1. Protein structure prediction
May 15, 2001
Quiz#4 postponed
Writing assignment
Learning objectives-Understand the basis of secondary structure prediction programs. Understand neural networks. Become familiar with manipulating known protein structures with Cn3D.
Workshop-Manipulation of the PTEN protein structure with Cn3D.

2. What is secondary structure?
Two major types:
Alpha Helical Regions
Beta Sheet Regions
Other classification schemes:
Turns
Transmembrane regions
Internal regions
External regions
Antigenic regions

3. Some Prediction Methods
ab initio methods
Based on physical properties of aa’s and bonding patterns
Statistics of amino acid distributions
Chou-Fasman
Position of amino acid and distribution
Garnier, Osguthorpe-Robeson (GOR)
Neural networks

4. Chou-Fasman Rules (Mathews, Van Holde, Ahern
Amino Acid -Helix -Sheet Turn
Ala
Cys
Leu
Met
Glu
Gln
His
Lys
Val
Ile
Phe
Tyr
Trp
Thr
Gly
Ser
Asp
Asn
Pro
Arg
Favors
-Helix
Favors
-Sheet
Favors
-Sheet

5. Chou-Fasman First widely used procedure
If propensity in a window of six residues (for a helix) is above a certain threshold the helix is chosen as secondary structure.
If propensity in a window of five residues (for a beta strand) is above a certain threshold then beta strand is chosen.
The segment is extended until the average propensity in a 4 residue window falls below a value.
Output-helix, strand or turn.

6. GOR
Position-dependent propensities for helix, sheet or turn is calculated for each amino acid. For each position j in the sequence, eight residues on either side of aaj is considered. It uses a PSSM
A helix propensity table contains info. about propensity for certain residues at 17 positions when the conformation of residue j is helical. The helix propensity tables have 20 x 17 entries.
The predicted state of aaj is calculated as the sum of the position-dependent propensities of all residues around aaj.

7. Neural networks
Computer neural networks are based on simulation of adaptive
learning in networks of real neurons.
Neurons connect to each other via synaptic junctions which are either
stimulatory or inhibitory.
Adaptive learning involves the formation or suppression of the right
combinations of stimulatory and inhibitory synapses so that a set
of inputs produce an appropriate output.

8. Neural Networks (cont. 1)
The computer version of the neural network involves
identification of a set of inputs - amino acids in the
sequence, which transmit through a network of
connections.
At each layer, inputs are numerically
weighted and the combined result passed to the next
layer.
Ultimately a final output, a decision, helix, sheet or
coil, is produced.

9. Neural Networks (cont. 2)
90% of training set was used (known structures)
10% was used to evaluate the performance of the neural
network during the training session.

10. Neural Networks (cont. 3)
During the training phase, selected sets of proteins of known structure are scanned, and if the decisions are incorrect, the input weightings are adjusted by the software to produce the desired result.
Training runs are repeated until the success rate is maximized.
Careful selection of the training set is an important aspect of this technique. The set must contain as wide a range of different fold types as possible, but without duplications of structural types that may bias the decisions.

11. Neural Networks (cont. 5)
An additional component of the PSIPRED procedures involves sequence alignment with similar proteins.
The rationale is that some amino acids positions in a sequence contribute more to the final structure than others. (This has been demonstrated by systematic mutation experiments in which each consecutive position in a sequence is substituted by a spectrum of amino acids. Some positions are remarkably tolerant of substitution, while others have unique requirements.)
To predict secondary structure accurately, one should place little weight on the tolerant positions, which clearly contribute little to the structure, and strongly emphasize the intolerant positions.

12. PSIPRED Uses multiple aligned sequences for prediction.
Uses training set of proteins with known structure.
Uses a two-stage neural network to predict structure based on position specific scoring matrices generated by PSI-BLAST (Jones, 1999)
First network converts a window of 15 aa’s into a raw score of h,b,c or terminus
Second network filters the first output. For example, an output of hhhhehhhh might be converted to hhhhhhhhh.
Can obtain a Q3 value of 70-78% (may be the highest achievable)

13. three outputs are helix, strand or coil
Provides info
on tolerant or
intolerant positions
Column specifies position within the protein
15 groups of 21 units
(1 unit for each aa plus
one specifying the end)
Filtering network
three outputs are helix, strand or coil

14. Example of Output from PSIPRED
PSIPRED PREDICTION RESULTS
Key
Conf: Confidence (0=low, 9=high)
Pred: Predicted secondary structure (H=helix, E=strand, C=coil)
AA: Target sequence
Conf:
Pred: CCEEEEEEEHHHHHHHHHHCCCCCCHHHHHHCCCCCEEEEECCCCCCHHHHHHHCCCCCC
AA: KDIQLLNVSYDPTRELYEQYNKAFSAHWKQETGDNVVIDQSHGSQGKQATSSVINGIEAD

15. 3D structure prediction-Threading
Threading, alluded to earlier, is a mechanism to address the alignment of two sequences that have <30% identity and are typically considered non-homologous. Essentially, one fits—or threads—the unknown sequence onto the known structure and evaluates the resulting structure’s fitness using environment- or knowledge-based potentials.

16. Helical Wheel If you can predict an alpha helix it is sometimes useful
to be able to tell if the helix is amphipathic. This would indicate
whether one face of the helix faces the solvent or perhaps another
protein. They have been particularly useful in predicting a
“super-secondary” structure known as coiled coils.
The helical wheel is based on the ideal alpha helix placing an amino acid every 100* around the circumference of the helix cylinder

17. Coiled-coil predictors
The alpha-helical coiled-coil structure has a strong signature
heptad pattern abcdefg where a and d are typically non
polar (leucine rich) and e and g are often charged. This makes
scoring from a sequence scale plot relatively easy.

18. 3D structure data
The largest 3D structure database is the Protein Database
It contains over 15,000 records
Each record contains 3D coordinates for macromolecules
80% of the records were obtained from X-ray diffraction studies, 16% from NMR and the rest from other methods and theoretical calculations

19. Part of a record from the PDB
ATOM N ARG A N
ATOM CA ARG A C
ATOM C ARG A C
ATOM O ARG A O
ATOM CB ARG A C
ATOM CG ARG A C
ATOM CD ARG A C
ATOM NE ARG A N
ATOM CZ ARG A C
ATOM NH1 ARG A N
ATOM NH2 ARG A N

20. Molecular Modeling DB (MMBD)
Relies on PDB for data
It contains over 10,000 structure records
Links connect the records to Medline and NCBI’s taxonomy database
Sequence “neighbors” of the structures are are provided by BLAST.
Structure “neighbors” are provided by VAST algorithm.
Cn3D is a molecular graphics viewer that allows one to view the three-dimensional structure.