Being a binding site: Characterizing ResidueComposition of Binding Sites on Proteins

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Being a binding site Characterizing
Residue-Composition of Binding Sites on Proteins
Vince Grolmusz

• joint work with
• Zoltán Szabadka and Gábor Iván,
• Protein Information Technology Group
• Department of Computer Science, Eötvös University
• Budapest, Hungary

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The Protein Data Bank

• It is a collection of the experimentally
  determined 3D structures of biopolymers and their
  complexes, today it contains more than 45 ,000
  entries
• Experimental methods include
• X-Ray Diffraction
• Nuclear magnetic resonance (NMR) spectroscopy
• PDB file formats
• pdb format
• mmCIF format
• XML format

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The graph model of molecules

• The molecule is modelled with a graph where the
  vertices are the atoms and the edges are the
  covalent bonds
• Each atom has an atomic number and a formal
  charge
• Each bond has an order that can be
• 0 for coordinated covalent bonds
• 1,2 or 3 for single, double and triple bonds
  respectively
• Aromatic ring systems are modelled with
  alternating single and double bonds
• A steric model is a graph model plus 3D
  coordinates for the atoms

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Main problems

• Given a pdb file, find the steric model of each
  molecule in it
• Find the molecules which have unrealistic steric
  models
• Make a searchable database of different
  protein-ligand complexes which fulfil certain
  additional quality requirements

Our solution The RS-PDB Database (RS stands
for Rich-Structure)
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Difficulties and solutions

• The two main difficulties with these problems
• the basic units of a pdb entry are the residues
  and HET groups, and not the molecules
• there are atoms, whose coordinates could not be
  determined, and these are simply missing from the
  files
• Therefore the problem can not be solved for every
  entries
• We developed a method to automatically process
  the PDB mmCIF files and created a database with
  an approximate solution and marked the places,
  where there are errors or ambiguities

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HET Group Dictionary

• The basic units of a pdb entry are the residues
  and HET groups, these will be called monomers
• A monomer can be a molecule or a molecule
  fragment
• Each monomer has a unique code ASN, C, MG, NAD,
  
• The covalent structure of these monomers are in a
  separate part of the PDB, the PDB Chemical
  Component Dictionary', formerly called the HET
  Group Dictionary (HGD)
• We converted the structure descriptions of these
  monomers to the graph model and put them in our
  HGD database

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Processing of an mmCIF file (1) Polymers

• We read all the so called entities from the file,
  each of them containing one ore more monomers
• Each entity has a type, that can be polymer,
  non-polymer or water, and each polymer entity has
  a polymer type
• Next we build the polymers from the monomers,
  one-by-one, for example in the case of proteins

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Constructing Polypeptide chains the peptide bond
When a new amino acid (i.e., a monomer) is added
we remove the atoms OXT and HXT from the end of
the chain, and the atom HN2 from the new monomer,
and add a covalent bond between the atoms C and
N. In the case of amino acid PRO, we remove
both HT1 and HT2 if, in the case of a
non-standard amino acid (i.e., protein monomer),
the above mentioned atoms are not present, we
refuse to make chain.
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• After the polymers are built, we define three
  types of polymer molecules
• Polypeptide chains (P) gt10 monomers long
• DNA/RNA chains (N) gt5 monomers long
• Polysaccharides (S) gt5 monomers long
• The sequence of these polymers will give the
  graph model of the molecules

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Processing of an mmCIF file (2)Ligands and their
bond graph

• Initially all monomers not belonging to a polymer
  are distinct ligands, their graph model taken
  from the HGD
• We read all the available atomic coordinates from
  the mmCIF file to create the (partial) steric
  models
• We find all pairs of atoms with distance less
  then 6 Å, building a kd-tree for this purpose
• If two atoms from different molecules are within
  covalent distance, we try to combine their graphs
  
• If this fails, or the atoms are too close, we
  record this in a separate database table
  containing bond errors
• Next, crystallization artefacts and junk
  ligands are removed (Similarly as in the PDBBind
  database).

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Database of protein-ligand complexes and binding
sites

• A protein-ligand complex consists of a ligand and
  one or more protein chains that have atoms in van
  der Waals distance from the ligand these atoms
  are painted red in the figure

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Getting rid of redundancies

• PDB is strongly biased in the direction of
  popular or important proteins some chains
  (e.g., bovine trypsin) are present in more than
  100 PDB entries.
• When mapping binding sites in the PDB,
  redundancies must be dealt with
• If to the chain A ligand X is bound to the same
  place in different PDB ids -gt counted once
• If to the chain A ligand X is bound at distinct
  places -gt counted twice or more
• Result 25,000 binding sites -gt 19,000 B.S.

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Residues in binding sites

• Next, those residues are collected from
• protein chains, that are close to the ligands
• We go through the ligand atoms one-
• by-one and find those protein atoms
• which were closer to them than 1.05
• times the sum of the Van der Waals radii
• of the two atoms scanned
• We do not have covalently bound ligands they
  were already filtered out .
• Next we identify the residues containing these
  atoms for every
• binding site a subset of the 20 amino acids were
  created.
• If the same residue appeared more than once, we
  inserted
• it only once into the residue-set we are
  interested in the plain
• appearance of the residue at the binding site.

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Binding site residue frequencies
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Association rules in residue-sets

• We are interested in implication-like rules such
  as
• (ALA,LEU) (ILE,VAL)
• that is, if a binding site contains amino
  acids leucine and alanine, it will likely''
  contain also valine and isoleucine.
• Main attributes of the rules are
• support Prob(ALA,LEU,ILE,VAL)
• confidence Prob((ILE,VAL) (ALA,LEU))
• lift Prob(ALA,LEU,ILE,VAL)/(Prob(ILE,VAL)Prob(ALA
  ,LEU))

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What is interesting?

• Association rules X Y, where Y is a very
  frequently appearing residue-subset, are not
  interesting generally.
• On the other hand, if Y is infrequent, then the
  support and the confidence generally will not
  reach the thresholds to be included in our
  results.
• For example, YGLY appears very frequently, while
  YCYS or YTRP appears rarely.
• Association rules of unusually high and unusually
  low lifts and rules of form X Y with high
  confidence and not-too-high support for Y are of
  particular interest. Our next figures here
  visualize such remarkable data.

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Our first figure

• was created by deleting all X GLY
  association rules for clarity, and including only
  those rules which satisfy that
• their support is at least 7.15 and
• their confidence is at least 0.5 and
• at least one of the following conditions hold
• a) their confidence is at least 0.8 or
• b) their lift is at least 1.8 or
• c) their lift is at most 0.97 or
• d) their support is at least 24.

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Low-lift area
High-confidence area
High-support area
High-lift area
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Figure 2 contains rules, where

• all X GLY association rules are deleted for
  clarity, and
• the support is at least 7.15 and
• the confidence is at least 0.55 and
• the lift is at least 1.7.

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Here, ALA, the sixth most frequent residue, is
present in almost all bases and THR (threonine),
the tenth most frequent residue appears in the
center all bases have 3 or 4 elements.
All large fan-in stars contains GLY
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Conclusions

• We believe that by the analysis of the
  residue-composition of the binding sites in a
  really large and reliable data set, one can
  identify pretty interesting data patterns,
  applicable in inhibitor and drug design
• We think that this work is just one of the first
  steps in that direction.

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Thank you very much!