Multi-Scale Hierarchical Structure Prediction of Helical Transmembrane Proteins

1
Multi-Scale Hierarchical Structure Prediction of
Helical Transmembrane Proteins
Zhong Chen and Ying Xu Department of Biochemistry
and Molecular Biology and Institute of
Bioinformatics University of Georgia
2
Outline

1. Background information
2. Statistical analysis of known membrane protein
   structures
3. Structure prediction at residual level
4. Helix packing at atomistic level
5. Linking predictions at residue and atomistic
   levels

3
Membrane Proteins

• Roles in biological process
• Receptors
• Channels, gates and pumps
• Electric/chemical potential
• Energy transduction
• gt 50 new drug targets are membrane proteins
  (MP).

4
Membrane Proteins

• 20-30 of the genes in a genome encode MPs.
• lt 1 of the structures in the Protein Data Bank
  (PDB) are MPs
• difficulties in
  experimental structure determination.

5
Membrane Proteins

• Prediction for transmembrane (TM) segments
  (a-helix or ß-sheet) based on sequence alone is
  very accurate (up to 95)

• Prediction of the tertiary structure of the TM
  segments how do these a-helices/ß-sheets arrange
  themselves in the constrains of bi-lipid layers?

Helical structures are relatively easier to solve
computationally
6
Membrane Protein Structures

• Difficult to solve experimentally
• Computational techniques could possibly play a
  significant role in solving MP structures,
  particularly helical structures

7
High Level Plan

• Statistical analysis of known structures
• Unveil the underlying principles for MP structure
  and stability
• Develop knowledge-based propensity scale and
  energy functions.
• Structure prediction at residue level
• Structure prediction at atomistic level MC, MD
• multi-scale, hierarchical computational framework
  

8
Part I Statistical Analysis of Known Structures
9
Database for Known MP Structures Helical Bundles

• Redundant database
• 50 pdb files
• 135 protein chains
• Non-redundant database (identity lt 30)
• 39 pdb files
• 95 protein chains (avg. length 220 AA)

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Bi-lipid Layer Chemistry
Polar header (glycerol, phosphate)
Hydrophobic tail (fatty acid)
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Statistics-based energy functions

• Length of bi-lipid layer 60 Å
• Central regions
• Terminal regions
• Three energy terms
• Lipid-facing potential
• Residue-depth potential
• Inter-helical interaction potential

Terminal
60 Å
30 Å
Central
Terminal
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Lipid-facing Propensity Scale
Residue Termini Central
ILE 0.84 1.33
VAL 0.71 1.30
LEU 0.89 1.30
PHE 1.03 1.38
CYS 0.37 0.67
MET 0.57 0.80
ALA 0.69 0.79
GLY 0.84 0.44
THR 0.79 0.61
SER 1.04 0.51
TRP 1.11 1.89
TYR 0.73 1.04
PRO 1.01 0.60
HIS 1.27 1.61
ASP 1.56 1.08
GLU 2.10 0.93
ASN 1.02 0.71
GLN 1.44 0.71
LYS 2.59 1.97
ARG 1.42 1.16
fraction of AA are
lipid-facing LF_scale(AA)
fraction of AA are in interior

• The most hydrophobic residues (ILE, VAL, LEU)
  prefer the surface of MPs in the central region,
  while prefer interior position in the terminal
  regions
• Small residues (GLY, ALA, CYS, THR) tend to be
  buried in the helix bundle
• Bulky residues (LYS, ARG, TRP, HIS) are likely to
  be found on the surface.

This propensity scale reflects both hydrophobic
interactions and helix packing
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Helical Wheel and Moment Analysis
The magnitude of each thin-vector is proportional
to the LF-propensity and overall lipid-facing
vector is the sum of all thin vectors,
Average Predication Error 41 degree
Lipid facing vector prediction state of the
art kPROT avg. error 41º Samatey
Scale 61º Hydrophobicity
scales 65 68º
14
Reside-Depth Potential
- hydrophobic residues tend to be located in the
hydrocarbon core - hydrophilic residues tend to
be closer to terminal regions - aromatic
residues prefer the interface region.
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TM Helix Tilt Angle Prediction
major pVIII coat protein of the filamentous fd
bacteriophage (1MZT)
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Inter-Helical Pair-wise Potential
Å
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Statistical energy potentials (summary)

1. Three residue-based statistic potentials were
   derived from the database (a) lipid-facing
   propensity, (b) residue depth potential, (c)
   inter-helical pair-wise potential
2. The lipid-facing scale predicted the lipid-facing
   direction for single helix with a uncertainty at
   40º
3. The residue-depth potential was able to predict
   the tilt angle for single helix with high
   accuracy.
4. Need more data to make inter-helical pair-wise
   potential more reliable

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Part II Structure Prediction at Residue Level
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Key Prediction Steps

• Structure prediction through optimizing our
  statistical potential (weighted sum)
• Idealized and rigid helical backbone
  configurations
• Monte Carlo moves translations, rotations,
  rotation by helix axis
• Wang-Landau sampling technique for MC simulation
• Principle component analysis.

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Wang-Landau Method for MC
Observation if a random walk is performed with
probability proportional to reciprocal of density
of states then a flat energy
histogram could be obtained.
The density of states is not known a priori.
In Wang-Landau, g(E) is initially set to 1 and
modified on the fly. Monte Carlo moves are
accepted with probability Each time when an
energy level E is visited, its density of states
is updated by a modification factor f gt1, i.e.,
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Wang-Landau Method for MC

• Advantages
• simple formulation and general applicability
• Entropy and free energy information derivable
  from g(E)
• Each energy state is visited with equal
  probability, so energy barriers are overcome with
  relative ease.

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Principal Component Analysis

• Purpose
• analyze the conformation variations during a
  simulation, and
• identify the most important conformational
  degrees of freedom.
• Covariance matrix

A large part of the systems fluctuations can
be described in terms of only a few PCA
eigenvectors.
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A Model System Glycophorin (GpA) Dimer

• GxxxG motif
• Ridges-into-grooves

22 residues, 189 atoms EITLIIFGVMAGVMAGVIGTILLISY
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Glycophorin (GpA) Dimer (1AFO)
A GEM (global energy minimum)
RMSD3.6A E-114.6kcal/mol
B LEM
RMSD0.8A E-93.9kcal/mol
B
A
RED experiment GREY simulation
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Helices A and B of Bacteriorhodopsin (1QHJ)
A
B
A GEM
RMSD2.7A E-94kcal/mol
B LEM
RMSD0.9A E-86kcal/mol
RED experiment GREY simulation
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Bacteriorhodopsin (1QHJ)
Rmsd5.0A
G
F
A
A
E
C
B
D
Computational prediction
Experimental structure
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Residue-level structure prediction (Summary)

1. A computational scheme was established for TM
   helix structure prediction at residue level
2. For two-helix systems, LEM structures very close
   to native structures (RMSD lt 1.0 Å) were
   consistently predicted
3. For a seven-helix bundle, a packing topology
   within 5.0 Å of the crystal structure was
   identified as one of the LEMs.

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Part III Structure Prediction at Atomistic Level
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Key Prediction Steps

• Structure prediction through optimizing
  atom-level energy potential
• CHARMM19 force field for helix-helix interaction
• Knowledge-based energy function for lipid-helix
  interaction
• Idealized and rigid helix structure for backbone
  and sidechain flexible
• Apply helix orientation constraint (i.e., N-term
  inside/outside cell)
• MC moves translations, rotations, rotation by
  helix axis, and side-chain torsional rotation
• Wang-Landau algorithm for MC simulation

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CHARMM19 Polar Hydrogen Force Field
- nonpolar hydrogen atoms are combined with heavy
atoms they are bound to , - polar hydrogen atoms
are modeled explicitly.
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2D Wang-Landau Sampling in PC1 and E Spaces
LEM2
LEM1
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Effect of Helix-Lipid Interactions Helices AB
of Bacteriorhodopsin
Helix-helix interactions
Helix-helix helix-lipid interactions
Helix-lipid interactions play a critical role in
the correct packing of helices
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Effect of Helix-Lipid Interactions Helix AB of
Bacteriorhodopsin (BR)
Hydrocarbon core region
30 Å
All four LEM structures share essentially the
same contact surfaces. In the native structure,
the polar N-terminals of both helices are located
outside of hydrocarbon core region, resulting in
low helix-lipid energy.
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Docking of a Seven-helix Bundle
Bacteriorhodopsin (1QHJ)
Crystal structure
7 helices, 174 residues, 1619 atoms
A

• CHARMM19 lipid-helix potential
• One month CPU time on one PC

B
A
B
Initial Configuration
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Potential Energy Landscape
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Global Energy Minimum Structure (RMSD3.0 Å)
RED experiment GREY simulation
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Atom-level Structure Prediction (Summary)

• Wang-Landau algorithm proved to be effective for
  the energetics study of TM helix packing
• Prediction results for two-helix and seven-helix
  structures are highly promising
• Practical application of Wang-landau method to
  large systems requires further work.

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Part IV Linking Predictions at Residue- and
Atomistic levels
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Correspondence between simulations at two levels

• A multi-scale hierarchical modeling approach is
  feasible and practical
• LEMs identified at residue-level be used as
  candidates for atomistic simulation
• Using PC vectors from residue-level simulation to
  improve search speed in atomistic simulation.

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Future Works

1. Further improvement of the residue-based folding
   potentials
2. Speed-up and parallelization of Wang-Landau
   sampling
3. Construct a hierarchical computational framework,
   and develop corresponding software package.

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Acknowledgements

• Funding from NSF/DBI, NSF/ITR, NIH, and Georgia
  Cancer Coalition
• Dr. David Landau (Wang-Landau algorithm) and Dr.
  Jim Prestegard (NMR data generation) of UGA
• Thanks DIMACS for invitation to speak here