IV. Protein Structure Prediction and Determination
•
Methods of protein structure determination
•
Critical assessment of structure prediction
•
Homology modelling
•
Threading
•
Prediction of novel folds
•
Protein design
Methods to determine protein structure
• X-Ray and NMR methods allow to determine the
structure of proteins and protein complexes
• These methods are expensive and difficult
– Could take several work months to process one
proteins
• A centralized database (PDB) contains all solved
protein structures
– XYZ coordinate of atoms within specified precision
– ~19,000 solved structures
X-ray crystallography and NMR are the two major techniques for determining
protein structures
X-ray
X-ray crystallography:
Protein isolation
Crystal
Protein Purification
Phases of diffracted rays
Protein Crystallisation
Electron density
Protein model
X-ray crystallography
Liquid nitrogen is used to freeze the crystal which allows for increased
reliability of information gathered from testing. The area detector, which
collects the diffracted x-rays once they pass through the crystal, is the
black plate located behind the nitrogen stream, (right) sample x-ray
diffraction pattern.
The phase problem:
Isomorphous Replacement: combination of diffraction data from the native
crystal with data from other crystals containing the same protein packed in the
same way but adding a heavy atom
Molecular Replacement: placement of a known relative structure in different
positions and orientations, providing approximate phases
Multiwavelength Anomalous Dispersion: Measurements of the variation of the
intensity distribution in the diffraction pattern over a range of wavelengths
Direct Methods: Knowledge of electron density distributions in crystals permits
calculation of phases directly from experimental data
Phase determined
Experimental data:
Three dimentional coordinates
Relative mobility of atoms
Model built over it
Refinement of the
model comparing
with empirical data
Optimised protein structure
X-ray crystallography
Limitations
• An extremely pure protein sample is needed.
• The protein sample must form crystals that are relatively large
without flaws. Generally the biggest problem.
• Many proteins aren’t amenable to crystallization at all (i.e.,
proteins that do their work inside of a cell membrane).
Measures of structural quality
R-factor is a measure of how well the model reproduces the
experimental intensity data, the lower this factor the better the
structure.
R = 0%
There is no experimental error (ideal)
R = 60%
Atoms placed randomly in the crystal
R  20
Good structure prediction
The free R-factor is an unbiased measure of the agreement between
the model and a subset of experimental data withheld during the
refinement process
Good protein structures:
1.
Are compact as measured by their surface area and packing
density
2.
Have hydrogen bonds with a reasonable geometry, and with all
the hydrogen bonds determined
3.
Their backbone conformation angles are confined to the allowed
areas of the Sasikharan-Ramakrishnan-Ramachandran diagram
Nuclear Magnetic Resonance
Nuclear magnetic resonance (NMR) spectra measure the energy level of the
magnetic nuclei in atoms
This energy depends on the effect transmitted between atoms affecting the
precise frequency of the signal from an atom (chemical shift). This
chemical shift can define secondary structures
NMR can determine the value of conformational angles
Interactions between spatially proximal atoms (< 5Å) can be used by NMR to
determine the closeness of atoms in the structure (Nuclear Overhauser
effect (NOE)
Peaks correspond to the interaction of
pairs of atoms
The spectroscopists has first to correlate peaks with amino acids in the sequence
(Assign the spectrum)
The data generated provide a set of distance constraints and determine the
secondary structure and some indications of the tertiary interactions
Nuclear Magnetic Resonance
• Solving an NMR structure means producing a model or set of models
that manage to satisfy all known NMR distance constraints (generated
by the experiment).
• NMR models are often released in groups of 20-40 models because the
solution to NMR structure determination is much more ambiguous
than x-ray.
• NMR is limited to small, soluble proteins only.
Nuclear Magnetic Resonance
Sample
RMN spectra
Spectra procession
Sequential assignation
Conformational restrictions
3D structure calculation
Refinment
Analysis
NMR vs. X-ray crystallography
NMR models
An X-Ray liquid crystal
Protein Structures
•
in theory, a protein structure can solved computationally
•
a protein folds into a 3D structure to minimizes its potential energy
•
the problem can be formulated as a search problem for minimum energy
–
–
–
the search space is defined by psi/phi angles of backbone and side-chain
rotamers
the search space is enormous even for small proteins!
the number of local minima increases exponentially of the number of residues
Protein Structure Prediction
• ab initio folding methods
– use first principles to computationally fold proteins
– not practical (yet) due to its high computational complexity
• Comparative modeling
– Protein threading – make structure prediction through
identification of “good” sequence-structure fit
– Homology modeling – identification of homologous proteins
through sequence alignment; structure prediction through placing
residues into “corresponding” positions of homologous structure
models
Protein Threading
•
•
the basic idea
– placing a protein sequence onto a structural template “optimally”
– assessing how good the structure is energetically
key components:
– a structural template database
– an “energy” function for measuring quality of a placement
(alignment)
– an algorithm for finding an optimal placement
– a capability for assessing the reliability of prediction
query sequence
MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE
template set
PROSPECT Predictions
t49
t57
actual
predicted
actual
predicted
t68
t70
actual
predicted
actual
predicted
How and Why Threading?
The idea of threading came from the observation that most of the proteins
adopt one of a limited number of folds:
Just 10 folds account for the 50% of similarities between protein
superfamilies
Rather than trying to predict the correct structure from the unlimited
number of possible structures, the protein structure might have been surely
determined before for other proteins
In case that our protein shares obvious similarity with other protein with a
known 3D structure the folding problem is trivial
It is desired, however, that threading might be able to detect structural
similarities that are not accompanied by any detectable similarity
Algoritmos de threading. General.
1. Library of protein structures (fold library)
all known structures
representative subset (seq. similarity filters)
structural cores with loops removed
................
2. Binary alignment algorithm with Scoring function
contact potential
environments
ALMVWTGH.........
Instead of aligning a sequence to a sequence, align strings of
descriptors that represent 3D structual features.
Usual Dynamic Programming: score matrix relates two amino acids
Threading Dynamic Programming: relates amino acids to environments in 3D structure
3. Method for generating models via alignments
Threading Algorithms
Puntuation function
-Amino acids are in similar environments to those
where known structures are found
ALMVWTGH.........
-Contact potentials
-Coincidence of predicted and real secondary structures
and calculation of accessibilities
-Homology matrices obtained from alignment of
structures
- HMMs
................
- Solvatation potentials
Threading algorithms
Contact potential
d
Count pairs of each residue
type at different separations
Energy of interaction =
-KT ln (frequency of interactions)
Boltzmann principle
Jones, 1992; Sippl, 1995
d
Threading algorithms
Sequence profiles + secundary structure
Kelley et al., 2000
http://www.bmm.icnet.uk/~3dpssm
threading. Examples
threading. Examples
threading
Post-processing of the results
Combining with additional information
threading
Post-processing of the results
Filtering models
De Juan et al., 2001
MAKEFGIPAAVAGTVLNVVEAGGWVTTIVSILTAVGSG
GLSLLAAAGRESIKAYLKKEI KKGKRAVIAW
threading
Evaluation of methods
I) CASP 94, 96, 98, 00, 02
EVALUATION
Databases
Algorithm
Computer evaluation
1/3 correct fold (ali?)
MODEL(S)
http://PredictionCenter.llnl.gov/casp4/
PROSPECT Predictions
t49
t57
actual
predicted
actual
predicted
t68
t70
actual
predicted
actual
predicted
Why engineer proteins?
• 1) Engineered macromolecules could have
experimental use as experimental tools, or for
development and production of therapeutics
• 2) During the process of said engineering,
new techniques are developed which expand
options available to research community as
whole
• 3) By approaching macromolecule as
engineer, better understanding of how native
molecules function
(Doyle, Chem & Bio, 1998)
Ligand Binding – protein flexibility
“In this study, we set out to elucidate the
cause for the discrepancy in affinity of a
range of serine proteinase inhibitors for
trypsin variants designed to be structurally
equivalent to factor Xa.”
(Rauh, J. Mol. Biol.,
2004)
Def: Ligand
Any molecule that binds
specifically to a receptor site
of another molecule; proteins
embedded in the membrane
exposed to extracellular fluid.