Adsorption and Desorption Profiles of MIT on POPA and POPC Membranes

Slides:



Advertisements
Similar presentations
The Role of Long-Range Forces in Porin Channel Conduction S. Aboud Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester.
Advertisements

Computational Issues in Modeling Ion Transport in Biological Channels: Self- Consistent Particle-Based Simulations S. Aboud 1,2, M. Saraniti 1,2 and R.
Calculation of interaction energy between voltage-gated potassium channel Kv1.2 and blocker agitoxin Valery N. Novoseletsky Maria A. Bolshakova Konstantin.
Lecture 14: Advanced Conformational Sampling
P7 folding and its interaction with amantadine Chee Foong Chew Structural Bioinformatics & Computational Biochemistry Unit Department of Biochemistry,
The Geometry of Biomolecular Solvation 1. Hydrophobicity Patrice Koehl Computer Science and Genome Center
Bioinf. Data Analysis & Tools Molecular Simulations & Sampling Techniques117 Jan 2006 Bioinformatics Data Analysis & Tools Molecular simulations & sampling.
Max Shokhirev BIOC585 December 2007
Comparative study of protein-protein interaction observed in PolyGalacturonase-Inhibiting Proteins from P. vulgaris and G. max and PolyGalacturonase from.
Ps ns ss ms nm mm mm Ab-initio methods Statistical and continuum methods Atomistic methods.
Lecture 19: Free Energies in Modern Computational Statistical Thermodynamics: WHAM and Related Methods Dr. Ronald M. Levy Statistical.
Chemistry 1- Separation Objectives: 1) Learn about 2 different separation methods.
Chap. 11. Problem 1. The shape of the curve indicates that at low compressive forces, the lipids in the surface monolayer behave as if they are in a two-dimensional.
Cell membrane. What is a lipid? What is a lipid?What is a lipid? A fat molecule that doesn’t like water (Hydrophobic)A fat molecule that doesn’t like.
ISOPRENE - A possible mechanism by which plants protect themselves from thermal shock Magdalena Siwko.
Cell Membrane & Transport  Fluid mosaic model - Lipids, proteins & carbohydrates  Membrane trafficking - Passive Transport - Active Transport - Bulk.
Advanced Sampling Techniques When molecular dynamics is just not enough.
Monte Carlo in different ensembles Chapter 5
Prediction of Protein Binding Sites in Protein Structures Using Hidden Markov Support Vector Machine.
Phospholipid A phospholipid is a type of lipid used in the cells of living things.
2010 RCAS Annual Report Jung-Hsin Lin Division of Mechanics, Research Center for Applied Sciences Academia Sinica Dynamics of the molecular motor F 0 under.
Measurements and Their Analysis. Introduction Note that in this chapter, we are talking about multiple measurements of the same quantity Numerical analysis.
Moscow State Institute for Steel and Alloys Department of Theoretical Physics Analytical derivation of thermodynamic characteristics of lipid bilayer Sergei.
Interactions between bovine seminal plasma proteins and model lipid membranes Danny Lassiseraye 1, Puttaswamy Manjunath 2 and Michel Lafleur 1 1 Département.
Protein-membrane association. Theoretical model, Lekner summation A.H.Juffer The University of Oulu Finland-Suomi A.H.Juffer The University of Oulu Finland-Suomi.
Biological membranes: different cellular organelles have different lipid and protein membrane compositions.
Movement Through the Membrane Mr. Luis A. Velázquez Biology.
Methods and Software NAMD (NAnoscale Molecular Dynamics) is a parallel molecular dynamics code designed for high performance simulation of large systems.
Lecture 14: Advanced Conformational Sampling Dr. Ronald M. Levy Statistical Thermodynamics.
Membranes organize a cell’s activities Ch ► Plasma membrane acts as a cell’s boundary ► Regulates the traffic of chemicals that go in and out of.
SCHRÖDINGER EQUATION APPROACH TO THE UNBINDING TRANSITION OF BIOMEMBRANES AND STRINGS : RIGOROUS STUDY M. BENHAMOU, R. El KINANI, H. KAIDI ENSAM, Moulay.
Same chromophore – different absorption maximum
Future energy communities
Voltage-Dependent Hydration and Conduction Properties of the Hydrophobic Pore of the Mechanosensitive Channel of Small Conductance  Steven A. Spronk,
Cell Membrane Strucutre
TEK B.4B Concept: Investigate and Identify Processes Including Transportation of Molecules 11/24/2018.
دانشگاه شهیدرجایی تهران
Enzyme Kinetics & Protein Folding 9/7/2004
تعهدات مشتری در کنوانسیون بیع بین المللی
Vishwanath Jogini, Benoît Roux  Biophysical Journal 
Study on the Self-assembly of Diphenylalanine-based Nanostructures by Coarse-grained Molecular Dynamics Cong Guo and Guanghong Wei Physics Department,
Cell Membrane Strucutre
Jing Han, Kristyna Pluhackova, Tsjerk A. Wassenaar, Rainer A. Böckmann 
Sonya M. Hanson, Simon Newstead, Kenton J. Swartz, Mark S.P. Sansom 
3.3 Cell Membrane.
Amy Y. Shih, Stephen G. Sligar, Klaus Schulten  Biophysical Journal 
CELL MEMBRANE.
How Does a Voltage Sensor Interact with a Lipid Bilayer
Cell Membrane Strucutre
Raf-1 Cysteine-Rich Domain Increases the Affinity of K-Ras/Raf at the Membrane, Promoting MAPK Signaling  Shuai Li, Hyunbum Jang, Jian Zhang, Ruth Nussinov 
Hyunbum Jang, Buyong Ma, Thomas B. Woolf, Ruth Nussinov 
Computational Modeling Reveals that Signaling Lipids Modulate the Orientation of K- Ras4A at the Membrane Reflecting Protein Topology  Zhen-Lu Li, Matthias.
Molecular Model of a Cell Plasma Membrane With an Asymmetric Multicomponent Composition: Water Permeation and Ion Effects  Robert Vácha, Max L. Berkowitz,
Sunhwan Jo, Joseph B. Lim, Jeffery B. Klauda, Wonpil Im 
Sundeep S. Deol, Peter J. Bond, Carmen Domene, Mark S.P. Sansom 
Volume 108, Issue 10, Pages (May 2015)
Thomas H. Schmidt, Yahya Homsi, Thorsten Lang  Biophysical Journal 
Molecular Dynamics Simulations of the Rotary Motor F0 under External Electric Fields across the Membrane  Yang-Shan Lin, Jung-Hsin Lin, Chien-Cheng Chang 
Alternative Mechanisms for the Interaction of the Cell-Penetrating Peptides Penetratin and the TAT Peptide with Lipid Bilayers  Semen Yesylevskyy, Siewert-Jan.
Membrane Insertion of a Voltage Sensor Helix
Matthieu Chavent, Elena Seiradake, E. Yvonne Jones, Mark S.P. Sansom 
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Chze Ling Wee, David Gavaghan, Mark S.P. Sansom  Biophysical Journal 
Fig. 2 Cardiolipin binding sites in complex I.
Interactions of the Auxilin-1 PTEN-like Domain with Model Membranes Result in Nanoclustering of Phosphatidyl Inositol Phosphates  Antreas C. Kalli, Gareth.
Conserved motifs in the ABC
Presentation transcript:

Adsorption and Desorption Profiles of MIT on POPA and POPC Membranes Reporter: Guanglin Kuang

MIT domain Microtubule Interacting and Trafficking domain of the chitin synthase of Saprolegnia monoica.

Interaction with membranes POPA POPC

Umbrella Sampling Umbrella sampling is a technique used to improve the sampling of a system where ergodicity is hindered by the form of the system's energy landscape. Main idea: Divide the reaction coordinate ξ into different windows. Apply a biasing window potential wi(ξ) in each window to enhance the sampling in the neighborhood of a chosen value 𝜉 𝑖 𝑐 Perform biased simulation in each window. Independently. Combine the results in each window to get unbiased global free energy profile. 𝒘 𝒊 𝝃 = 𝑲 𝟐 (𝝃− 𝝃 𝒊 𝒄 )

Umbrella Sampling-Theory Biased potential energy in window i: 𝑉 𝑏 𝒓 𝑵 =𝑉 𝒓 𝑵 + 𝑤 𝑖 (𝜉) (1) Biasing window potential in window i: (2) 𝑤 𝑖 𝜉 = 𝐾 2 (𝜉− 𝜉 𝑖 𝑐 ) K is the force constant and 𝝃 𝒊 𝒄 is the center reaction coordinate in window i. The unbiased distribution function in window i: 𝑃 𝑖 𝑢 𝜉 = exp −𝛽𝑉 𝒓 𝑵 𝛿[𝜉(𝑟)−𝜉]𝑑 𝒓 𝑵 exp −𝛽𝑉 𝒓 𝑵 𝑑 𝒓 𝑵 (3) The biased distribution function in window i: 𝑃 𝑖 𝑏 𝜉 = exp {−𝛽 𝑉 𝒓 𝑵 + 𝒘 𝒊 𝝃 } 𝛿[𝜉(𝑟)−𝜉]𝑑 𝒓 𝑵 exp {−𝛽 𝑉 𝒓 𝑵 + 𝒘 𝒊 𝝃 } 𝑑 𝒓 𝑵 (4)

Umbrella Sampling-Theory From (3) and (4): Probability 𝑃 𝑖 𝑢 𝜉 = 𝑃 𝑖 𝑏 𝜉 ∙ exp 𝛽 𝑤 𝑖 𝜉 ∙ exp −𝛽 𝑤 𝑖 𝜉 exp −𝛽𝑉 𝒓 𝑵 𝑑 𝒓 𝑵 exp −𝛽𝑉 𝒓 𝑵 𝑑 𝒓 𝑵 = 𝑃 𝑖 𝑏 𝜉 ∙ exp 𝛽 𝑤 𝑖 𝜉 ∙<exp⁡[−𝛽 𝑤 𝑖 𝜉 ]> (5) The potential of mean force (PMF) at 𝜉 in window i is: 𝐴 𝑖 𝜉 =− 1 𝛽 𝑙𝑛 𝑃 𝑖 𝑢 𝜉 =− 1 𝛽 𝑙𝑛 𝑃 𝑖 𝑏 𝜉 − 𝑤 𝑖 𝜉 + 𝐹 𝑖 (6) 𝐹 𝑖 =− 1 𝛽 𝑙𝑛<exp⁡[−𝛽 𝑤 𝑖 𝜉 ]> (7)

Weighted Histogram Analysis Method (WHAM) The global distribution function is: 𝑃 𝑢 𝜉 = 𝑖 𝑁 𝑤 𝑐 𝑖 (𝜉)𝑃 𝑖 𝑢 𝜉 (8) 𝑐 𝑖 (𝜉) are weights chosen to minimize the statistical error: 𝜕 𝜎 2 [ 𝑃 𝑢 𝜉 ] 𝜕 𝑐 𝑖 (𝜉) =0 (9) 𝑖 𝑐 𝑖 (𝜉)=1 (10) From (8), (9) and (10): 𝑃 𝑢 𝜉 = 𝑖 𝑁 𝑤 𝑔 𝑖 −1 𝑁 𝑖 𝑃 𝑖 𝑏 𝜉 𝑗 𝑁 𝑤 𝑔 𝑗 −1 𝑁 𝑖 exp⁡{−𝛽 [𝑤 𝑖 𝜉 − 𝐹 𝑖 ]} (11) 𝑔 𝑖 =1+2 𝜏 𝑖 exp −𝛽 𝐹 𝑖 =< exp −𝛽 𝑤 𝑖 𝜉 >= exp −𝛽 𝑤 𝑖 𝜉 𝑃 𝑢 𝜉 𝑑𝜉 = exp {−𝛽 [𝐴 𝜉 +𝑤 𝑖 𝜉 ]} 𝑑𝜉 J Comput Phys. 1977, 23: 187-199 J Comput Chem. 1992, 13(8): 1011-1021 Comput Phys Commun. 1995, 91: 275-282 J. Chem. Theory Comput. 2010, 6: 3713–3720 Wires Comput Mol Sci. 2011, 1: 932-942 (12)

Gromacs 4.6.1, CHARMM27 for protein and lipid Work Flow: Gromacs 4.6.1, CHARMM27 for protein and lipid Adsorption: 100 ns; COM Pull: 20 ns; Umbrella Sampling : 40x10 ns=400 ns

Different Adsorption Modes of MIT on the POPA Bilayer

Different Adsorption Modes of MIT on the POPC Bilayer

Adsorption and Desorption Profiles of MIT on POPA and POPC Bilayers

Binding Modes of MIT with POPA and POPC Any way to quantify this residue-lipid interaction??? POPC POPC

Conclusions Thank you for you attention! The adsorption and desorption profiles of MIT on POPA and POPC are significantly different. Salt-bridges between the arginine residues of MIT and POPA are the reason for the high affinity over POPC. The initial configuration also influences the binding affinity, especially for POPA, where the configuration with the most arginine-phosphate interactions has the highest binding affinity. Other factors, like hydrophobic interaction, electrostatic interaction and steric effect, also influence the binding affinity. ….. Thank you for you attention!