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Structure Prediction. Tertiary protein structure: protein folding Three main approaches: [1] experimental determination (X-ray crystallography, NMR) [2]

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Presentation on theme: "Structure Prediction. Tertiary protein structure: protein folding Three main approaches: [1] experimental determination (X-ray crystallography, NMR) [2]"— Presentation transcript:

1 Structure Prediction

2 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)

3 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

4 Steps in obtaining a protein structure Target selection Obtain, characterize protein Determine, refine, model the structure Deposit in database

5 X-ray crystallography http://en.wikipedia.org/wiki/X-ray_diffraction Sperm Whale Myoglobin

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8 PDB April 08, 2008 – 50,000 proteins, 25 new experimentally determined structures each day New folds Old folds New PDB structures

9 Example 1wey

10 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

11 Structure Characteristics

12 Beta Sheets

13 Ab Inito Prediction

14 Secondary structure prediction Chou and Fasman (1974) developed an algorithm based on the frequencies of amino acids found in  helices,  -sheets, and turns. Proline: occurs at turns, but not in  helices. GOR (Garnier, Osguthorpe, Robson): related algorithm Modern algorithms: use multiple sequence alignments and achieve higher success rate (about 70-75%) Page 279-280

15 Table

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19 Frequency Domain

20 Neural Networks

21 Training the Network Use PDB entries with validated secondary structures Measures of accuracy – Q 3 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

22 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 Q 3

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24 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

25 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

26 Fold recognition by threading Query sequence Compatibility scores Fold 1 Fold 2 Fold 3 Fold N

27 CASP http://www.predictioncenter.org/casp8/index. cgi

28 SCOP SCOP: Structural Classification of Proteins. http://scop.mrc-lmb.cam.ac.uk/scop/

29 CATH CATH: Protein Structure Classification Class (C), Architecture (A), Topology (T) and Homologous superfamily (H) Class (C), Architecture (A), Topology (T) and Homologous superfamily (H)


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