University of Pennsylvania Chemical and Biomolecular Engineering Multiscale Modeling of Protein-Mediated Membrane Dynamics: Integrating Cell Signaling.

Slides:



Advertisements
Similar presentations
Simulazione di Biomolecole: metodi e applicazioni giorgio colombo
Advertisements

Science & Technology Multiscale Modeling of Lipid Bilayer Interactions with Solid Substrates David R. Heine, Aravind R. Rammohan, and Jitendra Balakrishnan.
Bare Surface Tension and Surface Fluctuations of Clusters with Long–Range Interaction D.I. Zhukhovitskii Joint Institute for High Temperatures, RAS.
Molecular Dynamics: Review. Molecular Simulations NMR or X-ray structure refinements Protein structure prediction Protein folding kinetics and mechanics.
Self-propelled motion of a fluid droplet under chemical reaction Shunsuke Yabunaka 1, Takao Ohta 1, Natsuhiko Yoshinaga 2 1)Department of physics, Kyoto.
Some Ideas Behind Finite Element Analysis
Chem 388: Molecular Dynamics and Molecular Modeling Continuum Electrostatics And MM-PBSA.
1 Model Hierarchies for Surface Diffusion Martin Burger Johannes Kepler University Linz SFB Numerical-Symbolic-Geometric Scientific Computing Radon Institute.
The Calculation of Enthalpy and Entropy Differences??? (Housekeeping Details for the Calculation of Free Energy Differences) first edition: p
1 MECH 221 FLUID MECHANICS (Fall 06/07) Tutorial 7.
SPACE CHARGE EFFECTS IN PHOTO-INJECTORS Massimo Ferrario INFN-LNF Madison, June 28 - July 2.
University of Pennsylvania Chemical and Biomolecular Engineering Multiscale Modeling of Protein-Mediated Membrane Dynamics: Integrating Cell Signaling.
University of Pennsylvania Modeling of Targeted Drug Delivery Neeraj Agrawal.
Lattice regularized diffusion Monte Carlo
University of Pennsylvania Department of Bioengineering Hybrid Mesoscale Models For Protein- Membrane Interactions Neeraj Agrawal, Jonathan Nukpezah, Joshua.
Two Approaches to Multiphysics Modeling Sun, Yongqi FAU Erlangen-Nürnberg.
University of Pennsylvania Chemical and Biomolecular Engineering Multiscale Modeling of Protein-Mediated Membrane Dynamics: Integrating Cell Signaling.
Elastically Deformable Models
Multiscale Modeling of Protein-Mediated Membrane Processes
James Sprittles ECS 2007 Viscous Flow Over a Chemically Patterned Surface J.E. Sprittles Y.D. Shikhmurzaev.
Fluctuations and Brownian Motion 2  fluorescent spheres in water (left) and DNA solution (right) (Movie Courtesy Professor Eric Weeks, Emory University:
Lattice Boltzmann Equation Method in Electrohydrodynamic Problems
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Ananth Grama Coordinated Systems Lab Purdue University.
9/23/2015 KITPC - Membrane Biophysics 1 Modeling of Actomyosin Driven Cell Oscillations Xiaoqiang Wang Florida State Univ.
University of Pennsylvania Department of Bioengineering Hybrid Mesoscale Models For Protein- Membrane Interactions Neeraj Agrawal, Jonathan Nukpezah, Joshua.
Dynamics.  relationship between the joint actuator torques and the motion of the structure  Derivation of dynamic model of a manipulator  Simulation.
1 CE 530 Molecular Simulation Lecture 17 Beyond Atoms: Simulating Molecules David A. Kofke Department of Chemical Engineering SUNY Buffalo
Ps ns ss ms nm mm mm Ab-initio methods Statistical and continuum methods Atomistic methods.
Modeling of Biofilaments: Elasticity and Fluctuations Combined D. Kessler, Y. Kats, S. Rappaport (Bar-Ilan) S. Panyukov (Lebedev) Mathematics of Materials.
CZ5225 Methods in Computational Biology Lecture 4-5: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
12/01/2014PHY 711 Fall Lecture 391 PHY 711 Classical Mechanics and Mathematical Methods 10-10:50 AM MWF Olin 103 Plan for Lecture 39 1.Brief introduction.
A Unified Lagrangian Approach to Solid-Fluid Animation Richard Keiser, Bart Adams, Dominique Gasser, Paolo Bazzi, Philip Dutré, Markus Gross.
A particle-gridless hybrid methods for incompressible flows
Nonlinear localization of light in disordered optical fiber arrays
Yoon kichul Department of Mechanical Engineering Seoul National University Multi-scale Heat Conduction.
Neeraj Agrawal University of Pennsylvania 1 Modeling of Targeted Drug Delivery and Endocytosis Neeraj Agrawal Clathrin.
Frank L. H. Brown University of California, Santa Barbara Brownian Dynamics with Hydrodynamic Interactions: Application to Lipid Bilayers and Biomembranes.
Surface and Bulk Fluctuations of the Lennard-Jones Clusrers D. I. Zhukhovitskii.
1 M.Sc. Project of Hanif Bayat Movahed The Phase Transitions of Semiflexible Hard Sphere Chain Liquids Supervisor: Prof. Don Sullivan.
Structural origin of non-Newtonian rheology Computer simulations on a solution of telechelic associating polymers J. Stegen +, J. Billen°, M. Wilson °,
Study of Pentacene clustering MAE 715 Project Report By: Krishna Iyengar.
Dissipative Particle Dynamics. Molecular Dynamics, why slow? MD solves Newton’s equations of motion for atoms/molecules: Why MD is slow?
FLUID PROPERTIES Independent variables SCALARS VECTORS TENSORS.
Lecture III Trapped gases in the classical regime Bilbao 2004.
Lecture IV Bose-Einstein condensate Superfluidity New trends.
Direct Numerical Simulations of Non-Equilibrium Dynamics of Colloids Ryoichi Yamamoto Department of Chemical Engineering, Kyoto University Project members:
LATTICE BOLTZMANN SIMULATIONS OF COMPLEX FLUIDS Julia Yeomans Rudolph Peierls Centre for Theoretical Physics University of Oxford.
Introduction: Lattice Boltzmann Method for Non-fluid Applications Ye Zhao.
Thermal Surface Fluctuations of Clusters with Long-Range Interaction D.I. Zhukhovitskii Joint Institute for High Temperatures, RAS.
MS310 Quantum Physical Chemistry
LSM3241: Bioinformatics and Biocomputing Lecture 6: Fundamentals of Molecular Modeling Prof. Chen Yu Zong Tel:
Cell Communication Chapter 9.
Neeraj Agrawal University of Pennsylvania 1 Modeling of Targeted Drug Delivery and Endocytosis Neeraj Agrawal Clathrin.
Particle-based Viscoelastic Fluid Simulation Simon Clavet Philippe Beaudoin Pierre Poulin LIGUM, Université de Montréal.
University of Pennsylvania Department of Bioengineering Hybrid Models For Protein-Membrane Interactions At Mesoscale: Bridge to Intracellular Signaling.
--Experimental determinations of radial distribution functions --Potential of Mean Force 1.
Molecular dynamics (3) Equations of motion for (semi) rigid molecules. Restrained MD.
Molecular dynamics (MD) simulations  A deterministic method based on the solution of Newton’s equation of motion F i = m i a i for the ith particle; the.
Computational Physics (Lecture 11) PHY4061. Variation quantum Monte Carlo the approximate solution of the Hamiltonian Time Independent many-body Schrodinger’s.
A Multiscale Approach to Mesh-based Surface Tension Flows
Coarsening dynamics Harry Cheung 2 Nov 2017.
Numerical Modeling of Dynamics and Adhesion of Leukocytes
Thesis Project Proposal
Multiscale Modeling of Targeted Drug Delivery
Diffuse interface theory
Atomistic simulations of contact physics Alejandro Strachan Materials Engineering PRISM, Fall 2007.
Atomistic materials simulations at The DoE NNSA/PSAAP PRISM Center
Protein Induced Membrane Deformation
PHY 711 Classical Mechanics and Mathematical Methods
Xiaoqiang Wang Florida State University
Presentation transcript:

University of Pennsylvania Chemical and Biomolecular Engineering Multiscale Modeling of Protein-Mediated Membrane Dynamics: Integrating Cell Signaling with Trafficking Neeraj Agrawal Clathrin Advisor: Ravi Radhakrishnan Thesis Project Proposal

University of Pennsylvania Chemical and Biomolecular Engineering Previous Work Monte-Carlo Simulations Agrawal, N.J. Radhakrishnan, R.; Purohit, P. Biophys J. submitted Agrawal, N.J. Radhakrishnan, R.; J. Phys. Chem. C. 2007, 111, Protein-Mediated DNA Looping Role of Glycocalyx in mediating nanocarrier- cell adhesion DNA elasticity under applied force

University of Pennsylvania Chemical and Biomolecular Engineering Endocytosis: The Internalization Machinery in Cells Detailed molecular and physical mechanism of the process still evading. Endocytosis is a highly orchestrated process involving a variety of proteins. Attenuation of endocytosis leads to impaired deactivation of EGFR – linked to cancer Membrane deformation and dynamics linked to nanocarrier adhesion to cells Short-term Quantitative dynamic models for membrane invagination: Development of a multiscale approach to describe protein-membrane interaction at the mesoscale (  m) Long-term Integrating with signal transduction Minimal model for protein-membrane interaction in endocytosis is focused on the mesoscale

University of Pennsylvania Chemical and Biomolecular Engineering Endocytosis of EGFR A member of Receptor Tyrosine Kinase (RTK) family Transmembrane protein Modulates cellular signaling pathways – proliferation, differentiation, migration, altered metabolism Multiple possible pathways of EGFR endocytosis – depends on ambient conditions –Clathrin Mediated Endocytosis –Clathrin Independent Endocytosis

University of Pennsylvania Chemical and Biomolecular Engineering Clathrin Dependent Endocytosis One of the most common internalization pathway Kirchhausen lab. AP - 2 epsin AP - 2 clathrin AP-2 epsin AP - 2 clathrin AP - 2 epsin clathrin. EGF Membrane Common theme: –Cargo Recognition – AP2 –Membrane bending proteins – Clathrin, epsin Hypothesis: Clathrin+AP2 assembly alone is not enough for vesicle formation, accessory curvature inducing proteins required.

University of Pennsylvania Chemical and Biomolecular Engineering Overview Protein diffusion models Membrane models Model Integration Preliminary Results

University of Pennsylvania Chemical and Biomolecular Engineering Multiscale Modeling of Membranes Length scale Time scale nm ns µmµm s Fully-atomistic MD Coarse-grained MD Generalized elastic model Bilayer slippage Monolayer viscous dissipation Viscoelastic model Molecular Dynamics (MD)

University of Pennsylvania Chemical and Biomolecular Engineering Linearized Elastic Model For Membrane: Monge-TDGL Helfrich membrane energy accounts for membrane bending and membrane area extension. Force acting normal to the membrane surface (or in z-direction) drives membrane deformation Spontaneous curvatureBending modulus Frame tension Splay modulus Consider only those deformations for which membrane topology remains same. z(x,y) The Monge gauge approximation makes the elastic model amenable to Cartesian coordinate system In Monge notation, for small deformations, the membrane energy is

University of Pennsylvania Chemical and Biomolecular Engineering Hydrodynamics of the Monge-TDGL Non inertial Navier Stoke equation Dynamic viscosity of surrounding fluid Solution of the above PDEs results in Oseen tensor, (Generalized Mobility). Oseen tensor Fluid velocity is same as membrane velocity at the membrane boundary  no slip condition given by: This results in the Time-Dependent Ginzburg Landau (TDGL) Equation z(x,y) x y Hydrodynamic coupling White noise

University of Pennsylvania Chemical and Biomolecular Engineering Local-TDGL Formulation for Extreme Deformations A new formalism to minimize Helfrich energy. No linearizing assumptions made. Applicable even when membrane has overhangs Surface represented in terms of local coordinate system. Monge TDGL valid for each local coordinate system. Overall membrane shape evolution – combination of local Monge-TDGL. Monge-TDGL, mean curvature = Linearization Local-TDGL, mean curvature = Local Monge Gauges Membrane elastic forces act in x, y and z directions ×

University of Pennsylvania Chemical and Biomolecular Engineering Hydrodynamics of the Local-TDGL Non-inertial Navier Stoke equation Dynamic viscosity of surrounding fluid Fluid velocity is same as membrane velocity at the membrane boundary Surface viscosity of bilayer

University of Pennsylvania Chemical and Biomolecular Engineering Surface Evolution For axisymmetric membrane deformation Exact minimization of Helfrich energy possible for any (axisymmetric) membrane deformation Membrane parameterized by arc length, s and angle φ. S=L S=0

University of Pennsylvania Chemical and Biomolecular Engineering Solution Protocol for Monge-TDGL Divergence removed by neglecting mode k=0 (rigid body translation) The harmonic series is a diverging series for a periodic system. We sum in Fourier space (k 1, k 2 ) Periodic boundary conditions for membrane. Numerical solution using discrete version of membrane dynamics equation ‘n’ is number of grid points Explicit Euler scheme with h 4 spatial accuracy

University of Pennsylvania Chemical and Biomolecular Engineering Curvature-Inducing Protein Epsin Diffusion on the Membrane Each epsin molecule induces a curvature field in the membrane Membrane in turn exerts a force on epsin Epsin performs a random walk on membrane surface with a membrane mediated force field, whose solution is propagated in time using the kinetic Monte Carlo algorithm Bound epsin position KMC-move Metric epsin(a)  epsin(a+a 0 ) where a 0 is the lattice size, F is the force acting on epsin

University of Pennsylvania Chemical and Biomolecular Engineering Hybrid Multiscale Integration Regime 1: Deborah number De<<1 or (a 2 /D)/(z 2 /M) << 1 Regime 2: Deborah number De~1 or (a 2 /D)/(z 2 /M) ~ 1 KMC TDGL #=1/De #=  /  t Surface hopping switching probability Relationship Between Lattice & Continuum Scales Lattice  continuum: Epsin diffusion changes C 0 (x,y) Continuum  lattice: Membrane curvature introduces an energy landscape for epsin diffusion R

University of Pennsylvania Chemical and Biomolecular Engineering Applications Monge TDGL (linearized model)  Phase transitions Surface Evolution Local TDGL Integration with signaling –Clathrin Dependent Endocytosis –Clathrin Independent Endocytosis –Targeted Drug Delivery Energetic of vesicle formation Spatial Organizations of molecular components –Radial distribution function –Orientational correlation function

University of Pennsylvania Chemical and Biomolecular Engineering Local-TDGL (No Hydrodynamics) A new formalism to minimize Helfrich energy. No linearizing assumptions made. Applicable even when membrane has overhangs Exact solution for infinite boundary conditions TDGL solutions for 1×1 µm 2 fixed membrane At each time step, local coordinate system is calculated for each grid point. Monge-TDGL for each grid point w.r.to its local coordinates. Rotate back each grid point to get overall membrane shape.

University of Pennsylvania Chemical and Biomolecular Engineering Potential of Mean Force PMF is dictated by both energetic and entropic components Epsin experience repulsion due to energetic component when brought close. Second variation of Monge Energy (~ spring constant). Non-zero H 0 increases the stiffness of membrane  lower thermal fluctuations Test function Bound epsin experience entropic attraction. x0

University of Pennsylvania Chemical and Biomolecular Engineering Research Plan Include protein-dynamics in Local-TDGL. Numerical solver for Surface Evolution approach to validate Local-TDGL. Inclusion of relevant information about Clathrin and AP2 in the model.

University of Pennsylvania Chemical and Biomolecular Engineering Summary A Monte Carlo study to show the importance of glycocalyx on nanocarrier binding to cell surface. Effect of protein size on DNA loop formation probability demonstrated using Metropolis, Gaussian sampling and Density of State Monte Carlo. Two new formalisms developed for calculating membrane shape for non-zero spontaneous curvature  Local-TDGL and Surface- Evolution. Interaction between two membrane bound epsin studied.

University of Pennsylvania Chemical and Biomolecular Engineering Acknowledgments Jonathan Nukpezah Joshua Weinstein

University of Pennsylvania Chemical and Biomolecular Engineering Hydrodynamics Main assumptions – validity ? –Surrounding fluid extends to infinity –Membrane is located at z=0, i.e. deformations are low. Hydrodynamics in cellular environment is much more complicated. Can be used to compare system (dynamic and equilibrium) behavior in absence and presence of hydrodynamic interactions. Can be used to validate results against in vitro experiments.

University of Pennsylvania Chemical and Biomolecular Engineering Parameters Bending Rigidity ~ 4k B T = 1.6* erg Tension ~ 3 µm Diffusion coeff. in cell membrane ~ 0.01 µm 2 /s Cytoplasm viscosity ~ Pa.s a 0 = 3*3 nm (ENTH domain size)

University of Pennsylvania Chemical and Biomolecular Engineering Molecular Dynamics MD on bilayer and epsin incorporated bilayer Fluctuation spectrum of bilayer  bending rigidity and tension Intrinsic curvature Blood, P. D.; Voth, G. A., PNAS 2006, 103, (41), Marsh, D., Biophys. J. 2001, 81, 2154.

University of Pennsylvania Chemical and Biomolecular Engineering Targeted Drug Delivery

University of Pennsylvania Chemical and Biomolecular Engineering Atomistic to Block-Model Each protein – a combination of blocks. Charge per block determined by solving non-linear Poisson- Boltzmann equation. Implicit solvent. LJ parameters – sum of LJ parameters of all atom types in a block. Electrostatics & vDW are relevant only for distances of 30 Å. Specific interaction.

University of Pennsylvania Chemical and Biomolecular Engineering Clathrin and AP2 models Clathrin H 0 = H 0 (r,t,t 0,r 0 ) t 0 and r 0 : time and position of nucleation –H 0 grows in position as a function of time. –Rate of appearance ~ 3 events/(100 µm 2 -s). –Rate of growth ~ one triskelion/(2 s) –Rate of dissociation inferred from mean life time of clathrin cluster Ehrlich, M. et. al. Cell 2004, 118, AP2 #####