Prediction to Protein Structure

Fall 2005

CSC 487/687 Computing for Bioinformatics

Psi-BLAST Predict Secondary Structure (PSIPRED)

Three stages:1) Generation of sequence profile2) Prediction of initial secondary

structure3) Filtering of predicted structure

PSIPRED Uses multiple aligned sequences for prediction. Uses training set of folds 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,e (sheet), c (coil) 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)

Neural networks

• Computer neural networks are based on simulation of adaptivelearning 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 setof inputs produce an appropriate output.

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.

Neural Networks (cont. 2)

90% of training set was used (known structures)10% was used to evaluate the performance of the neuralnetwork during the training session.

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 without duplications of structural types that may bias the decisions.

Neural Networks (cont. 4)

•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 less weight on the tolerant positions, which clearly contribute little to the structure

•One must also put more weight on the intolerant positions.

15 groups of 21 units(1 unit for each aa plusone specifying the end)

Row specifies aa position

three outputs are helix, strand or coil

Filtering network

Provides infoon tolerant orintolerant positions

Example of Output from PSIPRED

Workshop

http://bioinf.cs.ucl.ac.uk/psipred/psiform.html

3D structure data

The largest 3D structure database is the Protein Database It contains over 33,000 records Each record contains 3D coordinates for

macromolecules 80% of the records were obtained from X-ray

diffraction studies, 15% from NMR and the rest from other methods and theoretical calculations

ATOM 1 N ARG A 14 22.451 98.825 31.990 1.00 88.84 N

ATOM 2 CA ARG A 14 21.713 100.102 31.828 1.00 90.39 C

ATOM 3 C ARG A 14 22.583 101.018 30.979 1.00 89.86 C

ATOM 4 O ARG A 14 22.105 101.989 30.391 1.00 89.82 O

ATOM 5 CB ARG A 14 21.424 100.704 33.208 1.00 93.23 C

ATOM 6 CG ARG A 14 20.465 101.880 33.215 1.00 95.72 C

ATOM 7 CD ARG A 14 20.008 102.147 34.637 1.00 98.10 C

ATOM 8 NE ARG A 14 18.999 103.196 34.718 1.00100.30 N

ATOM 9 CZ ARG A 14 18.344 103.507 35.833 1.00100.29 C

ATOM 10 NH1 ARG A 14 18.580 102.835 36.952 1.00 99.51 N

ATOM 11 NH2 ARG A 14 17.441 104.479 35.827 1.00100.79 N

Part of a record from the PDB

Steps to tertiary structure prediction

Comparative protein modeling Extrapolates new structure based on related

family members

Steps

1. Identification of modeling templates

2. Alignment

3. Model building

Identification of modeling templates

One chooses a cutoff value from FastA or BLAST search (10-5)

Up to ten templates can be used but the one with the highest sequence similarity to the target sequence (lowest E-value) is the reference template

C atoms of the templates are selected for superimposition. This generates a structurally corrected multiple sequence

alignment

Alignment

“Common core” of target sequence is threaded onto the template structure using only alpha carbons

Framework construction

Building the model

Framework construction Average the position of each atom in target,

based on the corresponding atoms in template.

Portions of the target sequence that do not match the

template are constructed from a “spare part” algorithm.

Each loop is defined by its length and C atom

coordinates of the four amino acids preceding

and following the loop.

Building the model

Completing the backbone-a library of PDB entries is consulted to add carbonyl groups and amino groups. The 3-D coordinates come from a separate library of pentapeptide backbone fragments. These backbone fragments are fitted onto the target C alpha carbons. The central tri-peptide is averaged from each backbone atom (N,C,C(O)).

Side chains are added from a table of most probable rotamers that depend on backbone conformation.

Model refinement-minimization of energy