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Structure Prediction

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).

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Structure Prediction

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  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

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

  7. Example 1wey

  8. 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

  9. Structure Characteristics

  10. Beta Sheets

  11. Ab Inito Prediction

  12. 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

  13. Table

  14. Frequency Domain

  15. Neural Networks

  16. 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

  17. 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

  18. Artificial Neural Network Predicts Structure at this point

  19. 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

  20. 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

  21. PHD Predicts 4 Predicts 5 Predicts 6

  22. 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

  23. 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

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

  25. CASP • http://www.predictioncenter.org/casp8/index.cgi

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

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

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