Using PERL in Projects Related to Protein Structure Prediction
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1 Using PERL in Projects Related to Protein Structure Prediction Ingo Ruczinski Department of Biostatistics Johns Hopkins University iruczins ollaborators David Baker Richard Bonneau hris Bystroff harles Kooperberg arol Rohl Kim Simons harlie Strauss Jerry Tsai University of Washington Institute for Systems Biology Rensselaer Polytechnic Institute Fred Hutchinson ancer Research tr U Santa ruz Harvard University Los Alamos ational Laboratory Texas A&M
2 What are Proteins? Without peptide bonds With peptide bonds The building blocks of proteins are amino acids. D and D Protein Structure Both figures show the same protein, highlighting the tertiary and secondary structure.
3 Motivation What are proteins? Why do we care about them? Why do we care about protein structure? Why do we need to predict protein structures? How does the computational approach work? Energy Landscape The free energy of a structure changes with its geometry.
4 A Scoring Function for Ab Initio Protein Folding P(structure sequence) P(sequence structure) P(structure) Sequence dependent: hydrophobic burial residue pair interaction Sequence independent: helix-strand packing strand-strand packing sheet configurations vdw interactions Hydrophobic Burial Y W V T S R Q P amino acid M L K I H G F E D A number of neighbours
5 Strand-Strand Interaction theta phi Energy Landscape () True landscape Our scoring function Decoys tend to cluster near low energy states
6 Structure Predictions T-A: PPase (Domain : -9) ative Model T-B: PPase (Domain : -) ative Model T9: Hypothetical Protein HI ative()/ Model (D) D D T: Protein Spb ative Model T: Ribosome Binding Factor A ative Model T-A: MutS (Domain : -) ative Model T-B: MutS (Domain : -9) ative Model Functional Annotation
7 Perl Scripts Perl scripts are used all over the place! Some applications include: Accessing and transforming sequence data from local and remote databases omparing amino acid sequences Extracting information from sequence alignments omparing protein structures Parsing structures (sequences) into domains reating decoy files and managing them heck out the bioperl tutorial ( Beta-Sheet Motifs Two possible motifs for -stranded sheets.
8 Bias towards Local onformations non-local pairs pgx sro 9 non-local pairs ah9 hqi non-local pairs vif 9 ptl 9 non-local pairs ksr 9 9 vqh 9 local pairs local pairs local pairs local pairs Local structures are easier to generate than non-local structures. Likely Sheet Topologies L L L L L L L L 9 9
9 References Publications: Simons KT, Ruczinski I, Kooperberg, Fox B, Bystroff, and Baker D (999), Improved Recognition of ative-like Protein Structures using a ombination of Sequencedependent and Sequence-independent Features of Proteins, Proteins: Structure, Function and Genetics () -9. Bonneau R, Tsai J, Ruczinski I, and Baker D (), Rosetta in ASP: Progress in Ab Initio Protein Structure Prediction, Proteins: Structure, Function and Genetics (S), 9-. Ruczinski I, Kooperberg, Bonneau R, and Baker D (), Distributions of Beta Sheets in Proteins with Application to Structure Prediction, Proteins: Structure, Function and Genetics, -9. Bonneau R, Tsai J, Ruczinski I, and Baker D (), ontact Order and Ab Initio Protein Structure Prediction, Protein Science (), 9-9. References Web Pages: My Home Page iruczins/ lass otes and Stuff iruczins/teaching/misc/misc.html The Protein Data Bank The Dunbrack Lab The Baker Lab The ASP Page The Bioperl Page The Beta Sheet Page iruczins/sheets/sheets.html
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