predicting the beta-helix fold from protein sequence data phil bradley, lenore cowen, matthew menke,...

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Page 1: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT
Page 2: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Predicting The Beta-Helix Fold From Protein Sequence Data

Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger

MIT

Page 3: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Structural Motif Recognition

Problem: Given a structural motif (secondary, super-secondary, tertiary), predict its presence from sequence data alone.

GCN4 leucine zipper

Example: Coiled-coil prediction (Berger et al. 1995)

Page 4: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Long Distance Correlations

In beta structures, amino acids close in the folded 3D structure may be far away in the linear sequence

Cyclophilin A

Page 5: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

A processive fold composed of repeated super-secondary units.

Each rung consists of three beta-strands separated by turn regions.

No sequence repeat.

The Right-handed Parallel Beta-Helix

Pectate Lyase C (Yoder et al. 1993)

Page 6: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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

Page 7: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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 BPectin MethylesteraseP.69 PertactinP22 Tailspike

Page 8: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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

Page 9: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT
Page 10: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT
Page 11: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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).

BetaWrap Program

Page 12: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

BetaWrap ProgramHistogram of protein scores for:

• beta helices not in database (12 proteins)• non-beta helices in PDB (1346 proteins

)

Page 13: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Single Rung of a Beta Helix

Page 14: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT
Page 15: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

3D Pairwise Correlations

Aligned residues in adjacent beta-strands

exhibit strong correlations

Residues in the T2 turn have special

correlations (Asparagine ladder,

aliphatic stacking)

B3T2

B2

B1

Page 16: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

3D Pairwise Correlations

Stacking residues in adjacent beta-strands

exhibit strong correlations

Residues in the T2 turn have special

correlations (Asparagine ladder,

aliphatic stacking)

B3T2

B2

B1

Page 17: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT
Page 18: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Question: how can we find these correlations which are a variable distance apart in sequence?

Phage P22 Tailspike

Page 19: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Finding Candidate Wraps

• Assume we have the correct locations of a

single T2 turn (fixed B2 & B3).

• Generate the 5 best-scoring candidates for the next rung.

B2

B3 T2Candidate

Rung

Page 20: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Scoring Candidate Wraps (rung-to-rung)

Rung-to-rung alignment score incorporates:

• Beta sheet pairwise alignmentpreferences taken from amphipathic beta structures in PDB.

(w/o beta helices)

• Additional stacking bonuseson internal pairs.

• Distribution on turn lengths.

Page 21: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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

Page 22: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Key Features of Our Approach

• Structural model

• Statistical score

• Dynamic search

Page 23: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

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!

Page 24: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Some Predicted Beta Helices in Human Pathogens

Vibrio cholerae Helicobacter pyloriPlasmodium falciparum Chlamyidia trachomatis Chlamydophilia pneumoniae Listeria monocytogenes Trypanosoma brucei Borrelia burgdorferiLeishmania donovani Bordetella bronchiseptica Trypanosoma cruizi Bordetella parapertussisBacillus anthracisRickettsia ricketsii Rickettsia japonicaNeisseria meningitidisLegionaella pneumophilia

CholeraUlcersMalariaVenereal infectionRespiratory infectionListeriosisSleeping sicknessLyme diseaseLeishmaniasisRespiratory infectionSleeping sicknessWhooping coughAnthraxRocky Mtn. spotted feverOriental spotted feverMeningitisLegionnaire’s disease

Page 25: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

Predicted Beta Helices

False positives?

Also present in the top 200 proteins are members of the LRR and hexapeptide

repeat families.

Hexapeptide repeatLRR

Page 26: Predicting The Beta-Helix Fold From Protein Sequence Data Phil Bradley, Lenore Cowen, Matthew Menke, Jonathan King, Bonnie Berger MIT

•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.

Structural Features of Beta-Helices

A single rung of Pectate Lyase C