Protein Structure Prediction

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Protein Structure Prediction

• Xiaole Shirley Liu
• And
• Jun Liu
• STAT115

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Protein Structure Prediction Ram
Samudrala University of Washington
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Outline

• Motivations and introduction
• Protein 2nd structure prediction
• Protein 3D structure prediction
• CASP
• Homology modeling
• Fold recognition
• ab initio prediction
• Manual vs automation
• Structural genomics

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Protein Structure

• Sequence determines structure, structure
  determines function
• Most proteins can fold by itself very quickly
• Folded structure lowest energy state

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Protein Structure

• Main forces for considerations
• Steric complementarity
• Secondary structure preferences (satisfy H bonds)
• Hydrophobic/polar patterning
• Electrostatics

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Rationale for understanding protein structure and
function
Protein sequence -large numbers of sequences,
including whole genomes
?
Protein function - rational drug design and
treatment of disease - protein and genetic
engineering - build networks to model cellular
pathways - study organismal function and evolution
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Protein Databases

• SwissProt protein knowledgebase
• PDB Protein Data Bank, 3D structure

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View Protein Structure

• Free interactive viewers
• Download 3D coordinate file from PDB
• Quick and dirty
• VRML
• Rasmol
• Chime
• More powerful
• Swiss-PdbViewer

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Compare Protein Structures

• Structure is more conserved than sequence
• Why compare?
• Detect evolutionary relationships
• Identify recurring structural motifs
• Predicting function based on structure
• Assess predicted structures
• Protein structure comparison and classification
• Manual SCOP
• Automated DALI

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Compare protein structures

• Need ways to determine if two protein structures
  are related and to compare predicted
• models to experimental structures
• Commonly used measure is the root mean square
  deviation (RMSD) of the Cartesian
• atoms between two structures after optimal
  superposition (McLachlan, 1979)
•  
• Usually use Ca atoms
•  

• Other measures include contact maps and torsion
  angle RMSDs

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SCOP

• Compare protein
• structure, identify
• recurring structural
• motifs, predict function
• A. Murzin et al, 1995
• Manual classification
• A few folds are highly
• populated
• 5 folds contain 20 of all homologous
  superfamilies
• Some folds are multifunctional

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Determine Protein Structure

• X-ray crystallography (gold standard)
• Grow crystals, rate limiting, relies on the
  repeating structure of a crystalline lattice
• Collect a diffraction pattern
• Map to real space electron density, build and
  refine structural model
• Painstaking and time consuming

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Protein Structure Prediction

• Since AA sequence determines structure, can we
  predict protein structure from its AA sequence?
• predicting the three angles, unlimited DoF!
• Physical properties that determine fold
• Rigidity of the protein backbone
• Interactions among amino acids, including
• Electrostatic interactions
• van der Waals forces
• Volume constraints
• Hydrogen, disulfide bonds
• Interactions of amino acids with water

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Protein folding landscape Large
multi-dimensional space of changing conformations
free energy
folding reaction
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Protein primary structure
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2nd Structure Prediction

• ? helix, ? sheet, turn/loop

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2nd Structure Prediction

• Chou-Fasman 1974
• Base on 15 proteins (2473 AAs) of known
  conformation, determine P?, P? from
• ? 0.5-1.5
• Empirical rules for 2nd struct nucleation
• 4 H? or h? out of 6 AA, extends to both dir, P? gt
  1.03, P? gt P?, no ? breakers
• 3 H? or h? out of 5 AA, extends to both dir, P? gt
  1.05, P? gt P?, no ? breakers
• Have 50-60 accuracy

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P? and P?
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2nd Structure Prediction

• Garnier, Osguthorpe, Robson, 1978
• Assumption each AA influenced by flanking
  positions
• GOR scoring tables (problem limited dataset)
• Add scores, assign 2nd with highest score

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2nd Structure Prediction

• D. Eisenberg, 1986
• Plot hydrophobicity as function of sequence
  position, look for periodic repeats
• Period 3-4 AA, ? (3.6 aa / turn)
• Period 2 AA, ? sheet
• Best overall JPRED by Geoffrey Barton, use many
  different approaches, get consensus
• Overall accuracy 72.9

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3D Protein Structure Prediction

• CASP contest Critical Assessment of Structure
  Prediction
• Biannual meeting since 1994 at Asilomar, CA
• Experimentalists before CASP, submit sequence of
  to-be-solved structure to central repository
• Predictors download sequence and minimal
  information, make predictions in three categories
• Assessors automatic programs and experts to
  evaluate predictions quality

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CASP Category I

• Homology Modeling (sequences with high homology
  to sequences of known structure)
• Given a sequence with homology gt 25-30 with
  known structure in PDB, use known structure as
  starting point to create a model of the 3D
  structure of the sequence
• Takes advantage of knowledge of a closely related
  protein. Use sequence alignment techniques to
  establish correspondences between known
  template and unknown.

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CASP Category II

• Fold recognition (sequences with no sequence
  identity (lt 30) to sequences of known structure
  
• Given the sequence, and a set of folds observed
  in PDB, see if any of the sequences could adopt
  one of the known folds
• Takes advantage of knowledge of existing
  structures, and principles by which they are
  stabilized (favorable interactions)

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CASP Category III

• Ab initio prediction (no known homology with any
  sequence of known structure)
• Given only the sequence, predict the 3D structure
  from first principles, based on energetic or
  statistical principles
• Secondary structure prediction and multiple
  alignment techniques used to predict features of
  these molecules. Then, some method necessary for
  assembling 3D structure.

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Structure Prediction Evaluation

• Hydrophobic core similar?
• 2nd struct identified?
• Energy minimized? H-bond contacts?
• Compare with solved crystal structure gold
  standard

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Comparative modelling of protein structure
refine
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Homology Modeling Results

• When sequence homology is gt 70, high resolution
  models are possible (lt 3 Å RMSD)
• MODELLER (Sali et al)
• Find homologous proteins with known structure and
  align
• Collect distance distributions between atoms in
  known protein structures
• Use these distributions to compute positions for
  equivalent atoms in alignment
• Refine using energetics

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Homology Modeling Results

• Many places can go wrong
• Bad template - it doesnt have the same structure
  as the target after all
• Bad alignment (a very common problem)
• Good alignment to good template still gives wrong
  local structure
• Bad loop construction
• Bad side chain positioning

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Homology Modeling Results

• Use of sensitive multiple alignment (e.g.
  PSI-BLAST) techniques helped get best alignments
• Sophisticated energy minimization techniques do
  not dramatically improve upon initial guess

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Fold Recognition Results

• Also called protein threading
• Given new sequence and library of known folds,
  find best alignment of sequence to each fold,
  returned the most favorable one

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Fold Recognition with Dynamic Programming

• Environmental class for each AA based on known
  folds (buried status, polarity, 2nd struct)

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Protein Folding with Dynamic Programming

• D. Eisenburg 1994
• Align sequence to each fold (a string of
  environmental classes)
• Advantages fast and works pretty well
• Disadvantages do not consider AA contacts

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Fold Recognition Results

• Each predictor can submit N top hits
• Every predictor does well on something
• Common folds (more examples) are easier to
  recognize
• Fold recognition was the surprise performer at
  CASP1. Incremental progress at CASP2, CASP3,
  CASP4

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Fold Recognition Results

• Alignment (seq to fold) is a big problem

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ab initio

• Predict interresidue contacts and then compute
  structure (mild success)
• Simplified energy term reduced search space
  (phi/psi or lattice) (moderate success)
• Creative ways to memorize sequence ?? structure
  correlations in short segments from the PDB, and
  use these to model new structures ROSETTA

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Ab initio prediction of protein structure
sample conformational space such that native-like
conformations are found
hard to design functions that are not fooled by
non-native conformations (decoys)
astronomically large number of conformations 5
states/100 residues 5100 1070
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Sampling conformational space continuous
approaches

• Most work in the field
• Molecular dynamics
• Continuous energy minimization (follow a valley)
• Monte Carlo simulation
• Genetic Algorithms

• Like real polypeptide folding process
• Cannot be sure if native-like conformations are
  sampled

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Molecular dynamics

• Force -dU/dx (slope of potential U)
  acceleration, force m a(t)
• All atoms are moving so forces between atoms are
  complicated functions of time
• Analytical solution for x(t) and v(t) is
  impossible numerical solution is trivial
• Atoms move for very short times of 10-15 seconds
  or 0.001 picoseconds (ps)
• x(tDt) x(t) v(t)Dt 4a(t) a(t-Dt)
  Dt2/6
• v(tDt) v(t) 2a(tDt)5a(t)-a(t-Dt) Dt/6
• Ukinetic ½ S mivi(t)2 ½ n KBT
• Total energy (Upotential Ukinetic) must not
  change with time

acceleration
old velocity
old position
new position
new velocity
n is number of coordinates (not atoms)
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Energy minimization

• For a given protein, the energy depends on
  thousands of x,y,z Cartesian atomic coordinates
  reaching a deep minimum is not trivial
• Furthermore, we want to minimize the free energy,
  not just the potential energy.

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Monte Carlo Simulation

• Propose moves in torsion or Cartesian
  conformation space
• Evaluate energy after every move, compute ?E
• Accept the new conformation based on
• If run infinite time, the simulated conformation
  follows the Boltzmann distribution
• Many variations, including simulated annealing
  and other heuristic approaches.

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Scoring/energy functions

• Need a way to select native-like conformations
  from non-native ones
• Physics-based functions electrostatics, van der
  Waals, solvation, bond/angle terms.
• Knowledge-based scoring functions
• Derive information about atomic properties from a
  database of experimentally determined
  conformations
• Common parameters include pairwise atomic
  distances and amino acid burial/exposure.

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Rosetta

• D. Baker, U. Wash
• Break sequence into short segments (7-9 AA)
• Sample 3D from library of known segment
  structures, parallel computation
• Use simulated annealing (metropolis-type
  algorithm) for global optimization
• Propose a change, if better energy, take
  otherwise take at smaller probability
• Create 1000 structures, cluster and choose one
  representative from each cluster to submit

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Manual Improvements and Automation

• Very often manual examination could improve
  prediction
• Catch errors
• Need domain knowledge
• A. Murzins success at CASP2
• CAFASP Critical Assessment of Fully Automated
  Structure Prediction
• Murzin Cant play!!
• MetaServers combine different methods to get
  consensus

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CAFASP Evaluation
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Structural Genomics

• With more and more solved structures and novel
  folds, computational protein structure prediction
  is going to improve
• Structural genomics
• Worldwide initiative to high throughput determine
  many protein structures
• Especially, solve structures that have no homology

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Summary

• Protein structures 1st, 2nd, 3rd, 4th
• Different DB SwissProt, PDB and SCOP
• Determine structure X-ray crystallography
• Protein structure prediction
• 2nd structure prediction
• Homology modeling
• Fold recognition
• Ab initio
• Evaluation energy, RMSD, etc
• CASP and CAFASP contest
• Manual improvement and combination of
  computational approaches work better
• Structural Genomics, still very difficult problem

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Acknowledgement

• Amy Keating
• Michael Yaffe
• Mark Craven
• Russ Altman