Вычислительный эксперимент в молекулярной биофизике белков и биомембран
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Вычислительный эксперимент в молекулярной биофизике белков и биомембран



Вычислительный эксперимент в молекулярной биофизике белков и биомембран ...

Вычислительный эксперимент в молекулярной биофизике белков и биомембран
Ефремов Роман Гербертович, доктор физико-математических наук, профессор, заместитель директора по науке Института биоорганической химии имени академиков М.М.Шемякина и Ю.А.Овчинникова РАН, руководитель Лаборатории моделирования биомолекулярных систем



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Вычислительный эксперимент в молекулярной биофизике белков и биомембран Вычислительный эксперимент в молекулярной биофизике белков и биомембран Presentation Transcript

  • Russian Academy of SciencesM.M. Shemyakin & Yu.A. Ovchinnikov Institute of Bioorganic ChemistryNUMERICAL EXPERIMENT IN MOLECULAR BIOPHYSICS OF PROTEINS AND MEMBRANES: TASKS AND SOLUTIONS FOR PROTEOMICS Roman EFREMOV Laboratory of Biomolecular Modeling efremov@nmr.ru model.nmr.ru www.ibch.ru
  • The Team:Dmitry E. NOLDE Ph.D.Anton O. CHUGUNOV Ph.D.Anton A. POLYANSKY Ph.D.Pavel E. VOLYNSKY Ph.D.Darya V. PYRKOVA Ph.D. studentNikolai A. KRYLOV Ph.D. studentNatalya K. TARASOVA student Thanks: Joint Supercomputer Center, Russian Academy of Sciences;Computations: i-SCALARE: INTEL-MIPT Laboratory of supercomputer systems. Russian Academy of Sciences;Grant sponsors: Russian Foundation for Basic Research.
  • Department of Structural Biology Laboratory Laboratory of Biomolecular NMR of Biomolecular ModelingHead: Prof. Alexander Arseniev Head: Prof. Roman EfremovNMR spectrometers with CRYOPROBE: Access to high-performanceUNITY 600 (Varian) computational clusters (up to 140 TFlops)Aliance III 800, II 700, II 600 (Bruker) Laboratory of Optical Microscopy & Spectroscopy of Biomolecules Head: Dr. Alexei Feofanov TIRF-confocal microscope (Carl Zeiss) 4-pi microscope (Leica), etc.
  • OUTLINE• Why membranes? Why computational experiment?• Computational “kitchen”: models, methods, realization;• Examples of problems under study: 1. “mosaic” nature of biomembrane surface and its role in binding of amphiphilic peptides to cell membranes; 2. peptide-peptide interactions in membranes – accent on the peptide surface;• Problems, conclusions, perspectives;
  • Biomembranes as perspective pharmacological targets Up to 70% of currently marketed drugs act either on membrane proteins or on membrane itselfExamples of potential targets:- G-protein coupled receptors (GPCRs);- Transmembrane ion channels and transporters;- Integral MPs involved in oligomerization upon their functioning (receptor tyrosine kinases, apoptotic proteins, etc.);- Lipid bilayer of biomembranes (direct and indirect modifications of its properties can be vitally important for cell)
  • MODERN APPROACHES IN MODELING OF CELL MEMBRANES  Implicit (“hydrophobic slab”) models;  All-atom hydrated lipid bilayers and micelles;  Simplified continuous models (no microscopic details);  Simplified corpuscular - coarse grain (CG) - models.
  • Implicit membrane models: How do they look like? Method: Monte Carlo simulation with energy minimization Potential Energy Function: Intraprotein: (Némethy et al, 1983)Protein/Solvent : Esolv = ∑ σ i × ASPi i  ASPi  mem − 0.5 ⋅ ( ASPi mem − ASPi wat ) ⋅ e ( z −z ) / λ 0 , z < z0 (Efremov et al., 2000, ASPi ( z) =  −( z − z ) / λ  ASPi wat + 0.5 ⋅ ( ASPi mem − ASPi wat ) ⋅ e  0 , z ≥ z0 Nolde et al., 2000)
  • Full-atom membrane models DOPS bilayer SDS micelleParameters of the systems: 3D periodic boundary60-500 lipid (detergent) molecules conditions103 – 104 waters Molecular dynamics in NPT ensemble.Size of the box: 10·10·10 nm3.Polyansky A. et al. (2005) J. Phys. Chem. B 109:15052
  • Models of lipid bilayers mimicking different types of biomembranes Membrane “ERYTH” (288 lipids) Membrane “GRAMN” (288 lipids)The mimic of the human erythrocyte membrane The mimic of the cytoplasmic membrane of gram negative bacteria (E. coli) POPC – 40%, POPE – 40%, CHOL – 20% POPE – 70%, POPG – 30%
  • Coarse-grain (CG) models Main characteristics: Interactions: 1. LJ-potential 2. Electrostatic 3. Harmonic potentials for bonds and valence angles between CG-particles Some parameters of MD protocol: Cutoff/smoothing functions (~12Å ) Integration time ~10-50 fs(Lopez et al, 2006) Gain in the computational time: 3-4 orders of magnitude!
  • Self-assembling of DPPC bilayer 128 DPPC / 2000 x4 H2O Т = 323 К Trajectory: 5 x 4 ns CPU: AMD Athlon 64 x2 3.8 ГГц Performance: 16 x 4 ns/hour (on 1 core) Gromacs 3.31 L-J: 12 Å (switch) / El. 12 Å (shift) CG-model: water, choline, phosphate, glycerol, aliphatics Resulting area per lipid molecule - 62 Å
  • CG-models: equilibrated lipid bilayer – for 1 day!180PE/76PG/2500W/76Na+ <AL>=62.1 Å2 <Dpp>=39.1 Å2 Polyansky et al. <Scd >DOPE=0.140±0.059 J.Phys. Chem B, 2005 <Scd >DOPG=0.144±0.064 averaging: last 0.25 ns
  • Systems / computational details Molecular hydrophobicity potential (MHP): Mapping of hydrophobic properties of surfaces of helical peptides.logP=log(Co/Cw) logP 1=f1+f2+f3 logP 2=f1+f2+f3+f4 ……………………………… Efremov R. et al., (1992). J. Prot. Chem. 11: 699 Efremov R. et al., (2007). Curr. Med. Chem. 14: 393 МНР-calculations were done with the program PLATINUM http://model.nmr.ru/platinum Pyrkov T. et al. (2009) Bioinformatics, 23: 2947
  • Hydrophobic properties of surfaces of hydrated lipid bilayers in atomistic presentationDistribution of molecular hydrophobicity potential on the Parameters of the systems:membrane surface 60-500 lipids; > 0 (Hydrophobic regions) 103 – 104 waters; < 0 (Hydrophilic regions) Cell dimensions: 10·10·10 nm3.
  • What properties of a molecular surface might be of interest?• Distribution of hydrophobic/hydrophilic regions;• Distribution of electrostatic potential;• Landscape (“ridges”, “canyons”, and “plains”);• Characteristics of conformational flexibility of a molecule (e.g., obtained in molecular dynamics simulations); Northern coast of the Black Sea, Таman, and Eastern Europe. Меrcator’s, “Atlas”, 1584. Re-edition of the map (dated 1513) based on ideas by Claudius Ptolemaeus.• Location of particular groups of atoms and residues, active sites, interfaces of intermolecular interactions, and so on; Upon employment of common format of surface mapping, differential, averaged, etc. maps become very important. Often, necessary information is obtained in the result of сomparative analysis of different types of surface maps (e.g., hydrophobicity/landscape, and so on)
  • Example:“mosaic” nature of biomembrane surface and its role in binding of amphiphilic peptides to cell membranes
  • Mosaic nature of lipid bilayers Membranes are more mosaic than fluid  large complexes of proteins  lipid raftsEngelman, Nature, 438, 2005  heterogeneous distribution of lipids of different chemical structure Complicated character of interactions between the membrane and various peripheral agents !
  • Lateral heterogeneity in lipid mixtures A «simple» system: 2-component lipid bilayer DОPC DPPCDOPC 288 -DOPC90 258 30DOPC80 230 58DOPC70 200 88DOPC50 144 144 Dioleoyl-phosphatidylcholine (DOPC)DOPC30 86 202DOPC20 58 230DOPC10 30 258DPPC - 288 Dipalmitoyl-phosphatidylcholine (DPPC)
  • Mosaic nature of lipid bilayers Structural parameters of the lipid bilayers under study AL (Å2) * DPP (Å) ** расчет Calc. эксп. Exp. расчет Calc. эксп. Exp. ДОФХDOPC 71.7 +/- 0.1 72.5 35.8 +/- 0.1 36.9 ДОФХ90DOPC90 66.4+/- 0.1 37.9+/- 0.1 exp. data ДОФХ80DOPC80 66.1+/- 0.1 37.8+/- 0.1 linear approximation ДОФХ70DOPC70 65.6 +/- 0.1 37.8 +/- 0.1 exp. data ДОФХ50DOPC50 64.2 +/- 0.1 37.7 +/- 0.1 MD data ДОФХ30DOPC30 63.9 +/- 0.1 37.2 +/- 0.1 ДОФХ20DOPC20 62.2+/- 0.1 37.9+/- 0.1 ДОФХ10DOPC10 61.9+/- 0.1 37.6 +/- 0.1 ДПФХDPPC 61.4 +/- 0.1 63.3 37.6 +/- 0.1 38.3 Pyrkova D. et al. (2011) Soft Matter, 7: 2569 * AL - area per lipid molecule; ** D - bilayer thickness. PP• DPP values in mixed bilayers are close to those in “pure” DPPC;• AL in mixed bilayers decreases with increasing of DPPC concentration.• Small amounts of saturated lipid induce large changes in structuralparameters of bilayer composed of unsaturated lipids.
  • Mosaic nature of lipid bilayers Hydrophobic / hydrophilic organization of surfaces in model lipid blayers DOPC DOPS hydrophilic hydrophobicDOPC – dioleoyl-phosphatidylcholine (zwitterionic), DOPS – dioleoyl-phosphatidylserine (anionic).
  • Mosaic nature of lipid bilayersSome conclusions about lateral organization of the binary DPPC/DOPC lipid membrane (in a liquid crystalline state) DOPC DPPC in clusters Top-view of the water- membrane interface outside clusters Solvation, H- ↓ bonding with water ↑ Tilt of lipid heads ↑ Lipid ordering ↑ Hydrophilicity Hydrophobic Hydrophilic Strongly hydrophilic
  • Mosaic nature of lipid bilayersMosaic nature of the membrane surface Hydrophobic organization of the surface N MHP = ∑ f k e − R jk k =1 fk- constant of atomic hydrophobicity, Rjk-distance between atom k and point j ( Å), N – number of atoms
  • MD simulations of pAntp in all-atom lipid bilayers In DOPS: In DOPC:-Deep penetration; -Interfacial location; -Always helical;-Helix destabilization; -Helical conformation; -Interfacial location;-No specific interactions; -Specific interactions (“traps”); -Few specific contacts;
  • MOLECULAR DYNAMICS DATA ON PEPTIDE-MEMBRANE BINDING Tilt angleDepth ofinsertion Burial of Peptide- the center bilayer of mass interactionsH-bonding
  • Bilayer’s properties affect peptide’s insertionDMPC – “fluid” bilayer DPPC – “rigid” bilayer
  • Peptide-induced thinning of bilayerUpper leafletLower leaflet
  • Specific peptide-lipids contacts (“traps”) - The peptide’s “traps” induce local destabilization of lipid bilayer: - Immobilization of nearest lipids - Changes in orientation of lipids’ headgroups; - The effects are much prominent in DOPS than in DOPC bilayers;Flexibility and geometry of lipid polar heads Polyansky A.A. et al. (2009) J. Phys. Chem B, 113:1107 Polyansky A.A. et al. (2009) J. Phys. Chem B, 113:1120
  • “Mosaic” nature of the membrane surface Peptide-lipid interactions may strongly depend on the local distribution of hydrophobic/hydrophilic properties of a bilayer in the contact area Polyansky A.A. et al. (2009) J. Phys. Chem B, 113:1107( MHP – molecular hydrophobicity potential ) Polyansky A.A. et al. (2009) J. Phys. Chem B, 113:1120
  • Antimicrobial peptides (AMPs): Membrane-assisted action “Pure” PE/PG (top view) hydrophob./hydrophil. surface + 12 AMPs (1 μs MD) hydrophobic ”defects”Martini v2.1 force-field, MD (1 μs),256 lipids (DOPE:DOPG 7:3mixture)CG-water box+12 AMP (Ltc2a) A. Polyansky et al , J. Phys. Chem. Lett. (2010)
  • Example:Interactions of transmembrane helical peptides in lipid environment:Prediction of 3D structure of helix-helix dimers taking into account hydrophobic properties and landscape of their surfaces
  • PROBING PROTEIN-PROTEIN INTERACTIONS IN MEMBRANE. WHY THIS IS IMPORTANT ?• Most of membrane proteins contain in their TM-domains several polypeptide regions.• Many membrane proteins are active only in oligomeric states: Examples: protein kinases (e.g., Erbb-, insulin-, and EpoR-receptors), mitochondrial apoptotic proteins, etc.• Molecular mechanisms of such interactions are still poorly understood (even for two helices).
  • THE CONCEPT OF “PEPTIDES-INTERCEPTORS” Normal cell Cancer cellligand Artificial “peptide-interceptor” ligand dimerization active dimer active dimer inactive dimer monomer
  • General scheme of the prediction approach 1 –building ideal helix from TM-sequence 2 – calculation of the helical surface (MHP + landscape) 3 – 2D interpolation of the surface 4 – slicing of the surface to a number of fragments (for each tilt angle) and their pairwise comparison (by scoring function) 5 – reconstruction of 3D structure of a dimer
  • Helix-helix association in membranePREDDIMER is capable of prediction reasonably correct structures of TM dimers BNIP3 EPHA1 Helix-helix interface NMR and predicted (PREDDIMER) structures
  • PERSPECTIVES:• Development of hybrid models combining advantages of different approximations used to describe biomembranes and protein-membrane interactions (all-atom, coarse-grained, implicit models);• Multi-level modeling of membranes with complex composition (3 and more components) in order to understand molecular basis of forming rafts and domains in cell membranes;• Analysis of relationships between properties of mixed bilayers and behavior of membrane and membrane-active peptides and proteins;• Application of structural, dynamic, and hydrophobic heterogeneity of native membranes to rational design of principally new membrane materials with predefined properties and to design efficient membrane-active compounds (including drugs).
  • CONCLUSIONS: Modeling is capable of providing reasonable guesses about important pharmacological targets - membranes and their complexes with peptides and proteins; The most reliable results are obtained via combined using of experimental and several self-consistent in silico methods; Computer simulations represent a powerful tool in design of new molecules targeting biological membranes and membrane proteins.
  • Laboratory of Biomolecular ModelingShemyakin-Ovchinnikov Institute of Bioorganic ChemistryRussian Academy of Scienceswww.ibch.ru model.nmr.ru