Title: Being a binding site: Characterizing ResidueComposition of Binding Sites on Proteins
1Being 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
2The 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
3The 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
4Main 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)
5Difficulties 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
6HET 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
7Processing 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
8Constructing 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.
9- 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
10Processing 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).
11Database 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
12Getting 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.
13Residues 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.
14Binding site residue frequencies
15Association 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))
16What 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.
17Our 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.
18Low-lift area
High-confidence area
High-support area
High-lift area
19Figure 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.
20Here, 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
21Conclusions
- 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.
22Thank you very much!