Post on 21-Dec-2015
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Protein Structure Prediction
[Based on Structural Bioinformatics, section VII]
Predicting protein 3d structure
Goal: 3d structure from 1d sequence
What kind of fold the given sequence may
adopt?
Fold recognition
Comparative modeling
ab-initio
An existing fold
A new fold
Measuring progress
CASP – Critical Assessment of Structure Prediction
CAFASP – Critical Assessment of Fully Automated Structure Prediction
Targets: unpublished NMR or X-ray structuresGoal: predict target 3d structure and submit it
for independent and comparative review
What Forces Hold the Structure?
Hydrogen Bonds
What Forces Hold the Structure?
• Charge-charge interactions• Positive charged groups prefer to be
situated against negatively charged groups
• Hydrophobic effect
What Forces Hold the Structure?
Disulfide bonds S-S bonds between
Cysteine residues
Homology modeling
Based on the two major observations:
1. The structure of a protein is uniquely defined by its amino acid sequence.
2. Similar sequences adopt practically identical structures, distantly related sequences still fold into similar structures.
Growth of the Protein Data Bank
Fraction of New Folds
[Rost, Protein Eng. 1999]
Two zones of sequence alignment
The 7 steps to homology modeling
1. Template recognition and initial alignment― BLAST, FASTA
2. Alignment correction― Better alignment, MSA
The 7 steps to homology modeling
3. Backbone generation― Copy backbone atoms [and side-chains
of conserved residues]
4. Loop modeling― Knowledge based― Energy based
The 7 steps to homology modeling
5. Side-chain modeling― Rotamer: a low energy
side-chain conformation― Rotamer library [backbone
independent, dependent]― HUGE search space [~5N]
High accuracy for residues in the hydrophobic core [90%], much lower for residues in the surface [50%]
The 7 steps to homology modeling
6. Model optimization― Predict the side-chains, then the resulting
shifts in the backbone, then the rotamers for the new backbone …
7. Model validation― Calculating the model’s energy― Determination of normality indices:
― bond lengths, bond and torsion angles― Inside/outside distribution of polar residues― Radial distribution function
Predicting protein 3d structure
Goal: 3d structure from 1d sequence
What kind of fold the given sequence may
adopt?
Fold recognition
Comparative modeling
ab-initio
An existing fold
A new fold
Fold recognition
Which of the known folds is likely to be similar to the (unknown) fold of a new protein when only its amino-acid sequence is known?
Fraction of new folds (PDB new entries in 1998)
Koppensteiner et al., 2000,Koppensteiner et al., 2000,JMB 296:1139-1152.JMB 296:1139-1152.
Unrelated proteins adopt similar folds
Only 100 folds account for ~50% of all protein superfamilies
Possible explanations:1. Divergent evolution2. Convergent evolution3. Limited number of folds4. Misguided analysis
Proteins as seen by a Biologist
Does a new protein sequence belong to a given family of proteins (with a specific set of mutation rules)?
Fold recognition is based on:• Sequence alignment, multiple sequence
alignment• Profile HMM, PSI-BLAST
Proteins as seen by a Physicist
“Thermodynamic hypothesis”: The native conformation of a protein corresponds to a global free energy minimum of the system (protein + solvent)
Naïve approach: having a correct energy function, search for the native structure in the conformational space
Threading
Threading: energy based fold recognition
Define:1. Protein model and interaction description2. Alignment algorithm3. Energy parameterization
11
22
33
44
55
66
77
1010
88
99
AA
CC
CC
EE
CC
AA
DDAA
AA
CCEEabab A C D E …..
A -3 -1 0 0 ..C -1 -4 1 2 ..D 0 1 5 6 ..E 0 2 6 7 ... . . . .
E Eji, positions
ba ji
MAHFPGFGQSLLFGYPVYVFGD...
Potential fold
...
1) ... 56) ... n)
...
-10 ... -123 ... 20.5
Find best fold for a protein sequence:
Fold recognition (threading)
GenTHREADER(Jones , 1999, JMB 287:797-815)
For each template provide MSA align the query sequence with the MSA assess the alignment by sequence
alignment score assess the alignment by pairwise
potentials assess the alignment by solvation function record lengths of: alignment, query,
template
Essentials of GenTHREADER
Predicting protein 3d structure
Goal: 3d structure from 1d sequence
What kind of fold the given sequence may
adopt?
Fold recognition
Comparative modeling
ab-initio
An existing fold
A new fold
Ab-initio folding
Goal: Predict structure from “first principles”
Requires: A free energy function, sufficiently close to
the “true potential” A method for searching the conformational
space
Benefits: Works for novel folds Shows that we understand the process
Ab-initio folding – the challenge
1. Current potential functions have limited accuracy
2. The conformational space is HUGE
Possible simplifications: Reduced representation Simplified potentials Coarse search strategies
Representation
Detailed representation – include all atoms of the protein and the surrounding solvent computational expansive
• Implicit solvent models• United atom representation• Side-chain as centroid or cα
• Restricted side-chain configurations (rotamers)
• Restricted backbone torsion angles
Rosetta[Simons et al. 1997]
• “Structural” signatures are reoccurring within protein structures
• Use these as cues during structure search
I-sites Library – a catalog of local sequence-structure correlations
Serine hairpin Type-I hairpin Frayed helix
Fragment insertion Monte Carlo
Energyfunctionchange
backbone angles
Convert to 3D
accept or reject
Choose a fragment
frag
men
tsbackbone torsion angles
Rosetta: a folding simulation program
evaluate
Potential functions
• Molecular mechanics – models the forces that determines protein conformation
• Van der Waals: Lennard-Jones 12-6• Electrostatic: Coulomb’s law
• Scoring functions – empirically derived from solved structures
• Useful with reduced complexity models• Useful in treating aspects of protein
thermodynamics
Search methods
• Molecular dynamics – Simulates the motion of a molecule in a given potential
• Impractical …
• Coarse sampling of energy landscape:• Simulated annealing, genetic algorithms,
…