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Predicting The Beta-Helix Fold From Protein Sequence Data

Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger. MIT. Structural Motif Recognition.

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Predicting The Beta-Helix Fold From Protein Sequence Data

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  1. Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

  2. Structural Motif Recognition Problem: Given a structural motif (secondary, super-secondary, tertiary), predict its presence from sequence data alone. Example: Coiled-coil prediction (Berger et al. 1995) GCN4 leucine zipper

  3. Long Distance Correlations In beta structures, amino acids close in the folded 3D structure may be far away in the linear sequence Cyclophilin A

  4. The Right-handed Parallel Beta-Helix A processive fold composed of repeated super-secondary units. Each rung consists of three beta-strands separated by turn regions. No sequence repeat. Pectate Lyase C (Yoder et al. 1993)

  5. Biological Importance of Beta Helices • Surface proteins in human infectious disease: • virulence factors • adhesins • toxins • allergens • Proposed as a model for amyloid fibrils (e.g. Alzheimer’s and CJD) • Virulence factors in plant pathogens

  6. What is Known Solved beta-helix structures: 12 structures in PDB in 7 different SCOP families Pectate Lyase: Pectate Lyase C Pectate Lyase E Pectate Lyase Galacturonase: Polygalacturonase Polygalacturonase II Rhamnogalacturonase A Pectin Lyase: Pectin Lyase A Pectin Lyase B Chondroitinase B Pectin Methylesterase P.69 Pertactin P22 Tailspike

  7. Approaches to Structural Motif Recognition General Methods: Sequence similarity searches Multiple alignments & profile HMMs Threading Profile methods (3D & 1D) -Heffron et al. (1998) *Statistical Methods

  8. BetaWrap Program • Performance: • On PDB: no false positives & no false negatives. Recognizes beta helices in PDB across SCOP families in cross-validation. • Recognizes many new potential beta helices when run on larger sequence databases. • Runs in linear time (~5 min. on SWISS-PROT).

  9. BetaWrap Program • Histogram of protein scores for: • beta helices not in database (12 proteins) • non-beta helices in PDB (1346 proteins )

  10. Single Rung of a Beta Helix

  11. 3D Pairwise Correlations B3 T2 B2 Aligned residues in adjacent beta-strands exhibit strong correlations Residues in the T2 turn have special correlations (Asparagine ladder, aliphatic stacking) B1

  12. 3D Pairwise Correlations B3 T2 B2 Stacking residues in adjacent beta-strands exhibit strong correlations Residues in the T2 turn have special correlations (Asparagine ladder, aliphatic stacking) B1

  13. Question: how can we find these correlations which are a variable distance apart in sequence? Phage P22 Tailspike

  14. Finding Candidate Wraps • Assume we have the correct locations of a • single T2 turn (fixed B2 & B3). Candidate Rung B3 T2 B2 • Generate the 5 best-scoring candidates for the next rung.

  15. Scoring Candidate Wraps (rung-to-rung) Rung-to-rung alignment score incorporates: • Beta sheet pairwise alignment preferences taken from amphipathic beta structures in PDB. • (w/o beta helices) • Additional stacking bonuses • on internal pairs. • Distribution on turn lengths.

  16. Scoring Candidate Wraps (5 rungs) • Iterate out to 5 rungs generating candidate wraps: • Score each wrap: • - sum the rung-to-rung scores • - B1 correlations filter • - screen for alpha-helical content

  17. Key Features of Our Approach • Structural model • Statistical score • Dynamic search

  18. Predicted Beta Helices • Features of the 200 top-scoring proteins in the NCBI’s protein sequence database: • Many proteins of similar function to the known beta-helices; some with similar sequences. • A significant fraction are characterized as microbial outer membrane or cell-surface proteins. • Mouse, human, worm and fly sequences significantly underrepresented – only two proteins!

  19. Some Predicted Beta Helices in Human Pathogens Cholera Ulcers Malaria Venereal infection Respiratory infection Listeriosis Sleeping sickness Lyme disease Leishmaniasis Respiratory infection Sleeping sickness Whooping cough Anthrax Rocky Mtn. spotted fever Oriental spotted fever Meningitis Legionnaire’s disease Vibrio cholerae Helicobacter pylori Plasmodium falciparum Chlamyidia trachomatis Chlamydophilia pneumoniae Listeria monocytogenes Trypanosoma brucei Borrelia burgdorferi Leishmania donovani Bordetella bronchiseptica Trypanosoma cruizi Bordetella parapertussis Bacillus anthracis Rickettsia ricketsii Rickettsia japonica Neisseria meningitidis Legionaella pneumophilia

  20. Predicted Beta Helices False positives? Also present in the top 200 proteins are members of the LRR and hexapeptide repeat families. Hexapeptide repeat LRR

  21. Structural Features of Beta-Helices • B2-T2-B3 region is well-conserved. • T1 and T3 turns highly variable (from 2 to 63 residues in length). • Active site is an extended surface, formed by T3, B1, T1. • Distinctive internal stacking interactions. A single rung of Pectate Lyase C

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