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Secondary structure prediction from amino acid sequence.

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Presentation on theme: "Secondary structure prediction from amino acid sequence."— Presentation transcript:

1 Secondary structure prediction from amino acid sequence

2 Homology: Paralogs and orthologs a ab duplication speciation species 1species 2 abab Paralogs = gene families in same species orthologs

3 Amino acid primary sequence 2. Homologue(s) with known 3D structure? Homology modelling available 1. Search for sequence homologue(s) and construct an alignment 3. Motif recognition: Search secondary databases Secondary structure prediction Fold assignment Physico-chemical properties (e. g., using EMBOSS suite) DNA sequence Automatic translation Primary db searches FASTA, BLAST

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5 Chou-Fasman Parameters Amino acid propensities

6 Q3 score Q3 = q  +q  +q coil X 100% total no. of residues Accuracy of prediction

7 Recent improvements The availability of large families of homologous sequences has greatly enhanced secondary structure prediction. The combination of sequence data in multiple alignments with sophisticated computing techniques such as neural networks has lead to accuracies well in excess of 70 %. The limit of 70-80% may be a function of secondary structure variation within homologous proteins.

8 Stereochemical analysis Patterns of residue conservation are indicative of particular secondary structure types. Alpha helices have a periodicity of 3.6. Many alpha helices in proteins are amphipathic, meaning that one face is pointing towards the hydrophobic core and the other towards the solvent. Patterns of hydrophobic residue conservation showing the i, i+3, i+4, i+7 pattern are highly indicative of an alpha helix. XOOXXOOX

9 Stereochemical analysis The geometry of beta strands means that adjacent residues have their side chains pointing in oppposite directions. Beta strands that are half buried in the protein core will tend to have hydrophobic residues at positions i, i+2, i+4, i+8 etc, and polar residues at positions i+1, i+3, i+5, etc. XOXOXOXOXO

10 Stereochemical analysis Beta strands that are completely buried (as is often the case in proteins containing both alpha helices and beta strands) usually contain a run of hydrophobic residues. XXXXXXXXXXXX

11 Helical transmembrane proteins Strong hydrophobicity signal from membrane spanning regions, each ~25 residues in length Predominance of positively charged amino acid residues on cytoplasmic side Prediction accuracy with multiple alignment = 95% +

12 Helical transmembrane proteins ~30% of top 100 drugs bind to membrane proteins Difficult to determine experimentally But much easier to predict than globular proteins! TMpred – based on statistical analysis of transmembrane proteins TMHMM – based on Hidden Markov Model

13 Protein Structure Classification Class(C)Class(C) secondary structure content – mainly alpha, mainly beta, alpha/beta, few secondary structures (type) Architecture(A)Architecture(A) gross arrangement of sec. structure elements (type and number of SS elements) Topology(T)Topology(T) shape and connectivity of SS (type, number and order of SS elements) Homologous superfamily (H)

14 Topology

15 Class Architecture Topology H-level Fold families Homologous domains, share common ancestor

16 Class Architecture Topology H-level Fold families Homologous domains, share common ancestor In CATH, the assignments of structures to fold groups and homologous superfamilies are made by sequence and structure comparisons.

17 Architecture: ‘Barrel’ 9 Topologies : type of SS, number and order Homologous domain family ?

18 Secondary structure prediction methods PSI-pred (PSI-BLAST profiles used for prediction; David Jones, Warwick)PSI-pred JPRED Consensus prediction (includes many of the methods given below; Cuff & Barton, EBI)JPRED DSC King & SternbergDSC PREDATORFrischman & Argos (EMBL)PREDATOR PHD home page Rost & Sander, EMBL, GermanyPHD home page ZPRED server Zvelebil et al., Ludwig, U.K.ZPRED server nnPredict Cohen et al., UCSF, USA.nnPredict BMERC PSA Server Boston University, USABMERC PSA Server SSP (Nearest-neighbor) Solovyev and Salamov, Baylor College, USA.SSP (Nearest-neighbor)

19 Consensus prediction method hydrophobic highly conservedb= buried, e = exposed

20 Consensus prediction method -JPRED hydrophobic highly conservedb= buried, e = exposed amphipathichydrophobic

21 Neural network prediction - PHD Multiple alignment of protein family SS profile for window of adjacent residues

22 Hidden Markov Models-HMMSTR amino acid secondary structure element structural context Markov state Recurrent local features of protein sequences Accuracy of 74% Bystroff et al., 2000


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