Structural bioinformatics for glycobiology

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Structural bioinformatics for glycobiology
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Structural glycoinformatics approaches

• Structural modeling
• Comparative modeling of glycoproteins
• Complex modeling glycoprotein replacement
• Modeling of the complex of glycans and GBPs and
  GTs
• docking
• Analysis of interaction specificities
• Key residues vs. Specific glycan conformations
• Molecular Dynamics
• Modeling the dynamics of the recognition of
  glycans by GBPs
• Modeling the enzymology of GTs quantum mechanic
  calculations

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Approaches to predicting protein structures
obtain sequence (target)
Sequence-sequence alignment or Sequence-structure
alignment
fold assignment
low identity fragment alignment
high identity long alignment
comparative modeling
ab initio modeling
build, assess model
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Comparative modeling of proteins

• Definition
• Prediction of three dimensional structure of a
  target protein from the amino acid sequence
  (primary structure) of a homologous (template)
  protein for which an X-ray or NMR structure is
  available.
• Why a Model
• A Model is desirable when either X-ray
  crystallography or NMR spectroscopy cannot
  determine the structure of a protein in time or
  at all. The built model provides a wealth of
  information of how the protein functions with
  information at residue property level, e.g. the
  interaction with the ligands, GBPs/GTs with
  glycans.

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Comparative Modeling (or homology modeling)
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Homology models can be very smart!
Homology models have RMSDs less than 2Å more than
70 of the time.
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Sequence similarity implies structural similarity?
.
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Step 1 Fold Identification
Aim To find a template or templates structures
from protein database (PDB)
pairwise sequence alignment - finds high homology
sequences BLAST Fold recognition programs find
low homology sequences (threading,
profile-profile alignment)
Improved Multiple sequence alignment methods
improves sensitivity - remote homologs PSIBLAST,
CLUSTAL
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Step 2 Model Construction
Aim To build three dimension (3D) structures of
proteins, coordinates of every atoms of the
homology proteins
Approach 1 protein structure buildup cores,
loops and sidechains Approach 2 whole protein
modeling constraint-based optimization. Commonly
used programs Modeller (http//salilab.org/mod
eller/) Swiss-model (http//swissmodel.expasy.org
/) Geno3D (http//geno3d-pbil.ibcp.fr/)
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Step 3 Model Construction
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Modeling of glycan-protein complexes

• Template glycan-protein complex
• Case 1 same glycan, different protein
• Glycoprotein replacement comparative modeling of
  protein structure
• Energy minimization, allowing structural
  flexibility of glycans
• Case 2 same protein, different glycan
• Flexible docking of glycans
• Case 3 different protein and different glycan
• Comparative modeling of proteins
• Flexible docking of glycan
• Can also be applied without a template of complex

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Flexible docking

• Semi-flexible (rigid protein, flexible ligand)
• Useful for drug screening
• gt150 programs Dock, AutoDock, FlexX/FlexE,
• Flexible protein mainly sidechains (hard)
• Two elements of semi-flexible docking algorithms
• ligand sampling methods
• Pattern matching Genetic Algorithm, Molecular
  Dynamics, Monte Carlo
• Treatment of intermolecular forces
• Simplified scoring functions empirical,
  knowledge-based and molecular mechanics e.g.
  AMBER, CHARMM, GROMOS, ...
• Very simple treatment of solvation and entropy,
  or completely ignored!

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Flexible docking of glycans to proteins

• Glycan structure sampling
• Automatic generation / sampling of 3D glycan
  structures Sweet II (http//www.dkfz-heidelberg.d
  e/spec/sweet2)
• Docking of each glycan conformation to the GBP
  Scoring schemes
• Empirical scores
• Forcefield
• GLYCAM modified AMBER forcefield / MD tools for
  glycans (R. Woods group)
• Challenge water molecules

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Flexibility of molecules

• Atoms connected by covalent bonds
• Bond lengths and bond angles are rigid
• Torsion (dihedral) angles are flexible

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Frequently used definitions of glycosidic torsion
angles
Angle NMR style C - 1 crystallographic style C 1 crystallographic style
? H1C1OC'x O5C1OC'x O5C1OC'x
? C1OC'xH'x C1OC'xC'x-1 C1OC'xC'x1
? (16)-linkage C1OC'6C'5 C1OC'6C'5 C1OC'6C'5
? (16)-linkage OC'6C'5H'5 OC'6C'5C'4 OC'6C'5O'5
ASN
sweet2 http//www.dkfz-heidelberg.de/spec/sweet2/
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Induced fit? rigid receptor hypethesis
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Preferred torsion angles of glycans
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Cone-like (left) and umbrella-like (right)
topologies of 2-3 and 2-6 siaylated glycans
binding to influenza viral HAs
Chandrasekaran, et. al. Nature Biotechnology 26,
107 - 113 (2008)
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Combine structural analysis with the glycan array
analysis providing structural insights.
M. E. Taylor and K. Drickamer, Glycobiology 2009
19(11)1155-1162
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Ligand binding by the scavenger receptor C-type
lectin (SRCL) and LSECtin
M. E. Taylor and K. Drickamer, Glycobiology 2009
19(11)1155-1162
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Binding of multiple classes of ligands to DC-SIGN
and the macrophage galactose receptor. Model of
the binding site in the macrophage galactose
receptor with a bound GalNAc residue, based on
the structure of the galactose-binding mutant of
mannose-binding protein that was created by
insertion of key binding site residues from the
galactose-binding receptor.
M. E. Taylor and K. Drickamer, Glycobiology 2009
19(11)1155-1162
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Mechanisms of mannose-binding protein interaction
with ligands.
M. E. Taylor and K. Drickamer, Glycobiology 2009
19(11)1155-1162
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Molecular Dynamics simulation of molecular
motions

• Energy model of conformation
• Two main approaches
• Monte Carlo - stochastic
• Molecular dynamics deterministic
• Understand molecular function and interactions
• Catalysis of enzymes
• Complementary to experiments
• Obtain a movie of the interacting molecules

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Basic Concepts of simulation of molecular motion

1. Compute energy for the interaction between all
   pairs of atoms.
2. Move atoms to the next state.
3. Repeat.

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Energy Function

• Target function that MD uses to govern the motion
  of molecules (atoms)
• Describes the interaction energies of all atoms
  and molecules in the system
• Always an approximation
• Closer to real physics --gt more realistic, more
  computation time (I.e. smaller time steps and
  more interactions increase accuracy)

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Scale in Simulations
continuum
mesoscale
Monte Carlo
Time Scale
10-6 S
molecular
dynamics
domain
10-8 S
quantum
D
exp(-
E/kT)
chemistry
10-12 S
F MA
10-10 M
10-8 M
10-6 M
10-4 M

Length Scale
Taken from Grant D. Smith Department of Materials
Science and Engineering Department of Chemical
and Fuels Engineering University of
Utah http//www.che.utah.edu/gdsmith/tutorials/tu
torial1.ppt
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The energy model

• Proposed by Linus Pauling in the 1930s
• Bond angles and lengths are almost always the
  same
• Energy model broken up into two parts
• Covalent terms
• Bond distances (1-2 interactions)
• Bond angles (1-3)
• Dihedral angles (1-4)
• Non-covalent terms
• Forces at a distance between all non-bonded atoms

http//cmm.cit.nih.gov/modeling/guide_documents/m
olecular_mechanics_document.html The NIH Guide to
Molecular Modeling
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The energy equation

• Energy
• Stretching Energy
• Bending Energy
• Torsion Energy
• Non-Bonded Interaction Energy
• These equations together with the data
  (parameters) required to describe the behavior of
  different kinds of atoms and bonds, is called a
  force-field.

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Bond Stretching Energy
kb is the spring constant of the bond. r0 is the
bond length at equilibrium.
Unique kb and r0 assigned for each bond pair,
i.e. C-C, O-H
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Bending Energy
k? is the spring constant of the bend. ?0 is the
bond length at equilibrium.
Unique parameters for angle bending are assigned
to each bonded triplet of atoms based on their
types (e.g. C-C-C, C-O-C, C-C-H, etc.)
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Torsion Energy
The parameters are determined from curve
fitting. Unique parameters for torsional rotation
are assigned to each bonded quartet of atoms
based on their types (e.g. C-C-C-C, C-O-C-N,
H-C-C-H, etc.)

• A controls the amplitude of the curve
• n controls its periodicity
• shifts the entire curve along the rotation angle
  axis (?).

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Non-bonded Energy
A determines the degree the attractiveness B
determines the degree of repulsion q is the charge
A determines the degree the attractiveness B
determines the degree of repulsion q is the charge
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Simulating In A Solvent

• The smaller the system, the more particles on the
  surface
• 1000 atom cubic crystal, 49 on surface
• 106 atom cubic crystal, 6 on surface
• Would like to simulate infinite bulk surrounding
  N-particle system
• Two approaches
• Implicitly
• Explicitly
• Periodic boundary conditions

Schematic representation of periodic boundary
conditions. http//www.ccl.net/cca/documents/molec
ular-modeling/node9.html
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Parameters for MD Forcefield

• Derived from direct experimental measurements on
  small molecules (10 atoms)
• Commonly used AMBER, CHARMM, GROMOS, etc
• GLYCAM for MD of glycoconjugates (derived from
  AMBER forcefield)

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Monte Carlo

• Explore the energy surface by randomly probing
  the configuration space by a Markov Chain
  approach
• Metropolis method (avoids local minima)

1. Specify the initial atom coordinates.
2. Select atom i randomly and move it by random
   displacement.
3. Calculate the change of potential energy, ?E
   corresponding to this displacement.
4. If ?E lt 0, accept the new coordinates and go to
   step 2.
5. Otherwise, if ?E ? 0, select a random R in the
   range 0,1 and
6. If e-?E/kT lt R accept and go to step 2
7. If e-?E/kT ? R reject and go to step 2

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Deterministic Approach

• Provides us with a trajectory of the system.
• From atom positions, velocities, and
  accelerations, calculate atom positions and
  velocities at the next time step.
• Integrating these infinitesimal steps yields the
  trajectory of the system for any desired time
  range.
• Typical simulations of small proteins including
  surrounding solvent in the pico-seconds.

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Deterministic / MD methodology

• From atom positions, velocities, and
  accelerations, calculate atom positions and
  velocities at the next time step.
• Integrating these infinitesimal steps yields the
  trajectory of the system for any desired time
  range.
• There are efficient methods for integrating these
  elementary steps with Verlet and leapfrog
  algorithms being the most commonly used.

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MD algorithm
r(tDt), v(tDt)

• Initialize system
• Ensure particles do not overlap in initial
  positions (can use lattice)
• Randomly assign velocities.
• Move and integrate.

r(t), v(t)
Leapfrog algorithm
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MD studies of Prion proteins

• Prion protein (PrP) is associated with an unusual
  class of neurodegenerative diseases
• Scrapie (sheep) bovine spongiform encephalopathy
  (BSE) in cattle kuru, Creutzfeldt-Jacob disease
  (CJD), Gerstmann-Sträussler-Scheinker syndrome
  (GSS), and fatal familiar insomnia (FFI) in
  humans
• Protein-only hypothesis (Prusiner, 1982) the
  disease is caused by an abnormal form of the 250
  amino acid PrP, which accumulates in plaques in
  the brain.
• PrP (PrPSc) differs from the normal cellular form
  (PrPC) only in its 3-D structure, and FTIR and CD
  spectra indicate it has a significantly increased
  content of ß-sheet conformation compared with
  PrPC
• Glycosylation appears to protect prion protein
  (PrPC) from the conformational transition to the
  disease-associated scrapie form (PrPSc)

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PrP is a glyco-protein

• Available NMR structures are for non-glycosylated
  PrPC only
• Glycosylation appears to protect prion protein
  (PrPC) from the conformational transition to the
  disease-associated scrapie form (PrPSc)
• Objective study of the influence of two N-linked
  glycans (Asn181 and Asn197) and of the GPI anchor
  attached to Ser230

Zuegg, et. al., Glycobiology, 2000,
10(10)959-974.
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MD simulations

• Molecular dynamics simulations on the C-terminal
  region of human prion protein HuPrP(90230), with
  and without the three glycans
• AMBER94 force field in a periodic box model with
  explicit water molecules, considering all
  long-range electrostatic interactions
• HuPrP(127227) is stabilized overall from
  addition of the glycans, specifically by
  extensions of two helix and reduced flexibility
  of the linking turn containing Asn197
• The stabilization appears indirect, by reducing
  the mobility of the surrounding water molecules,
  and not from specific interactions such as H
  bonds or ion pairs.
• Asn197 having a stabilizing role, while Asn181 is
  within a region with already stable secondary
  structure

Zuegg, et. al., Glycobiology, 2000,
10(10)959-974.
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Cone-like (left) and umbrella-like (right)
topologies of 2-3 and 2-6 siaylated glycans
binding to influenza viral HAs
A retrospective analysis
Chandrasekaran, et. al. Nature Biotechnology 26,
107 - 113 (2008)
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MD simulation of glycan binding of influenza HAs

• A combined approach (MD sequences) to predict
  ligand-binding mutants of H5N1 influenza HA
• Modeling the ligand-bound state of H5N1 HA using
  the isolate VN1194 bound to a2,3-sialyllactose as
  previously crystallized
• Excess mutual information was computed between
  each residue of each monomer and the
  corresponding bound ligand, using the average
  mutual information between the residue and all
  residues as an estimate of the background
  mutual information.
• Combine these results with sequence analysis of
  H5N1 mutational data to predict clusters of
  residues that undergo coordinated mutation, which
  have some capacity to vary but are subject to
  selective pressure relating mutation. These
  residues may be richer targets to change ligand
  specificity than residues absolutely conserved or
  residues that display uncorrelated mutations
  (involved in immune escape).

Kasson, et. al., JACS, 2009, 131 (32), pp
1133811340
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Experimentally identified ligand-binding
mutations in red, the top 5 of residues by
dynamics scoring in cyan (overlap of these two in
magenta), and the six mutation sites identified
by both dynamics and sequence analysis in yellow.
The top three mutations from the ligand
dissociation analyses in yellow. A modeled
a2,3-sialyllactose is shown in orange.
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Prediction of dissociation rate for HA mutants
(in silico mutagenesis)

• Bayesian analysis methods to predict dissociation
  rates based on extensive simulation of each
  mutant and evaluate whether a mutant has a faster
  dissociation rate than the influenza clinical
  isolate that we use as a wild-type reference.
• These simulations were used to estimate the
  dissociation rate for each mutation.
• The mutation sites predicted by analysis of the
  molecular dynamics data include both residues
  immediately contacting the bound glycan and
  residues located farther away on the globular
  head of the hemagglutinin molecule.