Structural Bioinformatics

1
Structural Bioinformatics

• Motivation
• Concepts
• Structure Prediction
• Summary

2
Motivation

• Holy Grail Mapping between sequence and
  structure. Structure F(Sequence). What is F?
• Why
• Structure dictates chemistry, thermodynamics and
  therefore function
• Not all structures can be (need be?) determined
  experimentally
• Cost
• Experimental limitations

3
Concepts Prediction spectrum
Decreasing reliance on known structures
Homology Modeling
ab initio
Quantum Mechanics
Threading
4
Concepts - Common Principles

• Constraints to reduce search space
• Consideration of many alternate conformations
• Protein backbone dihedral angles (Twists along
  axis of protein)
• Amino-acid geometry (Amino-acids can have more
  than one shape)
• Method for local optimization
• Scoring function to compare conformations

5
Evaluation of quality of prediction

• RMSD comparison with experimentally known
  structure
• Comparison with crystal structure quality
  criteria
• Ramachandran Plot
• Residue specific dihedral angle distribution
• CASP (Critical assessment of structure
  prediction) and CAFASP (..Fully Automated..)
  competitions

6
Methods

• Knowledge-based constraints of search space
• Homology Modeling
• Threading
• ab initio (Based on knowledge primitives not
  true ab initio)
• Approaches to refinement
• Quantum mechanics (ab initio)
• Based on quantum mechanical model of elementary
  particles
• Unscalable
• Molecular mechanics
• Uses parametric Force Fields (Newtons laws,
  Hookes law, )
• Typically used for local or constrained global
  optimization
• Molecular Dynamics or Monte Carlo-based

7
Homology modeling

• Homology
• Based on sequence-sequence similarity ( gt 25,
  the higher, the better)
• Steps
• Pair-wise local sequence similarity to identify
  related structures (possible templates)
• Refine alignment by global pair-wise sequence
  similarity and msa
• Overlay sequence backbone (N-C-C) on template
• Model loops based on
• Statistical knowledge from databases of known
  structures
• Molecular mechanics
• Model side-chains (approach similar to that of
  loops)
• Molecular mechanical unconstrained local
  optimization
• Pray for a good solution!

8
Threading

• Based on sequence-structure similarity
• Concept
• Residues in core adopt fewer conformations than
  surface
• Approach
• Thread sequence through all known structures
• Score match with core of each structure based on
• Environmental scoring matrices and/or
• Amino acid neighborhood matrices (a la Dot
  matrix)
• Refine structure using molecular mechanics based
  on best template(s)

9
Rosetta (ab initio) Approach

• Pioneered by David Bakers group in the late
  1990s
• Remarkable success in CASP and CAFASP experiments
• Recently made publicly available on an automated
  server by Christopher Bystroffs group
• Pot pourri of many different approaches
• Key components
• Divide and conquer strategy with respect to
  length of sequence to be modeled
• Use of knowledge based energy function

10
Divide and conquer

• Mimics natural process of protein folding
• Compromise between extremes of
• Looking for homologous sequences with known
  structure
• Modeling a priori (one amino acid at a time)
• Use library of 3D structures of fragments of
  length 3 and 9 derived from the crystal structure
  database (a priori estimates 8K and 1012).
• Break up query sequence into a set of 3mers and
  9mers, to find matches with above library using
  a sequence profile approach

11
Divide and conquer

• Once matches found, reduces to combinatorial
  problem of selecting best set of fragments with
  most energetically favorable structure
• In practice, Monte Carlo based search of possible
  combinations is carried out.

12
Knowledge based energy function

• Fundamentally,
• ?G ?H - T ?S
• Free energy is the enthalpy less an entropic term
  that is proportional to temperature
• Entropy is proportional to the natural log of the
  number of conformations/possible states
• S K ln W

13
Knowledge based energy function

• Hence makes sense to use existing distribution of
  structures to derive energy function
• Energy function is based on taking statistical
  distribution of 3D shapes in database of known
  structures as the underlying probability
  distribution
• For a given structure, deviations from
  probability distribution are subject to
  proportional energetic penalties

14
Rosetta Steps used in CASP4

• If possible, use PSI-BLAST to find similar
  sequences
• If found, use the multiple sequence alignment to
  break down sequence into domains to be modeled
  independently
• For domains with similarity to known structures,
  use Homology based approach
• For remaining domains, carry out Rosetta

15
Rosetta - Steps

• For domains with similarity to other sequences,
  apply following steps to the homologs as well
  (consensus modeling)
• Generate fragment library for each query
• Collect 3mer and 9mer sub-structures from the PDB
  with similarity to 3mer and 9mer subsequences
• Use Monte Carlo approach for backbone fragment
  substitution into query
• Pick a fragment at random from library (40,000
  fragment substitutions for each structure)
• Repeat A several times
• Between 10K and 100K conformations (decoys)
  generated for each target

16
Rosetta - Steps

• Filter set of conformations to remove unlikely
  structures
• Remove structures with minimal long range
  interactions (low contact order)
• Remove structures with unrealistic strands
• Add side chains as statistically predicted by the
  backbone conformation
• Cluster set of conformations (including, when
  available, the generated structures of
  homologues)
• Representative structures from the top 5
  most-populous clusters are candidate structures

17
Summary

• Methods like Rosetta represents a breakthrough in
  the ab initio prediction of protein 3D structure
  and are very useful in cases where homology
  cannot be observed
• For CASP4, at least one subsequence longer than
  50 residues could be predicted correctly (lt 6.5
  rmsd) in 17 of 21 cases
• Combination of various approaches works best

18
Summary

• However, both completeness and accuracy of
  prediction leave ample room for improvement
• RMS error frequently too high to be useful
• Even in homology modeling, template per se is
  often better match!
• Often, only subsequences are accurately modeled,
  and not the whole structure
• The Nobel Prize is still up for grabs!