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BYU BIO 465 - Structure Prediction

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Structure PredictionSlide 2Slide 3Slide 4X-ray crystallographySlide 6Slide 7PDBExample 1weyAb initio protein predictionStructure CharacteristicsBeta SheetsAb Inito PredictionSlide 14TableSlide 16Slide 17Slide 18Frequency DomainNeural NetworksTraining the NetworkCorrelation CoeficientArtificial Neural NetworkDangerProfile network from HeiDelbergPHDFold recognition (structural profiles)ThreadingSlide 29CASPSCOPCATHStructure PredictionTertiary protein structure: protein foldingThree 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 crystallizationSteps in obtaining a protein structure Target selection Obtain, characterize protein Determine, refine, model the structure Deposit in databaseX-ray crystallographyhttp://en.wikipedia.org/wiki/X-ray_diffractionSperm Whale MyoglobinPDB•April 08, 2008 – 50,000 proteins, 25 new experimentally determined structures each dayNew foldsOld foldsNew PDB structuresExample 1weyAb 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 structuresStructure CharacteristicsBeta SheetsAb Inito PredictionSecondary structure predictionChou and Fasman (1974) developed an algorithmbased 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 algorithmModern algorithms: use multiple sequence alignmentsand achieve higher success rate (about 70-75%) Page 279-280TableFrequency DomainNeural NetworksTraining 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 metricCorrelation 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 Q3Artificial Neural NetworkPredictsStructure at this pointDanger•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 structureProfile 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 selectionPHDPredicts 4Predicts 6Predicts 6Predicts 5Predicts 5Fold 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 foldsThreading•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 mannerFold recognition by threadingQuery sequenceCompatibility scoresFold 1Fold 2Fold 3Fold NCASP•http://www.predictioncenter.org/casp8/index.cgiSCOP•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


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BYU BIO 465 - Structure Prediction

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