PowerPoint PresentationSlide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Protein threading algorithms1. GenTHREADER Jones, D. T. JMB(1999) 287, 797-8152. Protein Fold Recognition by Prediction-based Threading Rost, B., Schneider, R. & Sander, C. JMB(1997)270,471-480Presented by Jian QiuWhy do we need protein threading? To detect remote homologue  Genome annotation Structures are better conserved than sequences. Remote homologues with low sequence similarity may share significant structure similarity. To predict protein structure based on structure template Protein A shares structure similarity with protein B. We could model the structure of protein A using the structure of protein B as a starting point.An successful example by GenTHREADER ORF MG276 from Mycoplasma genitalium was predicted to share structure similarity with 1HGX. MG276 shares a low sequence similarity (10% sequence identity) with 1HGX.Supporting Evidence: MG276 has an annotation of adenine phosphoribosyltransferase, based on high sequence similarity to the Escherichia coli protein; 1HGX is a hypoxanthine-guanine-xanthine phosphoribosyltransferase from the protozoan parasite Tritrichomonas foetus.  Four functionally important residues in 1HGX are conserved in MG276.  The secondary structure prediction for ORF MG276 agrees very well with the observed secondary structure of 1HGX.Structure of 1HGXFunctional residue conservation between 1HGX and MG276GenTHREADER ProtocolSequence alignment For each template structure in the fold library, related sequences were collected by using the program BLASTP. A multiple sequence alignment of these sequences was generated with a simplified version of MULTAL. Get the optimal alignment between the target sequence and the sequence profile of a template structure with dynamic programming.Threading PotentialsPairwise potential (the pairwise model family): k: sequence separation s: distance interval mab: number of pairs ab observed with sequence separation k weight given to each observation fk(s): frequency of occurrence of all residue pairs fkab(s): frequency of occurrence of residue pair abSolvation potential (the profile model family): r: the degree of residue burial the number of other C atoms located within 10 Å of the residue's C atomfa(r): frequency of occurrence of residue a with burial rf (r): frequency of occurrence of all residues with burial rVariables considered to predict the relationship Pairwise energy score Solvation energy score Sequence alignment score Sequence alignment length Length of the structure Length of the target sequenceArtificial Neural NetworkA nodeNeural network architecture in GenTHREADERThe effects of sequence alignment score and pairwise potential on the Network outputConfidence level with different network scoresLow Medium(80%) High(99%)Certain(100%)Genome analysis of Mycoplasma genitaliumAll the 468 ORFs were analyzed within one day.Distribution of protein folds in M. genitaliumPHD: Predict 1D structure from sequenceMaxHomSequenceMultiple Sequence AlignmentPHDsec PHDaccSecondary structure:H(helix), E(strand),L(rest)Solvent accessibility:Buried(<15%), Exposed(>=15%)Threading ProtocolSimilarity matrix in dynamic programming Purely structure similarity matrix: six states (combination of three secondary structure states and two solvent accessibility states) Purely sequence similarity matrix: McLachlan or Blosum62 Combination of strcture and sequence similarity matrix: Mij=Mij1D structure + (100-)Mijsequence  sequence alignment only  1Dstructure alignment onlyPerformance of the algorithmResults on the 11 targets of CASP1 Correctly detected the remote homologues at first rank in four cases; Average percentage of correctly aligned residues: 21%; Average shift: nine residues.Best performing methods in CASP1: Expert-driven usage of THREADER by David Jones and colleagues detected five out of nine proteins correctly at first rank.  Best alignments of the potential-based threading method by Manfred Sippl and colleagues were clearly better than the best ones of this