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

• April 08, 2008 – 50,000 proteins, 25 new experimentally determined structures each day

New folds

Old folds

New

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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 alpha-

helix, 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 algorithmbased on the frequencies of amino acids found ina 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 alignmentsand 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

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

Query sequence

Compatibility scores

Fold 1

Fold 2

Fold 3

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)