bio molecular modeling 1

8/6/2019 Bio Molecular Modeling 1

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Instructor: Prof. Jess A. Izaguirre

Textbook: Tamar Schlick,Molecular Modeling and

Simulation: An Interdisciplinary Guide, Springer-Verlag, Berlin-New York, 2002

Reference: C. Brooks, M. Karplus, B.

Pettitt,Proteins: A Theoretical Perspective

of Dynamics, Structure, and

Thermodynamics, Wiley, 1988

Computational Biology

Introduction to Biomolecular

Modeling


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Outline

1. What is biomolecular

modeling?

2. Historical perspective3. Theory and

experiments

4. Protein

characterization

5. Computational

successes

6. Remaining challenges


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What is biomolecular modeling?

Application of computational models to

understand the structure, dynamics, and

thermodynamics of biological molecules

The models must be tailored to the question at

hand: Schrodinger equation is not the answer to

everything! Reductionist view bound to fail!

This implies that biomolecular modeling must beboth multidisciplinaryand multiscale


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Historical Perspective

1. 1946 MD calculation

2. 1960 force fields

3. 1969 Levinthals paradox on protein folding

4. 1970 MD of biological molecules

5. 1971 protein data bank

a. 1998 ion channel protein crystal structure

b. 1999 IBM announces blue gene project


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Theoretical Foundations

1. Born-Oppenheimer approximation (fixednuclei)

2. Force field parameters for families of chemical

compounds3. System modeled using Newtons equations of

motion

4. Examples: hard spheres simulations (alder and

Wainwright, 1959); Liquid water (Rahman andStillinger, 1970); BPTI (McCammon andKarplus); Villin headpiece (Duan and Kollman,1998)


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Experimental Foundations I

1. X-ray crystallography Analysis of the X-ray diffraction pattern produced when a

beam of X-rays is directed onto a well-ordered crystal. Thephase has to be reconstructed.

Phase problem solved by direct method for smallmolecules

For larger molecules, sophisticated Multiple IsomorphousReplacement (MIR) technique used

Current resolution below 2 \AA

2. Protein crystallography Difficult to grow well-ordered crystals Early success in predicting alpha helices and beta sheets

(Pauling, 1950s)


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Experimental Foundations II

3. NMR Spectroscopy

Nuclear Magnetic Resonance provides

structural and dynamic information about

molecules. It is not as detailed as X-ray,

limited to masses of 35 kDa

Distances between neighboring hydrogens

are used to reconstruct the 3D structureusing global optimization


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Proteins I

Polypeptide chains made up of amino

acids or residues linked by peptide bonds

20 aminoacids 50-500 residues, 1000-10000 atoms

Native structure believed to correspond to

energy minimum, since proteins unfoldwhen temperature is increased


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Protein Function in Cell

1. Enzymes Catalyze biological reactions

2. Structural role Cell wall Cell membrane

Cytoplasm


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Protein: The Machinery of Life

NH2-Val-His-Leu-Thr-Pro-Glu-Glu-

Lys-Ser-Ala-Val-Thr-Ala-Leu-Trp-

Gly-Lys-Val-Asn-Val-Asp-Glu-Val-

Gly-Gly-Glu-..


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Proteins II

Secondary structure: alpha helices, betasheets, turns

Tertiary structure: proteins are tightlypacked, with hydrophobic groups in thecore and charged sidechains in thesurface

Quaternary structure: protein domainsmay assemble into so called quaternarystructures


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Protein Structure


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Protein Structure


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Model Molecule: Hemoglobin


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Hemoglobin: Background

Protein in red blood cells


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Red Blood Cell (Erythrocyte)


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Hemoglobin: Background Protein in red blood cells

Composed of four subunits, each

containing a heme group: a ring-like

structure with a central iron atom that

binds oxygen


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Heme Groups in Hemoglobin


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Hemoglobin: Background Protein in red blood cells

Composed of four subunits, each

containing a heme group: a ring-like

structure with a central iron atom that

binds oxygen

Picks up oxygen in lungs, releases it inperipheral tissues (e.g. muscles)


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Hemoglobin Quaternary

Structure

Two alpha subunits and two beta subunits

(141 AA per alpha, 146 AA per beta)


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Hemoglobin Tertiary Structure

One beta subunit (8 alpha helices)


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Hemoglobin Secondary

Structure

alpha helix


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Proteins III

Protein motions of importance are torsionaloscillations about the bonds that link groupstogether

Substantial displacements of groups occur overlong time intervals

Collective motions either local (cage structure)or rigid-body (displacement of different regions)

What is the importance of these fluctuations forbiological function?


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Proteins IV

Effect of fluctuations:

Thermodynamics: equilibrium behavior

important; examples, energy of ligand binding

Dynamics: displacements from average

structure important; example, local sidechain

motions that act as conformational gates in

oxygen transport myoglobin, enzymes, ionchannels


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Proteins V: Local Motions

0.01-5 AA, 1 fs -0.1s

Atomic fluctuations Small displacements for substrate binding in enzymes

Energy source for barrier crossing and otheractivated processes (e.g., ring flips)

Sidechain motions Opening pathways for ligand (myoglobin)

Closing active site Loop motions

Disorder-to-order transition as part of virus formation


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Proteins VI: Rigid-Body Motions

1-10 AA, 1 ns 1 s

Helix motions

Transitions between substrates (myoglobin)

Hinge-bending motions

Gating of active-site region (liver alcoholdehydroginase)

Increasing binding range of antigens(antibodies)


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Proteins VII: Large Scale Motion

> 5 AA, 1 microsecond 10000 s

Helix-coil transition Activation of hormones

Protein folding transition Dissociation

Formation of viruses

Folding and unfolding transition

Synthesis and degradation of proteins

Role of motions sometimes only inferred fromtwo or more conformations in structural studies


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Study of Dynamics I

The computational study of atomicfluctuations in BPTI and other proteins hasshown that :

Directional character of active-site fluctuationsin enzymes contributes to catalysis

Small amplitude fluctuations are lubricant

It may be possible to extrapolate from shorttime fluctuations to larger-scale proteinmotions


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Study of Dynamics II

Collective motions particularly important

for biological function, e.g., displacements

for transition from inactive to active

Extended nature of these motions makes

them sensitive to environment: great

difference between vacuum and solution

simulations Collective motions transmit external solvent

effects to protein interior


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Study of Dynamics III

For the related storage protein, myoglobin:

Fluctuations in the globin are essential tobinding: the protein matrix in X-ray is so tightly

packed that there is no low energy path forthe ligand to enter or leave the heme pocket

Only through structural fluctuations can thebarriers be lowered sufficiently

Demonstrated through energy minimizationand molecular dynamics


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Study of Dynamics IV

For the transport protein hemoglobin there

are several important motions:

Oxygen binding produces tertiary structural

change A quaternary structural change from deoxy (low

oxygen affinity) to oxy configuration takes place.

This transmits information over a long distance

From the X-ray deoxy and oxy structures, astochastic reaction path has been found. Detailed

ligand binding has been performed using MD. A

statistical mechanical modelhas provided

coupling between these two processes


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Study of Dynamics VI

Three open problems are the following:

1. Ion channel gating: highly correlated fluctuations are

likely to be of great importance. Long time dynamics

problem2. Flexible docking: for MMP, enzymes, etc.,

fluctuations enter into thermodynamics and kinetic

of reactions. Samplingproblem

3. Protein folding: too complicated for full treatment butfor smallest proteins, beyond current methodology.

Coarseningproblem


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Possible topics for final projects

Applications Virtual screening

Extend recommender for MD protocols

Algorithms Multiscale integrators or sampling methods

Cellular automata solvers for diffusion, reaction,advection, etc.

Software 3D Visualization Extend simulation engines


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How to create hierarchical,

multiscale, multilevel algorithms?Examples:

Algorithms for N-body problem(linear complexity, multiplegrids) e.g., Matthey andIzaguirre (2004) J. Par. Dist.Comp.

Multiscale integration (15 order ofmagnitude gap on timescales)e.g. Ma and Izaguirre (2003),Multisc. Model. Simul.

Coarse approximations (use

averagingorstochasticorensemble) solutions, e.g.Izaguirre and Hampton (2004),J. Comp. Phys.


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Lengthening scales: DPD

Dissipative ParticleDynamics combinescoarsening of atoms

into fluid packageswith dissipative pairinteractions, and astochastic pair

interaction Total momentumconserved

Self-organization of lipid bilayer,

self-assembled aggregates

formed by amphiphilic lipid

molecules in water.


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Lengthening Scales: SRP

Enzyme simulation of

a ms using stochastic

reaction path

disadvantage: needinitial and final

configuration

Finds a trajectory

where global energyis minimized


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How to predict protein interaction

networks?

Goal:

Predict proteins in agenome that are likely tointeract, thus giving clue

as to their function.Our current solution starts

from experimentalinteraction data and usesclustering and a set coverapproach to predict novelinteractions.

This is documented inHuang et al. (2004),IEEE/ACM TCBB,submitted


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How to create high-performance

software that is easy to use?

Goals:

Encapsulate optimizations like parallelism andcluster/grid computing so that these canbe used easily. MATLAB andMathematica are examples of easy to usescientific software

Allow easy prototyping of algorithms,extensions of the software bycomputational scientists (not expertcomputer scientists)

Our current solutions use:

Generic and object-oriented programming

Design patterns

XML-based domain specific languages

Related publications:

Matthey et al. (2004) ACM Trans. Math.Software, 20(3)

Cickovski et al. (2004) IEEE/ACM Trans.Comput. Biol. and Bioinformatics

Cickovski and Izaguirre (2004) ACMTrans. Prog. Lang. and Systems, in

preparation

ProtoMol, CompuCell3D, Biologo

ProtoMol is open source and available at http://protomol.sourceforge.net


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How to help user select software,

algorithms, and parameters to solve

their problems?

Simulation Requirements

ProtoMol/MDSimAid Server

Optimal parameters

via XML

Oursolution uses performance models and

machine learning to generate rules, run-time optimization to fine tune suggestions.

We want to use agents and machine

learning to update the rules.

This is documented in Ko (2002) and

Crockeret al. (2004), J. Comp. Chem.

Goal:

Recommend optimal softwareand architectural parameters to

solve particular problems

Make this easily available as web

portals

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