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					                    Homology modeling




                                    Dinesh Gupta
                                    ICGEB, New Delhi



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       Protein structure prediction
• Methods:
     – Homology (comparative) modelling
     – Threading
     – Ab-initio




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      Protein Homology modeling
• Homology modeling is an extrapolation of
  protein structure for a target sequence
  using the known 3D structure of similar
  sequence as a template.
• Basis: proteins with similar sequences are
  likely to assume same folding
• Certain proteins with as low as 25%
  similarity have been observed to assume
  same 3D structure
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              The accuracy of modeling is proportional
              to the similarity in primary sequences
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                    Steps…
• Given:
     – A query sequence Q
     – A database of known protein structures
• Find protein P such that P has high
  sequence similarity to Q
• Return P’s structure as an approximation
  to Q’s structure
• Energy minimization
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    Sofware for homology molecular
               modelling
• Freeware: available for all OS
     – Downloadable
          • Modeller (Sali, 1998)
          • DeepView (SwissPDB viewer)
          • WHATIF (Krieger et al. 2003)
     – Web based:
          • SWISS MODEL server (www.expasy.org/swissmod/SWISS-
            MODEL.html)
          • CPH model server
            (http://www.cbs.dtu.dk/services/CPHmodels)
          • SDSC1 server (http://cl.sdsc.edu/hm.html)


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       Protein structure prediction
• Methods:
     – Homology (comparative) modelling
     – Threading
     – Ab-initio




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                    Threading
• Structure prediction that picks up where
  homology modelling leaves off.
• Recognize folds in proteins having no
  similarity to known proteins structures
• Very approximate models
• Check by forcing a sequence of structure
  into known folds checking the packing of
  aa residues, including sides chains, in
  each fold.
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                    2 kinds of threading
• Three dimensional threading
     – Distance Based Method (DBM)
• Two dimensional threading
     – Prediction Based Methods (PBM)




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                    Threading software
• EVA: http://cubic.bioc.columbia.edu/eva/
• SAMt99:
  http://www.cse.ucsc.edu/research/compbi
  o/HMM-apps/T99-model-library-
  search.html
• 3DPSSM:
  http://www.sbg.bio.ic.ac.uk/3dpssm
• FUGUE: http://tardis.nibio.go.jp/fugue/
• Metaservers:
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5/19/2012 9:31 PM
       Protein structure prediction
• Methods:
     – Homology (comparative) modelling
     – Threading
     – Ab-initio




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      Ab initio structure prediction
• Still experimental
• ROSETTA (David Baker)




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     Energy minimization (Molecular
            Mechanics, MM)
• Energy minimization is an important part
  of both empirical and predicted structures
• MM could be used to calculate large scale
  conformational changes over long periods
  of time, but currently computationally
  infeasible.




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                How does MM work?
• Three aspects:
     – Functions that describe the forces acting on the
       atoms
     – Numerical integration methods, to calculate the
       motion of the atoms due to the forces acting on them
     – Long time propagation of the equations of motion
• Computational demands are intense
     – Accuracy (small errors propagate!)
     – Stability
     – Lots of techniques for approximation (e.g. rigid
       bodies) and handling artifacts (resonance).

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                    The Force Fields
• How do atoms stretch, vibrate, rotate, etc.?
• Must represent the constraints on atomic motion
  (e.g. van der Waals, electrostatic, bonds, etc.)
• Must also represent solvation effects etc.
• Quantum solutions exist, but are too complex to
  calculate for such large systems
• Empirical (approximate) energy functions must
  be used. No single best function exists.


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                    Real energetics
• Steric (conformational) energy. Additive
  combination of
     – Bonded: stretching, bending, stretching and bending
     – Non-bonded: Van der Waals, electrostatic and
       “torsional”
• Minimum energy conformation minimizes these
  energies
• Rosetta energy function is an empirical attempt
  to capture most of this energy function without
  having to calculate it fully.
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                    Bond length
• Spring-like term for energy based on
  distance         Estr = ½ks,ij(rij -ro)2
  where ks,ij is the stretching force constant
  for the bond between i and j, rij is the
  length, and ro is the equilibrium bond
  length



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                    Bond bend
• Same basic idea for bending
                  Ebend = ½kb,ij(ij –o)2

    where where kb,ij is the bending force constant,
    ij is the instantaneous
    bond angle, and o is
     the equilibrium
     bond angle



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                    Stretch-bend
• When a bond is bent, the two associated bond
  lengths increase, with interaction term:
             Estr-bend =½ksb,ijk(rij-ro)(ik - o)

    where ksb,ijk is the stretch-bend force constant for
    the bond between
    atoms i and j with the
    bend between atoms
    i, j, and k.

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                    Van der Waals
• A non-bonded interaction capturing the
  preferred distance between atoms



    where A and B are constants depending
    on the atoms. For two
    hydrogen atoms,
    A=70.4kCÅ6 and
    B=6286kCÅ12
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                    Electrostatics
• If bonds in the molecule are polar, some atoms
  will have partial electrostatic charges, which
  attract if opposite and repel otherwise.
                       where Qi and Qj are the partial
                      atomic charges for i and j
  separated by distance rij ,
   is the dielectric constant
  of the solute, and k is a units
   constant (k=2086 kcal/mol)



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                    Torsional energy
• Torsion is the energy needed to rotate about
  bonds. Only relevant to single bonds, since
  others are too stiff to rotate at all
      Etor = ½ktor,1 (1 - cos ) + ½ktor,2 (1 - 2cos )
              + ½ktor,3 (1 - 3cos )
  where  is the dihedral angle
  around the bond, and ktor,1, ktor,2
  and ktor,3 are constants for one-,
  two- and three-fold barriers.

5/19/2012 9:31 PM                           energy of 3-fold torsional
                                            barrier in ethane
                    Energy minimization
• Given some energy function and initial
  conditions, we want to find the minimum
  energy conformation.
• Optimization problem, various methods:
     – Steepest descent
     – Conjugate gradient descent
     – Newton-Raphson
• Various programs: Charmm, Amber are
  two most widely used (and packaged)
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                      Time steps




Need time steps of roughly 1/10 the period of the smallest time
scale of interest, or about a femtosecond (10-15s). A million
computational steps per nanosecond of simulation...
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  Issues in Molecular Mechanics
• Solvation models: water & salt are very
  important to molecular behaviour. Must model
  as many water atoms as protein atoms.
• Initial conditions: velocity & position
• Equilibration: simulated heating and cooling
• Chaos: sensitivity to initial conditions, and
  statistical characterization of states
• Computational issues (e.g. parallelization)



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                    Molecular Dynamics

  • Molecules, especially proteins, are not static.
        – Dynamics can be important to function
  • Trajectories, not just minimum energy state.
        – MM ignores kinetic energy, does only potential energy
        – MD takes same force model, but calculates F=ma and
          calculates velocities of all atoms (as well as positions)




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                    Docking
• Computation to assess binding affinity
• Looks for conformational and electrostatic "fit"
  between proteins and other molecules e.g.
  inhibitors
• Optimization again: what position and
  orientation of the two molecules minimizes
  energy?
• Large computations, since there are many
  possible positions to check, and the energy for
  each position may involve many atoms
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                    Virtual Screening
• Docking small ligands to proteins is a way to find
  potential drugs. Industrially important
• A small region of interest (pharmacophore) can
  be identified, reducing computation
• Empirical scoring functions are not universal
• Various search methods:
     – Rigid provides score for whole ligand (accurate)
     – Flexible breaks ligands into pieces and docks them
       individually

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                    Docking example




         Benzamidine binding to beta-Trypsin 3ptb,
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           Macromolecular docking
• Docking of proteins to proteins or to DNA
• Important to understanding
  macromolecular recognition, genetic
  regulation, etc.
• Conceptually similar to small molecule
  docking, but practically much more difficult
    – Score function can't realistically compute
      energies
    – Use either shape complementarities alone or
      some kind of mean field approximation
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          Docking Resources
• AutoDock
  http://www.scripps.edu/pub/olson-
  web/doc/autodock/
• FlexX http://www.biosolveit.de/FlexX/ and
     commercially at http://www.tripos.com
• Dock
     http://www.cmpharm.ucsf.edu/kuntz/dock.html
• 3D-Dock http://www.bmm.icnet.uk/docking/
     which uses an unusual “Fourier correlation”
     method and is aimed at protein-protein
     interactions
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                    Lab Exercise-1

Install:
• MDL chime
• RasMol
• SwissPDBviewer
• Cn3D

Explore few protein/DNA structures

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                    Lab exercise-2
• Download sequence file for S. cerevisiae endoplasmic
  reticulum mannosidase

• Generate a homology model using SWISS-model server
  http://www.expasy.ch/swissmod/

• Download the template structure from www.rcsb.org

• Compare the model and template structures

• Repeat the exercise for other protein sequences of your
  choice

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