Abinitio protein structure prediction

1
Ab-initio protein structure prediction
Chen Keasar BGU
?
Any educational usage of these slides is
welcomed. Please acknowledge. keasar_at_cs.bgu.ac.il
2
The problem Predict the three dimensional
structure of a protein based on its sequence.
TVFAIYDYDFK.. TEDDAGSFHEK
TLUNSGDGDWW TGYVGSSYV
Chen Keasar BGU
3
How can we predict protein structures?
Chen Keasar BGU
4
Why is ab-initio prediction hard?
Chen Keasar BGU
5
Ab-initio is hard, why do it? Wait until enough
proteins are solved and use homology
modeling/fold-recognition
Chen Keasar BGU
6
Chen Keasar BGU
7

• Because homology modeling tells us nothing about
  the physical nature of the protein folding and
  stability.
• Because ab-initio methods can augment
  fold-recognition and homology (refinement, large
  loops, side chains).
• Because of ORFans (orphan ORFs).
• Because it can ease experimental structure
  determination.
• Because prediction is the basis of design.

Chen Keasar BGU
8
ab-initio protein structure prediction
Chen Keasar BGU
9
Simulating the actual folding process
Model I quantum description of the system
dimer a CHOOH
Chen Keasar BGU
10
Model II

• Semi-empirical energy functions forcefields
• Classic world no quantum effects (that is no
  chemistry).
• Parameterized to reproduce experimental results
  for small molecules. Their use for proteins is an
  extrapolation.
• The basic element is an atom
• Unbreakable.
• Represented by the X,Y,Z coordinates of its
  center.
• Its attributes (volume, charge, mass etc.) are
  the basic parameters of the energy function.

Chen Keasar BGU
11
Chen Keasar BGU
12
Chen Keasar BGU
13
Chen Keasar BGU
14
Chen Keasar BGU
15
The good news The model is rather accurate and
correctly describe many natural phenomena. ?

• The bad news
• Each time step is hard to compute.
• An order of 1012 steps are needed to simulate
  protein folding. ?

Chen Keasar BGU
16
Ab-initio protein structure prediction as an
optimization problem

• Define a function that map protein structures to
  some quality measure.

• Solve the computational problem of finding an
  optimal structure.
• ?

Chen Keasar BGU
17

• A dream function
• ? Has a clear minimum in the native structure.
• ? Has a clear path towards the minimum.
• ? Global optimization algorithm should find the
  native structure.

Chen Keasar BGU
18

• An approximate function
• ? Easier to design and compute.
• ? Native structure not always the global
  minimum.
• ? Global optimization methods do not converge.
  Many alternative models (decoys) should be
  generated.

Chen Keasar BGU
19

• An approximate function
• ? Easier to design and compute.
• ? Native structure not always the global
  minimum.
• ? Global optimization methods do not converge.
  Many alternative models (decoys) should be
  generated.
• ? No clear way of choosing among them.

Chen Keasar BGU
20

• Energy functions
• Typically include terms for hydrophobicity,
  hydrogen bonds etc.
• Typically based on the distribution of structural
  features (say contacts between alanine residues
  and arginine residues) in the PDB. The more
  frequent is the feature the lower is the energy
  associated with it.

• Assumptions
• These features are independent.
• The proteins in the PDB are a representative
  sample of conformation space.

A small problems these assumptions are wrong.
A brilliant solution ignore it.
Chen Keasar BGU
21
Basic element
electrons protons
AMBR ECEP CHARM OPLS ENCAD GROMOS
atom
heavy atom
Levitt Keasar
Baker (Rosetta)
Scheraga 1998
Levitt 1976
half a residue
Jones
Skolnik 1998
Park Levitt
Osguthorpe
residue
Skolnik 2000
Some residues
Hinds Levitt
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU
22
Basic element
electrons protons
atom
extended atom
half a residue
residue
Some residues
Hinds Levitt
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU
23
Basic element
electrons protons
atom
extended atom
half a residue
Park Levitt
residue
Some residues
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU
24
Basic element
electrons protons
atom
extended atom
half a residue
residue
Skolnik 2000
Some residues
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU
25
Basic element
electrons protons
atom
extended atom
Scheraga 1998
half a residue
residue
Some residues
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU
26
Basic element
electrons protons
Apparently the best current method
AMBR ECEP CHARM OPLS ENCAD GROMOS
atom
extended atom
Levitt, Keasar
Baker (Rosetta)
Scheraga 1998
Levitt 1976
half a residue
Jones
Skolnik 1998
Park Levitt
Osguthorpe
residue
Skolnik 2000
Some residues
Hinds Levitt
diamond lattice
fine square lattice
fragments
torsion angle lattice
continuous
Chen Keasar BGU