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Structure Prediction
Tertiary protein structure: protein folding
Three main approaches:
[1] experimental determination
(X-ray crystallography, NMR)
[2] Comparative modeling (based on homology)
[3] Ab initio (de novo) prediction
(Dr. Ingo Ruczinski at JHSPH)
Experimental approaches to protein structure
[1] X-ray crystallography
-- Used to determine 80% of structures
-- Requires high protein concentration
-- Requires crystals
-- Able to trace amino acid side chains
-- Earliest structure solved was myoglobin
[2] NMR
-- Magnetic field applied to proteins in solution
-- Largest structures: 350 amino acids (40 kD)
-- Does not require crystallization
Steps in obtaining a protein structure
Target selection
Obtain, characterize protein
Determine, refine, model the structure
Deposit in database
X-ray crystallography
http://en.wikipedia.org/wiki/X-ray_diffraction
Sperm Whale Myoglobin
PDB
New PDB structures
• April 08, 2008 – 50,000 proteins, 25 new experimentally
determined structures each day
Old folds
New folds
Example 1wey
Ab initio protein prediction
• Starts with an attempt to derive secondary structure from the
amino acid sequence
– Predicting the likelihood that a subsequence will fold into an alphahelix, beta-sheet, or coil, using physicochemical parameters or HMMs
and ANNs
– Able to accurately predict 3/4 of all local structures
Structure Characteristics
Beta Sheets
Ab Inito Prediction
Secondary structure prediction
Chou and Fasman (1974) developed an algorithm
based on the frequencies of amino acids found in
a helices, b-sheets, and turns.
Proline: occurs at turns, but not in a helices.
GOR (Garnier, Osguthorpe, Robson): related algorithm
Modern algorithms: use multiple sequence alignments
and achieve higher success rate (about 70-75%)
Page 279-280
Table
Frequency Domain
Neural Networks
Training the Network
• Use PDB entries with validated secondary
structures
• Measures of accuracy
– Q3 Score percentage of protein correctly predicted
(trains to predicting the most abundant structure)
– You get 50% if you just predict everything to be a
coil
– Most methods get around 60% with this metric
Correlation Coeficient
• How correlated are the predictions for coils,
helix and Beta-sheets to the real structures
• This ignores what we really want to get to
– If the real structure has 3 coils, do we predict 3
coils?
• Segment overlap score (Sov) gives credit to
how protein like the structure is, but it is
correlated with Q3
Artificial Neural Network
Predicts
Structure
at this
point
Danger
• You may train the network on your training
set, but it may not generalize to other data
• Perhaps we should train several ANNs and
then let them vote on the structure
Profile network from HeiDelberg
• family (alignment is used as input) instead of just the
new sequence
• On the first level, a window of length 13 around the
residue is used
• The window slides down the sequence, making a
prediction for each residue
• The input includes the frequency of amino acids
occurring in each position in the multiple alignment (In
the example, there are 5 sequences in the multiple
alignment)
• The second level takes these predictions from neural
networks that are centered on neighboring proteins
• The third level does a jury selection
PHD
Predicts 4
Predicts 5
Predicts 6
Fold recognition (structural profiles)
• Attempts to find the best fit of a raw
polypeptide sequence onto a library of known
protein folds
• A prediction of the secondary structure of the
unknown is made and compared with the
secondary structure of each member of the
library of folds
Threading
• Takes the fold recognition process a step
further:
– Empirical-energy functions for residue pair
interactions are used to mount the unknown onto
the putative backbone in the best possible
manner
Fold recognition by
threading
Fold 1
Fold 2
Fold 3
Query
sequence
Compatibility
scores
Fold N
CASP
• http://www.predictioncenter.org/casp8/index.
cgi
SCOP
• SCOP: Structural Classification of Proteins.
• http://scop.mrc-lmb.cam.ac.uk/scop/
CATH
• CATH: Protein Structure Classification
• Class (C), Architecture (A), Topology (T) and
Homologous superfamily (H)