BCB 444/544

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BCB 444/544

• Lab 7
• Protein Structure Prediction
• Oct 11, 2007

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Chp 14 - Secondary Structure Prediction

• SECTION V STRUCTURAL BIOINFORMATICS
• Xiong Chp 14
• Protein Secondary Structure Prediction
• Secondary Structure Prediction for Globular
  Proteins
• Secondary Structure Prediction for Transmembrane
  Proteins
• Coiled-Coil Prediction

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Secondary Structure Prediction

• Has become highly accurate in recent years (gt85)
• Usually 3 (or 4) state predictions
• H ?-helix
• E ?-strand
• C coil (or loop)
• (T turn)

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Secondary Structure Prediction Methods

• 1st Generation methods
• Ab initio - used relatively small dataset of
  structures available
• Chou-Fasman - based on amino acid propensities
  (3-state)
• GOR - also propensity-based (4-state)
• 2nd Generation methods
• based on much larger datasets of structures now
  available
• GOR II, III, IV, SOPM, GOR V, FDM
• 3rd Generation methods
• Homology-based Neural network based
• PHD, PSIPRED, SSPRO, PROF, HMMSTR, CDM
• Meta-Servers
• combine several different methods
• Consensus Ensemble based
• JPRED, PredictProtein, Proteus

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Secondary Structure Prediction Servers

• Prediction Evaluation?
• Q3 score - of residues correctly predicted
  (3-state)
• in cross-validation experiments
• Best results? Meta-servers
• http//expasy.org/tools/ (scroll for 2'
  structure prediction)
• http//www.russell.embl-heidelberg.de/gtsp/secstru
  cpred.html
• JPred www.compbio.dundee.ac.uk/www-jpred
• PredictProtein http//www.predictprotein.org/
  Rost, Columbia
• Best "individual" programs? ??
• CDM http//gor.bb.iastate.edu/cdm/
  SenJernigan, ISU
• FDM (not available separately as server)
  ChengJernigan, ISU
• GOR V http//gor.bb.iastate.edu/
  KloczkowskyJernigan, ISU

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Consensus Data Mining (CDM)

• Developed by Jernigan Group at ISU
• Basic premise combination of 2 complementary
  methods can enhance performance by harnessing
  distinct advantages of both methods combines
  FDM GOR V
• FDM - Fragment Data Mining - exploits
  availability of sequence-similar fragments in the
  PDB, which can lead to highly accurate prediction
  - much better than GOR V - for such fragments,
  but such fragments are not available for many
  cases
• GOR V - Garnier, Osguthorpe, Robson V - predicts
  secondary structure of less similar fragments
  with good performance these are protein
  fragments for which FDM method cannot find
  suitable structures
• For references additional details
  http//gor.bb.iastate.edu/cdm/

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Secondary Structure Prediction for Different
Types of Proteins/Domains

• For Complete proteins
• Globular Proteins - use methods previously
  described
• Transmembrane (TMM) Proteins - use special
  methods
• (next slides)
• For Structural Domains many under development
• Coiled-Coil Domains (Protein interaction
  domains)
• Zinc Finger Domains (DNA binding domains),
• others

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SS Prediction for Transmembrane Proteins

• Transmembrane (TM) Proteins
• Only a few in the PDB - but 30 of cellular
  proteins are membrane-associated !
• Hard to determine experimentally, so prediction
  important
• TM domains are relatively 'easy' to predict!
• Why? constraints due to hydrophobic environment
• 2 main classes of TM proteins
• ??- helical
• ?- barrel

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SS Prediction for TM ?-Helices

• ??-Helical TM domains
• Helices are 17-25 amino acids long (span the
  membrane)
• Predominantly hydrophobic residues
• Helices oriented perpendicular to membrane
• Orientation can be predicted using "positive
  inside" rule
• Residues at cytosolic (inside or cytoplasmic)
  side of TM helix, near hydrophobic anchor are
  more positively charged than those on lumenal
  (inside an organelle in eukaryotes) or
  periplasmic side (space between inner outer
  membrane in gram-negative bacteria)
• Alternating polar hydrophobic residues provide
  clues to interactions among helices within
  membrane
• Servers?
• TMHMM or HMMTOP - 70 accuracy - confused by
  hydrophobic signal peptides (short hydrophobic
  sequences that target proteins to the
  endoplasmic reticulum, ER)
• Phobius - 94 accuracy - uses distinct HMM
  models for TM helices
• signal peptide sequences

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SS Prediction for TM ?-Barrels ?

• ?-Barrel TM domains ?
• ?-strands are amphipathic (partly hydrophobic,
  partly hydrophilic)
• Strands are 10 - 22 amino acids long
• Every 2nd residue is hydrophobic, facing lipid
  bilayer
• Other residues are hydrophilic, facing "pore" or
  opening
• Servers? Harder problem, fewer servers
• TBBPred - uses NN or SVM (more on these ML
  methods later)
• Accuracy ?

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Chp 15 - Tertiary Structure Prediction

• SECTION V STRUCTURAL BIOINFORMATICS
• Xiong Chp 15
• Protein Tertiary Structure Prediction
• Methods
• Homology Modeling
• Threading and Fold Recognition
• Ab Initio Protein Structural Prediction
• CASP

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Protein Tertiary Structure Prediction

• 3 Major Methods
• Homology Modeling (easiest!)
• Threading and Fold Recognition (harder)
• Ab Initio Protein Structural Prediction (really
  hard)

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Comparative Modeling?

• Comparative modeling - term is sometimes used
  interchangeably with homology modeling, but also
  sometimes used to mean both homology modeling
  and/or threading/fold recognition

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Ab Initio Prediction

• Develop energy function
• bond energy
• bond angle energy
• dihedral angle energy
• van der Waals energy
• electrostatic energy
• Calculate structure by minimizing energy function
  
• (usually Molecular Dynamics or Monte Carlo
  methods)
• Ab initio prediction - impractical for most real
  (long) proteins
• Computationally? very expensive
• Accuracy? Usually poor for all except short
  peptides
• (but much improvement recently!)

Provides both folding pathway folded structure
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Comparative Modeling

• Two types
• 1) Homology modeling
• 2) Threading (fold recognition)
• Both rely on availability of experimentally
  determined structures that are "homologous" or
  at least structurally very similar to target

Provide folded structure only
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Homology Modeling

• Identify homologous protein sequences (?-BLAST)
• Among available structures (in PDB), choose one
  with closest sequence to target as template
• (can combine steps 1 2 by using PDB-BLAST)
• Build model by placing target sequence residues
  in corresponding positions of homologous
  structure refine by "tweaking" modeled
  structure (energy minimization)
• Homology modeling - works "well"
• Computationally? "relatively" inexpensive
• Accuracy? higher sequence identity ? better
  model
• Requires 30 sequence identity with sequence for
  which structure is known

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Threading - Fold Recognition

• Identify best fit between target sequence
  template structure

• Develop energy function
• Develop template library
• Align target sequence with each template score
• Identify top scoring template (1D to 3D
  alignment)
• Refine structure as in homology modeling
• Threading - works "sometimes"
• Computationally? Can be expensive or cheap,
  depends on energy function whether "all atom"
  or "backbone only" threading
• Accuracy? in theory, should not depend on
  sequence identity (should depend on quality of
  template library "luck")
• Usually, higher sequence identity to protein of
  known structure ? better model

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Today's Lab

• Homology Modeling - using SWISS-MODEL
• http//swissmodel.expasy.org//SWISS-MODEL.html
• Threading - using 3-D JURY (BioinfoBank, a
  METAserver)
• http//meta.bioinfo.pl/submit_wizard.pl
• Take a look at CASP contest
• http//predictioncenter.gc.ucdavis.edu/
• CASP7 contest in 2006
• http//www.predictioncenter.org/casp7/Casp7.html