Coarse-Grained Models Part II: Statistical potentials, CABS model

Slides:

Advertisements

Similar presentations

Protein Structure.

Advertisements

1 Resonance assignment strategies. 2 Amino acid sequence + The assignment problem.

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

Rosetta Energy Function Glenn Butterfoss. Rosetta Energy Function Major Classes: 1. Low resolution: Reduced atom representation Simple energy function.

Andrzej Kolinski LABORATORY OF THEORY OF BIOPOLYMERS WARSAW UNIVERSITY Structure and Function of Biomolecules, Bedlewo,

Combining atomic-level Molecular Dynamics with coarse-grained Monte-Carlo dynamics Andrzej Koliński Laboratory of Theory of Biopolymers, Faculty of Chemistry,

Biology 107 Macromolecules II September 9, Macromolecules II Student Objectives:As a result of this lecture and the assigned reading, you should.

A COMPLEX NETWORK APPROACH TO FOLLOWING THE PATH OF ENERGY IN PROTEIN CONFORMATIONAL CHANGES Del Jackson CS 790G Complex Networks

Two Examples of Docking Algorithms With thanks to Maria Teresa Gil Lucientes.

Determination of alpha-helix propensities within the context of a folded protein Blaber et al. J. Mol. Biol 1994.

Protein Primer. Outline n Protein representations n Structure of Proteins Structure of Proteins –Primary: amino acid sequence –Secondary:  -helices &

Biology 107 Macromolecules II September 8, 2003.

. Protein Structure Prediction [Based on Structural Bioinformatics, section VII]

1. Primary Structure: Polypeptide chain Polypeptide chain Amino acid monomers Peptide linkages Figure 3.6 The Four Levels of Protein Structure.

Physics of Protein Folding. Why is the protein folding problem important? Understanding the function Drug design Types of experiments: X-ray crystallography.

Resonance Assignment NMR analysis of proteins Sequential resonance assignment strategies Practical Issues.

Computing for Bioinformatics Lecture 8: protein folding.

Van der Waals forces and hydrogen bonds J. Israelichvili, „Intermolecular and Surface Forces”, Academic Press, London, 1997.

Computational Structure Prediction Kevin Drew BCH364C/391L Systems Biology/Bioinformatics 2/12/15.

Construyendo modelos 3D de proteinas ‘fold recognition / threading’

What are proteins? Proteins are important; e.g. for catalyzing and regulating biochemical reactions, transporting molecules, … Linear polymer chain composed.

 Four levels of protein structure  Linear  Sub-Structure  3D Structure  Complex Structure.

Representations of Molecular Structure: Bonds Only.

Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding I Prof. Corey O’Hern Department of Mechanical Engineering & Materials.

Protein Folding Programs By Asım OKUR CSE 549 November 14, 2002.

Department of Mechanical Engineering

Web Servers for Predicting Protein Secondary Structure (Regular and Irregular) Dr. G.P.S. Raghava, F.N.A. Sc. Bioinformatics Centre Institute of Microbial.

A Technical Introduction to the MD-OPEP Simulation Tools

Protein Folding and Modeling Carol K. Hall Chemical and Biomolecular Engineering North Carolina State University.

INTERACTIONS IN PROTEINS AND THEIR ROLE IN STRUCTURE FORMATION.

Biological Signal Detection for Protein Function Prediction Investigators: Yang Dai Prime Grant Support: NSF Problem Statement and Motivation Technical.

Pharm 201 Lecture 10, Reductionism and Classification Require Detailed Comparison Consider 3D Comparison Pharm 201/Bioinformatics I Philip E. Bourne.

Protein Modeling Protein Structure Prediction. 3D Protein Structure ALA CαCα LEU CαCαCαCαCαCαCαCα PRO VALVAL ARG …… ??? backbone sidechain.

JM - 1 Introduction to Bioinformatics: Lecture XI Computational Protein Structure Prediction Jarek Meller Jarek Meller Division.

Introduction to Protein Structure Prediction BMI/CS 576 Colin Dewey Fall 2008.

Structure prediction: Ab-initio Lecture 9 Structural Bioinformatics Dr. Avraham Samson Let’s think!

Protein Folding & Biospectroscopy Lecture 6 F14PFB David Robinson.

Chemistry XXI Unit 3 How do we predict properties? M1. Analyzing Molecular Structure Predicting properties based on molecular structure. M4. Exploring.

Coarse-Grained Models Part I: Motivation, Principles, and History

Lecture 12 Coarse-Grained Models of Biomolecules Part II: Physics-based force fields: MARTINI, UNRES, MSCG.

Protein Structures from A Statistical Perspective Jinfeng Zhang Department of Statistics Florida State University.

Structural Bioinformatics Elodie Laine Master BIM-BMC Semester 3, Genomics of Microorganisms, UMR 7238, CNRS-UPMC e-documents:

Organic Macromolecules: Proteins and Nucleic Acids.

Computational Structure Prediction

Department of Chemistry

The heroic times of crystallography

AIM: How are Proteins important to our Body?

Database extraction of residue-specific empirical potentials

Introduction & overview

Protein dynamics Folding/unfolding dynamics

Haixu Tang School of Inforamtics

Ligand Docking to MHC Class I Molecules

Yang Zhang, Andrzej Kolinski, Jeffrey Skolnick Biophysical Journal

Rosetta: De Novo determination of protein structure

AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking

The effect of the protein dielectric coefficient and pore radius on the Na+ affinity of a model sodium channel Dezső Boda1,2, Mónika Valiskó2, Bob Eisenberg1,

CZ5225 Methods in Computational Biology Lecture 7: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:

From Computer Simulations to Human Disease

Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach

Design and NMR analyses of compact, independently folded BBA motifs

Volume 14, Issue 9, Pages (September 2006)

GPCR-I-TASSER: A Hybrid Approach to G Protein-Coupled Receptor Structure Modeling and the Application to the Human Genome Jian Zhang, Jianyi Yang, Richard.

Coarse grain models (continuous)

Feng Ding, Sergey V. Buldyrev, Nikolay V. Dokholyan

Feng Ding, Douglas Tsao, Huifen Nie, Nikolay V. Dokholyan Structure

Systematic assessment of the importance of force field terms

LC8 is structurally variable but conserved in sequence.

Four Levels of Protein Structure

Sterol hindrance of Orai activation

Presentation transcript:

Coarse-Grained Models Part II: Statistical potentials, CABS model Lecture 13 Coarse-Grained Models of Biomolecules Part II: Statistical potentials, CABS model

Statistical potentials x – geometric variable p – type(s) of site(s) s – sequence context Leu-Leu pair, statistics from PDB

Contact potentials S. Miyazawa, R. Jernigan, Macromolecules, 18, 534-552 (1985)

Estimating the reference number of contacts

The CABS model Koliński, Acta Biochim. Polon., 51, 347-351 (2004)

Features of CABS Four interaction sites/residue (Ca, Cb, side chain center, peptide group. High-resolution cubic-lattice representation of proteins. Lattice Monte Carlo for sampling. Sequence-independent short-range potentials. Sequence-dependent short-range potentials. Hydrogen-bonding potentials including correlation terms. Sequence-independent excluded-volume long-range potentials. Context-dependent long-range potentials of sidechain- sidechain interactions.

Moves in CABS

Sequence-independent short-range potentials Stiffness

Sequence-independent short-range potentials Secondary structure

Sequence-independent short-range potentials Preventing crumpled structures Total energy from the generic short-range potentials

Sequence-specific short-range potentials in CABS

Hydrogen-bonding terms in CABS H-bonding: multibody terms Conditions for H-bonding

captures the orientation dependence of backbone hydrogen bonding interactions Approx. PMF PMF Liwo et al., Prot. Sci., 2, 1697 (1993); J. Phys. Chem. B 108, 9421 (2004)

Illustration of the four-body terms in UNRES

Illustration of the third-order correlation terms in UNRES

Generic repulsive interactions in CABS

Long-range sequence-specific interactions in CABS

Protein structure prediction with CABS 1ten (thick: model) 2fxd (thick: model)

Protein folding simulations Kmiecik and Koliński, Biophys. J., 94, 726-736 (2008)

CABS references A. Koliński, A. Godzik, J. Skolnick, J. Chem. Phys., 98, 7420-7433 (1993) (the first version of the model, not yet CABS). A Koliński, J. Skolnick, Proteins Struct. Funct. Genet., 18, 338-352; 353- 366 (1994) (more about the model and preliminary application). A. Koliński, Acta Biochim. Polon., 51, 347-351 (2004) (description of the CABS model). S. Kmiecik, M. Kurcinski, A. Rutkowska, D. Gront, A. Kolinski, Acta Biochim. Polon., 53, 131–143 (2006) (denaturated states by CABS). D. Gront, A. Koliński, Bioinformatics, 22, 621–622, 2006 (BioShell) D. Latek, A. Koliński , J. Comput. Chem., 32, 536–44, (2011) (CABS-NMR) M. Jamroż, A. Koliński, S. Kmiecik , Nucl. Acids Research, 41, W427-W431 (2013) (CABS Flex server) M. Błaszczyk, M. Jamroż, S. Kmiecik, A. Koliński Nucl. Acids Research, 41, W406-W411, (2013) (CABS Fold server) M. Kurciński, M. Jamroż, M. Błaszczyk, A. Koliński, S. Kmiecik Nucl. Acids Research, 43 (W1):W419-W424 (2015) (CABS Dock server).