Protein structure prediction.pptVIP

Protein structure prediction May 24, 2005 Return of Quiz#3 Writing assignments-please hand in. Learning objectives-Understand the basis of secondary structure prediction programs. Become familiar with the databases that hold secondary structure information. Understand neural networks and how they help to predict secondary structure. Workshop-Analysis of a portion of p53 with PSIRED and BLIMPS. What is secondary structure? Some Prediction Methods ab initio methods Based on physical properties of aa’s and bonding patterns Statistics of amino acid distributions in known structures Chou-Fasman Position of amino acid and distribution Garnier, Osguthorpe-Robeson (GOR) Neural networks Chou-Fasman First widely used procedure Output-helix, strand or turn GOR (Garnier, Osguthorpe-Robeson) Psi-BLAST Predict Secondary Structure (PSIPRED) Three stages: 1) Generation of sequence profile 2) Prediction of initial secondary structure 3) Filtering of predicted structure PSIPRED Uses multiple aligned sequences for prediction. Uses training set of folds with known structure. Uses a two-stage neural network to predict structure based on position specific scoring matrices generated by PSI-BLAST (Jones, 1999) First network converts a window of 15 aa’s into a raw score of h,e (sheet), c (coil) or terminus Second network filters the first output. For example, an output of hhhhehhhh might be converted to hhhhhhhhh. Can obtain a Q3 value of 70-78% (may be the highest achievable) Neural Networks (cont. 1) Neural Networks (cont. 2) Neural Networks (cont. 3) Neural Networks (cont. 4) Example of Output from PSIPRED 3D structure prediction-Threading Recognizing motifs in proteins. PROSITE is a database of protein families and domains. Most proteins can be grouped, on the basis of similarities in their sequences, into a limited number of families. Proteins or protein domains belonging to a particular family generally share functional attributes and are derived from a common ancestor. PROS