Marta Enciso Universidad Complutense de Madrid. BIFI 2011 - Marta Enciso Wylie, JACS, 2009 Kannan, Int. J. Mol. Sci., 2009 Chen, PNAS, 2009 Dobson, Annu.

Slides:



Advertisements
Similar presentations
Protein NMR terminology COSY-Correlation spectroscopy Gives experimental details of interaction between hydrogens connected via a covalent bond NOESY-Nuclear.
Advertisements

Adsorption and Desorption Profiles of MIT on POPA and POPC Membranes
Functional Site Prediction Selects Correct Protein Models Vijayalakshmi Chelliah Division of Mathematical Biology National Institute.
3D Molecular Structures C371 Fall Morgan Algorithm (Leach & Gillet, p. 8)
Rosetta Energy Function Glenn Butterfoss. Rosetta Energy Function Major Classes: 1. Low resolution: Reduced atom representation Simple energy function.
©CMBI 2001 The amino acids in their natural habitat.
By Guang Song and Nancy M. Amato Journal of Computational Biology, April 1, 2002 Presentation by Athina Ropodi.
A COMPLEX NETWORK APPROACH TO FOLLOWING THE PATH OF ENERGY IN PROTEIN CONFORMATIONAL CHANGES Del Jackson CS 790G Complex Networks
Incorporating additional types of information in structure calculation: recent advances chemical shift potentials residual dipolar couplings.
StreamMD Molecular Dynamics Eric Darve. MD of water molecules Cutoff is used to truncate electrostatic potential Gridding technique: water molecules are.
. Protein Structure Prediction [Based on Structural Bioinformatics, section VII]
PREDICCIÓN DE ESTRUCTURAS DE CRISTALES CON MOLÉCULAS FLEXIBES EN SU CELDA V. Bazterra, M. B. Ferraro, J. C. Facelli Departamento de Física Facultad de.
Anders Eriksson Complex Systems Group Dept. Energy and Environmental Research Chalmers EMBIO Cambridge July 2005 Complex Systems at Chalmers Information.
How NMR is Used for the Study of Bio-macromolecules Analytical biochemistry Comparative analysis Interactions between biomolecules Structure determination.
Construyendo modelos 3D de proteinas ‘fold recognition / threading’
Optimization of Carbocyclic Analogues to a Specific Pharmaceutical Enzyme Target via Discovery Studio TM Douglas Harris Department of Chemistry and Biochemistry,
02/03/10 CSCE 769 Dihedral Angles Homayoun Valafar Department of Computer Science and Engineering, USC.
Modeling of Biofilaments: Elasticity and Fluctuations Combined D. Kessler, Y. Kats, S. Rappaport (Bar-Ilan) S. Panyukov (Lebedev) Mathematics of Materials.
 Four levels of protein structure  Linear  Sub-Structure  3D Structure  Complex Structure.
RNA Secondary Structure Prediction Spring Objectives  Can we predict the structure of an RNA?  Can we predict the structure of a protein?
CZ5225 Methods in Computational Biology Lecture 4-5: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
Biomolecular Nuclear Magnetic Resonance Spectroscopy BASIC CONCEPTS OF NMR How does NMR work? Resonance assignment Structure determination 01/24/05 NMR.
BL5203 Molecular Recognition & Interaction Section D: Molecular Modeling. Chen Yu Zong Department of Computational Science National University of Singapore.
Experimental and theoretical asymmetry parameters for photoionization of H 2 showing interference from the Q 1 and Q 2 doubly excited states T. J. Reddish.
Part I : Introduction to Protein Structure A/P Shoba Ranganathan Kong Lesheng National University of Singapore.
Protein Structure 1 Primary and Secondary Structure.
Doug Raiford Lesson 17.  Framework model  Secondary structure first  Assemble secondary structure segments  Hydrophobic collapse  Molten: compact.
A Technical Introduction to the MD-OPEP Simulation Tools
Protein Folding and Modeling Carol K. Hall Chemical and Biomolecular Engineering North Carolina State University.
A 4D wave packet study of the CH 3 I photodissociation in the A band. Comparison with femtosecond velocity map imaging experiments A. García-Vela 1, R.
New Strategies for Protein Folding Joseph F. Danzer, Derek A. Debe, Matt J. Carlson, William A. Goddard III Materials and Process Simulation Center California.
MODELING MATTER AT NANOSCALES 3. Empirical classical PES and typical procedures of optimization Classical potentials.
Shouyong Peng Shouyong Peng Physics Department, Boston University Feb. 24, Clark University Molecular Dynamics Observation of A  Peptide Aggregation.
A computational study of shear banding in reversible associating polymers J. Billen +, J. Stegen *, A.R.C. Baljon + + Department of Physics, San Diego.
Coarse grained to atomistic mapping algorithm A tool for multiscale simulations Steven O. Nielsen Department of Chemistry University of Texas at Dallas.
Víctor M. Castillo-Vallejo 1,2, Virendra Gupta 1, Julián Félix 2 1 Cinvestav-IPN, Unidad Mérida 2 Instituto de Física, Universidad de Guanajuato 2 Instituto.
Chapter 4.1: Overview of Protein Structure CHEM 7784 Biochemistry Professor Bensley.
Wave packet calculations on the effect of the femtosecond pulse width in the time-resolved photodissociation of CH 3 I in the A-band A. García-Vela 1 and.
Chemistry XXI Unit 3 How do we predict properties? M1. Analyzing Molecular Structure Predicting properties based on molecular structure. M4. Exploring.
Developing a Force Field Molecular Mechanics. Experimental One Dimensional PES Quantum mechanics tells us that vibrational energy levels are quantized,
Gas phase spectroscopy at the CLS
Molecular Dynamics Arjan van der Vaart PSF346 Center for Biological Physics Department of Chemistry and Biochemistry Arizona State.
Structural classification of Proteins SCOP Classification: consists of a database Family Evolutionarily related with a significant sequence identity Superfamily.
Wed. Apr. 15– Calculus Lecture #23.2 Vectors, Dot Products, Projections, and Planes ) If v and w are unit vectors, what is the geometrical meaning.
The Folding of a Family of Three- Helix Bundle Proteins: Spectrin R15 Has a Robust Folding Nucleus, Unlike Its Homologous Neighbours J. Mol. Biol. (2014)
Ch7_ Inner Product Spaces In this section, we extend those concepts of R n such as: dot product of two vectors, norm of a vector, angle between vectors,
Chem - mystery What has more energy, a heat lamp or a tanning lamp?
Ab-initio protein structure prediction ? Chen Keasar BGU Any educational usage of these slides is welcomed. Please acknowledge.
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.
PROTEIN PHYSICS LECTURE 15. Protein Structures & Physical Background of Their Natural Selection  Structures  Selection.
Molecular Formula Calculations Combustion & Weight Percent C x H y + (x + y/4) O 2  x CO 2 + y/2 H 2 O C 2 H 5 OH + 3 O 2  2 CO H 2 O.
SMA5422: Special Topics in Biotechnology Lecture 9: Computer modeling of biomolecules: Structure, motion, and binding. Chen Yu Zong Department of Computational.
1 Dynamical Processes in Glasses by Molecular Dynamics Simulations José Pedro Rino Universidade Federal de São Carlos, Departamento de Física
Highlights in Physics –14 October 2005, Dipartimento di Fisica, Università di Milano Quantum methods in protein science C. Camilloni *, P. Cerri.
a useful tool to probe protein structural changes
Coarse-Grained Models Part II: Statistical potentials, CABS model
Protein Structure Prediction and Protein Homology modeling
Computer Simulations of
Database extraction of residue-specific empirical potentials
Hierarchical Structure of Proteins
Introduction & overview
Dynamics of Protein Molecules: Modeling and Applications
Protein dynamics Folding/unfolding dynamics
Study on the Self-assembly of Diphenylalanine-based Nanostructures by Coarse-grained Molecular Dynamics Cong Guo and Guanghong Wei Physics Department,
Feng Ding, Sergey V. Buldyrev, Nikolay V. Dokholyan 
Feng Ding, Douglas Tsao, Huifen Nie, Nikolay V. Dokholyan  Structure 
Volume 95, Issue 7, Pages (October 2008)
The Three-Dimensional Structure of Proteins
Volume 114, Issue 2, Pages (January 2018)
Presentation transcript:

Marta Enciso Universidad Complutense de Madrid

BIFI Marta Enciso Wylie, JACS, 2009 Kannan, Int. J. Mol. Sci., 2009 Chen, PNAS, 2009 Dobson, Annu. Rev. Biochem., 2006 Protein structure Protein design Folding Aggregation

BIFI Marta Enciso System description Potential definition –Geometrical restrictions –Energy calculation Simulation technique ME & A. Rey, J. Chem. Phys., 2010 Distance Angle

BIFI Marta Enciso Frozen region Meaningful region ME & A. Rey, J. Chem. Phys., 2010 C D B A

BIFI Marta Enciso Domain B of protein A DSSPSTRIDE PyMOLOurs flavodoxin domain B protein A α-spectrin T4 lysozyme fibronectin PDZ domain

BIFI Marta Enciso L. Prieto, D. de Sancho & A. Rey, J. Chem. Phys., 2005 Protein Folding Topology-based models Protein Folding Hydrophobics Hydrogen bonds +

BIFI Marta Enciso *J. Clarke, JMB, 1997 Experiment*4 K Topology-based8 K Topology+HB5 K Peak width Fibronectin type III domain

BIFI Marta Enciso Fibronectin type III domain Two chains

BIFI Marta Enciso A correct description of hydrogen bonds is necessary for understanding protein folding and aggregation We have designed a coarse-grained hydrogen bond model We have proved its validity for obtaining secondary structure elements and detecting real hydrogen bonds It can be successfully applied to the study of protein folding and interprotein interactions

Grupo de Simulación de Proteínas Departamento de Química Física I Universidad Complutense de Madrid Antonio Rey Ramiro Perezzan David de Sancho (U. Cambridge) Lidia Prieto (CUNY) María Larriva (U. Navarra)

Marta Enciso Universidad Complutense de Madrid

BIFI Marta Enciso a)First principles – Quantum Mechanics b)Empiric potentials a)Atomic resolution b)Coarse-grained models a)Others b)Our approach

R1 is a spatial restriction that designates the distance between the two carbons of the hydrogen bonded residues R1 = rij = rj − ri R2 is an orientational restrain which computes the cosine of the angle associated to the relative orientation between the auxiliary vectors of both residues R2 = cos(hi, hj) R3 is also an orientational quantity that computes the cosine of the angle between the direction of the tentative hydrogen bond in the model and each of the auxiliary vectors; thus, R3 is independently calculated for both i and j beads R3i and R3j R3 = cos(hi, rij)