University of Cagliari, CNR-SLACS: Sardinian Dept of Physics LAboratory for Computational Materials Science Eric Hajjar , Amit Kumar, Enrico Spiga, Francesca Collu, Atilio Vargiu, Paolo Ruggerone and Matteo Ceccarelli
University of Cagliari, CNR-SLACS: Sardinian Dept of Physics LAboratory for Computational Materials Science How molecular simulations can help understand Eric Hajjar permeation properties of antibiotics through porins Towards ’Antibiotic in-silico drug design’ Wednesday talk methodology and proof of concept. Successful stories using this Amit Kumar, methodology; methods, presentation of Thursday talk previous and novel results. 5 POSTERS -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
Outline 1. Introduction / Biological problems 2. Methods / Theoretical solutions From classic Molecular Modeling to advanced Metadynamic algortithm Our strategy 3. Antibiotic in silico drug design / Proof of concept Study of a common antibiotic in wild type and a natural strain of OmpF mutation identification of the determinants for translocation. 4. Extending methodology to cephalosporins 5. Conclusions, Perspectives, Work in progress
Biological Problem resistance BACTERIA ANTIBIOTICS inhibit growth of bacterias cell wall Focus on Gram-negative bacteria: - Pathogenic for humans, increased antibiotic resistance - Outer Membrane rich with lipopolysaccharides (LPS) and porins.
Problem of Bacterial resistance to Antibiotics American Medical Association (AMA): "The global increase in resistance to antimicrobial drugs has created a public health problem of potentially crisis proportions." Many ways for Bacteria to resist to Antibiotics -Production of B-lactamases -Under-expressing porins -Over-expressing efflux pumps -Mutating porins to affect antibiotic uptake N eed of a new way to design antibiotics / a bottom-up approach: focus on the molecular basis of antibotic transport and bacterial resistance.
Antibiotics are transported in bacteria via Outer Membrane Proteins Antibiotics have to diffuse passively through some general porins at the outer membrane •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)
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.
Molecular modeling Broad range of computational methods with associated analysis tools A molecule is: composed of atoms and springs between them Energy: any arrangement of atoms and molecules in the system E ~ f (atomic positions) The potential energy function (E) is a sum of terms r r éq r éq φ θ éq θ éq θ θ
Molecular Dynamic (MD) Calculates the time dependent behavior of a molecular system - Strategy : = m . d 2 x F = ma = m . dv F =− dE dt dt 2 dr Calculate forces on Iterative integration of Newton’s each atom laws (over dt). - Result : trajectory, specifies how the positions of the atoms vary with time
Energy landscapes The case of butane As the size of the molecule increases: bigger steric effects; more complex energy landscape
Advanced (realistic) energy landscape Simple (trivial) 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 Our Systems of study Antibiotics: different famillies with The Porin systems: different properties • Wild Type OmpF (and OmpC in preparation) • OmpF-variants: R42A, R82A, R132A, D113A, D113N, E117A. according to litterature and discussions with partners Thursday’s talk
Antibiotics of different charge, size, hydrophobicity...interest ! cephalosporins fluoroquinolones penicillins Cefpirome, Ampicillin Ciprofloxacine Cephepime Norfloxacine Penicillin-G Ceftizoxime Moxifloxacine Cefetamet Carbenicillin Enrofloxacine Cefpodoxime Thursday’s talk
The case of Antibiotics passage through porins • Experimentally, from electrophysiology experiments on ‘BLM’, the time of this process is ~100 µ s (Winterhalter & Bezrukov). • This time exceeds typical simulations time ~ the process is classified as rare event Using MD, free energy barriers are difficult to cross LIMITATIONS of MD for studying antibiotic translocation Reaction Coordinates Reaction Coordinates Our strategy to overcome this problem: Accelerated Molecular Dynamic (MD) simulations
Accelerated Molecular Dynamic METADYNAMICS (Laio and Parrinello, PNAS 2002) 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 Very efficient method to sample free energy landscapes / accelerate evolution of the system Thursday’s talk
Simulations of antibiotic translocations + OMP antibiotic Building Complexes Monomer / Trimer FF field Wild Type / Mutants parametrization detergent molecule / lipid bilayer Solvation (cubic pre-eq. water box, counter ions) 4 CPU with proper reaction coordinates Metadynamic run ~ 40 days (samples millions of conformations) Quantitative : free energy landscape of translocation process Analysis Qualitative : Inventory of the interactions, area, atomic fluctuations.
III) Proof of concept Towards in-silico design of antibiotics Start with a common antibiotic 1 study its tranlocation through OmpF WT and Mutants 2 Propose a better antibiotic in accordance with the findings 3 Verify / Extend the hypothesis
Ampicillin translocation through OmpF
Contour plot of the free energy surface (FES) for the Amp-OmpF WT simulation Constriction zone Highly populated (deep) minimum in energy at Z {-1:1) and Θ {120:160}. The energy (ΔG) to overcome this barrier is ~8kcal. Thursday’s talk
Visual Analysis ? complicated and untractable to analyse trajectories with the eyes We used various computational methods
Some ways of quantifying the translocation process Cross sectional area calculation Inventory of interactions Simulation time Atoms of Antibiotic Thursday’s talk
Identify the conformations corresponding to the free energy minima extract minima I II III IV V VI - Quantify the barrier between each minima, - Launch standard equilibrium MD for each of them
Amp- OmpF WT Conformations along MD simulations of Minima’s Simulation along Mini-I&II along Mini-III & IV along Mini-V At the constriction region below constriction region above constriction region F D At constriction region D113 Hbonds to antibiotic / Strong binding site D113 induce repulsion slows down escape
III) Proof of concept Towards in-silico design of antibiotics Start with a common antibiotic 1 The N+ group of Amp slows down its diffusion due to study its tranlocation through interactions with D113 OmpF WT and Mutants Propose a better antibiotic in 2 accordance with the findings 3 Verify / Extend the hypothesis
Simulations of Ampicillin with OMPF-D113A WT D113A • Amp successfully translocate through OmpF-D113A • A single mutation changes drastically strength and localization of minima on the FES
Conformations along MD simulations of Minima’s above constriction region At the constriction region below constriction region Quickly and passively translocate Amp find the proper stronger hydrophobic interactions configuration to translocate with the porin. faster as there is no repulsion with A113.
III) Proof of concept Towards in-silico design of antibiotics The N+ group of Amp slows Start with a common antibiotic 1 down its diffusion due to interactions with D113 study its tranlocation through OmpF WT and Mutants The mutation D113A strengthen the hydrophobic character which helps diffusion Propose a better antibiotic in 2 accordance with the findings PenG, lacks the Nterm + group 3 Verify / Extend the hypothesis
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