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Protein structure determination & prediction

Protein structure determination & 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

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Protein structure determination & prediction

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  1. Protein structure determination & 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. Nuclear magnetic resonance spectroscopy http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance

  7. Article

  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. 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

  10. 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

  11. 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

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