockets found.
• Mutation D113A enhance hydrophobicity

~ opens a Hpocket at the constriction region, CFR enters there

Importance of Polarity: Importance of Flexibility:

Conclusions from our simulations ....(1/2)

• Our results refute presence of a single barrier and/or single binding site.

 There are several of them along the translocation path. Importance of the topology of the free energy surface:

Conclusions from our simulations ....(2/2)

 Characterize the nature, strength and localisation of each minima

• This work provide a rational basis for
• designing antibiotics with improved properties
• predicting antibiotic susceptibility
• interpreting experiments
• improving the model of antibiotic diffusion

Wolfgang R. Bauer, Bezrukov, our kinetic monte-carlo schemes...

Improve our quantitative and qualitative description of translocation:

• Add new reaction coordinates in the metadynamic algorithm
• Estimate and minimize the error in free energy.
• Effect of desolvatation
• Kinetic monte Carlo to estimate the barriers between minima’s

Work in Progress / Perspectives

Improve the comparison with experiments / synchronisation with partners

Thursday’s talk Electrophysiology-disagreement but Liposome swelling-agreement

One particular strength of the project: many partners towards same goal

(electrophysiology, swelling assays, fluorescence, drug design, antibiotic susceptibility)

Acknowledgments

TO ALL OF YOU 

Project No.: 19335 RTN ”Translocation”