Transcript Alignments -> Database Searching

Structure and Function
Suggested readings:
Lee D, Redfern O, Orengo C.
Predicting protein function from sequence and structure.
Nat Rev Mol Cell Biol. 2007 8(12):995-1005.
Chapter 2 of the book Understanding bioinformatics Marketa Zvelebil Jeremy O. Baum
Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S,
Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Mazumder R, O'Donovan C,
Redaschi N, Suzek B.
The Universal Protein Resource (UniProt): an expanding universe of protein
information. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D187-91.
Kinoshita K, Nakamura H.
Protein informatics towards function identification.
Curr Opin Struct Biol. 2003 Jun;13(3):396-400. Review.
The information on biological data is not stored in books…..
BUT
in Electronic Databases publicly available (generally!) and accessible
by internet
Most of the work of a bioinformatician is to learn how to retrieve those data,
how to analyse them, and how to produce novel data necessary to the
understanding of the complex biological world
“Empirical art”
In 1965 Gordon Moore, co-founder of
Intel noticed that:
Every chip had a capacity double to the
predecessor and that every 18-24
months a new generation of chip was
born.
In the seventies Dickerson, a professor of Physical Chemistry
noticed that the number of solved X-ray structures of proteins
had increased from 1
in 1961 to 23 in 1977
After the completion of the human genome project we have available
millions of sequences which represent a biological ‘knowledge’ that
we have to understand…
Growth of pdb structures
Structures are collected in the PDB databank:http://www.rcsb.org/pdb/home/home.do
45.000
So our ‘knowledge’ is very limited
Protein Structure Prediction
• In theory, a protein structure can be solved
computationally
• A protein folds into a 3D structure to minimizes
its free potential energy
• The problem can be formulated as a search
problem
for minimum energy
•
•
the search space is enormous
the number of local minima increases exponentially
Computationally it is an exceedingly difficult problem
protein prediction .vs. protein determination
X-Ray
inferred
data
Comparative Modeling
Experimental
data
NMR
Threading
Ab-initio
8
QuickTime™ and a
decompressor
are needed to see this picture.
….from a string of pearl to a folded structure….
Why is it useful to know the structure of a protein,
not only its sequence?
•
The biochemical function (activity) of a protein is defined by its interactions with other molecules.
•
The biological function is in large part a consequence of these interactions.
•
The 3D structure is more informative than sequence because interactions are determined by
residues that are close in space but are frequently distant in sequence.
In addition, since evolution tends to conserve
function and function depends more directly on
structure than on sequence, structure is more
conserved in evolution than sequence.
The net result is that patterns in space are
frequently more recognizable than patterns
in sequence.
11
There is a relationship between sequence similarity and
structural similarity
• Seq. Id. > 50%:
core region ~90% of the
structure, r.m.s.d. of the
main chain around 1.0 Å
• Seq. Id. < 20%:
core region ~50% of the
structure, r.m.s.d. of main
chain around 1.8 Å
[Chothia & Lesk, EMBO J.
(1986) 5: 823-826]
A Ramachandran plot (also known as a Ramachandran map or a Ramachandran diagram), is a way to visualize
dihedral angles φ against ψ of amino acid residues in protein structure. It shows the possible conformations of φ
and ψ angles for a polypeptide.
Mathematically, the Ramachandran plot is the visualization of a function (torus).
Hence, the conventional Ramachandran plot is a projection of the torus on the plane, resulting in a distorted view
and the presence of discontinuities.
One would expect that larger side chains would result in more restrictions and consequently a smaller allowable
region in the Ramachandran plot. In practice this does not appear to be the case; only the methylene group at the
β position has an influence.
Glycine has a hydrogen atom, with a smaller van der Waals radius, instead of a methyl group at the β position.
Hence it is least restricted and this is apparent in the Ramachandran plot for Glycine for which the allowable area
is considerably larger.
In contrast, the Ramachandran plot for proline shows only a very limited number of possible
combinations of ψ and φ.
QuickTime™ and a
decompressor
are needed to see this picture.
A Ramachandran plot generated from the protein PCNA, a human DNA
clamp protein that is composed of both beta sheets and alpha helices
(PDB ID 1AXC). Points that lie on the axes indicate N- and C-terminal
residues for each subunit.
The green regions show possible angle formations that include Glycine,
while the blue areas are for formations that don't include Glycine.
From Wikipedia
http://swift.cmbi.ru.nl/gv/dssp/
The DSSP code
http://bioweb.pasteur.fr/seqanal/interfaces/dssp-simple.html
H = alpha helix
B = residue in isolated beta-bridge
E = extended strand, participates in beta ladder
G = 3-helix (3/10 helix)
I = 5 helix (pi helix)
T = hydrogen bonded turn
S = bend
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded
and geometrical features.
Biopolymers. 1983 Dec;22(12):2577-637.
The secondary structure assignment with DSSP over a database of structures
can be used as ‘standard of truth’ for secondary structure prediction methods.
the alpha helix (α-helix) in which every
backbone N-H group donates a hydrogen
bond to the backbone C=O group of the
amino acid four residues earlier
(
hydrogen bonding).
The amino acids in a 310-helix are arranged
in a right-handed helical structure.
The N-H group of an amino acid forms a
hydrogen bond with the C = O group of the
amino acid three residues earlier;
this repeated i + 3 → i hydrogen bonding
defines a 310-helix.
Homology: two proteins are homologous if
they have a common ancestor.
This is a binary property (once defined is
either there or not).
It is a useful information:
when a known gene G is homologous to an
unknown gene X we will gain information on X
from G through transitivity.
Orthology
Homologous sequences are orthologous if they were separated
by a speciation event: when a species diverges into two separate
species, the divergent copies of a single gene in the resulting species
are said to be orthologous.
Orthologs, or orthologous genes, are genes in different species, that
are similar to each other because they originated from a common
ancestor.
Paralogy
Homologous sequences are paralogous if they were
separated by a gene duplication event: if a gene in an
organism is duplicated to occupy two different positions in
the same genome, then the two copies are paralogous.
A set of sequences that are paralogous are called paralogs of
each other. Paralogs typically have the same or similar
function, but sometimes do not: due to lack of the original
selective pressure upon one of copy of the duplicated gene,
this copy is free to mutate and acquire new functions.
Homologies:
Rouse
Hemoglobin
 Hemoglobin
Rouse
Mouse
 Hb
Gene
duplication
 Hemoglobin
Speciation
Rat
 Hb
Paralogs
Rouse
Mouse
 Hb
Rat
 Hb
Orthologs
The genes encoding myoglobin and hemoglobin are
considered to be ancient paralogs.
Similarly, the four known classes of hemoglobins
(hemoglobin A, hemoglobin A2, hemoglobin S, and
hemoglobin F) are paralogs of each other.
While each of these genes serve the same basic function of
oxygen transport, they have already diverged slightly in
function: fetal hemoglobin (hemoglobin F) has a higher
affinity to oxygen than adult hemoglobin.
Paralogous genes often belong to the same species, but this is
not necessary: for example, the hemoglobin gene of humans
and the myoglobin gene of chimpanzees are paralogs. This is
a common problem in bioinformatics: when genomes of
different species have been sequenced and homologous genes
have been found, one can not immediately conclude that
these genes have the same or similar function, as they could
be paralogs whose function has diverged.
QuickTime™ and a
decompressor
are needed to see this picture.
Where it all starts…..
http://www.expasy.ch/sprot/
-
sequence
+
conservation
structure
function
Function is attributed to very few atoms absolutely
conserved during the evolutionary process
The ‘centrality’ of a 3D structure
How Can We Compare Sequences ?
The Twilight Zone
%Sequence Identity
Similar Sequence
Similar Structure
Different Sequence
Structure ????
Same 3D Fold
30%
30
Twilight Zone
Length
100
How Do Sequences Evolve ?
In a structure, each Amino Acid plays a Special Role
+
On the surface,
CHARGE MATTERS
In the core,
SIZE MATTERS
Divergent evolution?
Convergent evolution?
Inactivation of neurotransmitters
Sensorial cellular response
sequence identity
for 104 over 198 residues
Structures are collected in the PDB databank http://www.rcsb.org/pdb/home/home.do
The Protein Data Bank (PDB) is a repository for 3-D structural data
of proteins and nucleic acids.
This data, typically obtained by X-ray crystallography or
NMR spectroscopy, is submitted by biologists and biochemists
from around the world, is released into the public domain,
and can be accessed for free.
The database is the central repository for biological structural data.
PDB COORDINATE FILE FORMAT
Figure 1: Each separate column in a given section of a PDB file is designated as a different "field”
#Definition of PDB format:
#http://www.wwpdb.org/documentation/format23/v2.3.html
#The ATOM record:
#
#COLUMNS
DATA TYPE
FIELD
DEFINITION
#-------------------------------------------------------------------------------# 1 - 6
Record name
"ATOM "
# 7 - 11
Integer
serial
Atom serial number.
#13 - 16
Atom
name
Atom name.
#17
Character
altLoc
Alternate location indicator.
#18 - 20
Residue name
resName
Residue name.
#22
Character
chainID
Chain identifier.
#23 - 26
Integer
resSeq
Residue sequence number.
#27
AChar
iCode
Code for insertion of residues.
#31 - 38
Real(8.3)
x
Orthogonal coordinates for X
#
in Angstroms.
#39 - 46
Real(8.3)
y
Orthogonal coordinates for Y
#
in Angstroms.
#47 - 54
Real(8.3)
z
Orthogonal coordinates for Z
#
in Angstroms.
#55 - 60
Real(6.2)
occupancy
Occupancy.
#61 - 66
Real(6.2)
tempFactor
Temperature factor.
#73 - 76
LString(4)
segID
Segment identifier,
#
left-justified.
#77 - 78
LString(2)
element
Element symbol,
#
right-justified.
#79 - 80
LString(2)
charge
Charge on the atom.
Atomic coordinates
Contact plots
Calculate the distances between C-beta atoms
Define a distance cut-off (7 Angstrom)
If distance is shorter than cut-off define it as a contact
$atomDist = sqrt(($xCoor[$cBeta[$i]] - $xCoor[$cBeta[$j]])**2 +
($yCoor[$cBeta[$i]] - $yCoor[$cBeta[$j]])**2 +
($zCoor[$cBeta[$i]] - $zCoor[$cBeta[$j]])**2);
Human prion protein
Ovine prion protein variant R168
Ovine prion
Sheep prion
Divergent evolution?
Divergent evolution?
Convergent evolution?
Inactivation of neurotransmitters
Sensorial cellular response
Convergent evolution?
Proteins are organised in domains with specific architecture
QuickTime™ and a
decompressor
are needed to see this picture.
So proteins with similar function have similar domain architecture!!
Domains: conserved folded units stable in isolation
DTGM Fusion Protein to Treat Patients With Recurrent or Refractory Acute Myeloid Leukemia
Anticancer drug formed by the combination of diphtheria toxin and a colony- stimulating factor (GM-CSF)
Calmodulin
http://structbio.vanderbilt.edu/cabp_database/pic_gallery/ca_coord/cam_chapter1.html
EF-hand Sequences
in Pfam Database
1 3 5 7 9 12
X Y Z # -X -Z
Protein function can be divided into three broad areas:
molecular function, biological process and cellular component.
Molecular function describes activity at the molecular level, such as catalysis, which is
commonly predicted through methods that identify homologues or orthologues.
Biological process describes broader functions that are carried out by assemblies of
molecular functions, such as a particular metabolic pathway.
Genomic inference methods can identify the direct physical protein–protein
interactions and indirect functional associations that are found in biological processes.
Cellular component describes the compartment(s) of a cell in which the protein
performs its function. This component can be predicted through methods that predict
signal sequences, residue composition, membrane association or post-translational
modifications.
Within these areas there are broad categories of computational methods, all of which
ultimately depend on experimental data.
From Lee D, Redfern O, Orengo C.
Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol. 2007 8(12):995-1005
What does it means function?
EC nomenclature
Go project provides three structured vocabularies (ontologies)
to describe gene products in terms of their
1)associated biological processes; 2)cellular components; 3)molecular functions
http://www.blast2go.de/
http://www.geneontology.org/GO.annotation.shtml
From Lee D, Redfern O, Orengo C.
Predicting protein function from sequence and structure. Nat Rev Mol Cell Biol. 2007 8(12):995-1005
Learning outcomes of the lecture
The relevance of the knowledge of a 3D structure:
why? Implications?
What are the most common secondary structure elements
What are the ‘teachings’ of the PDB database
How can we approach the search of the function of a protein or a gene