Download Structural Bioinformatics In this presentation……

Structural
Bioinformatics
In this presentation……
Part 1 – Proteins & Proteomics
Part 2 – Protein Structure & Function
Part 3 – Analysis & Visualization
Part 4 – Protein Structure Prediction
Part
1
Proteins &
Proteomics
Proteins
• Proteins are the fundamental building blocks of life
• Enzymes are proteins that are molecular machines
responsible for all the chemical transformations cells
are capable of
• Those structure that are not made of proteins are
produced by enzymes (which are proteins)
• A human contains proteins of the order of 100,000
different proteins
• Proteins are of variable length and shape
Structural types and conceptual
models
• Globular proteins are soluble in
predominantly aqueous solvents such as the
cytosol and extra-cellular fluids, and
integral membrane proteins exist within the
lipid-dominated environment of biological
membranes
• Conceptual models of protein structure are
valuable aids to understanding protein
bioinformatics
Globular proteins
• The linear amino acid polymer forms a 3D
structure by folding into a globular compact
shape
• Globular proteins tend to be soluble in
aqueous solvents and folding is dominated
by the hydrophobic effect, which directs
hydrophobic amino acid side-chains to the
structural core of the protein, away from the
solvent
Secondary structure
• Globular proteins usually contain elements of
regular secondary structure, including –helices
and –strands
• These are stabilized by hydrogen bonding and
contribute most of the amino acids to globular
protein cores
• Residues in regular secondary structures are given
the symbol H, meaning helix, or E (or B), meaning
extended or  strand
Folding of polypeptide chain into an  helix
0.54 nm (3.6 amino
acid residues per turn)
C atoms of consecutive
amino acid residues
Position of polypeptide
backbone consisting of
0.15 nm (100°
C and peptide bond C-N
rotation per residue)
atoms
Cross-sectional view of an  helix showing the positions of the side-chains (R groups)
of the amino acids on the outside of the helix
Amino acid side-chains
H
N
C
C
H
R1 O
H
H
O
N
C
C
R2
H
N
C
C
H
R3 O
H
H
O
N
C
C
R4
H
N
C
C
H
R5 O
In the  helix the
CO group of residue
n is hydrogen
bounded to the NH
group on residue
(n+4)
Hydrogen bond
R
R
R
R
R
R
R
R
Cross-sectional view of an  helix showing the
positions of the side-chains (R groups) of the amino
acids on the outside of the helix
Tertiary structure
• It is the full 3D atomic structure of a single
peptide chain
• It can be viewed as the packing together of
secondary structure elements, which are
connected by irregular loops that lie
predominantly on the protein surface
• Loop residues are given the symbol C to
distinguish them from residues in helices or
strands
Tertiary Structures
Quaternary structures
• Several tertiary structures may pack
together to form the biologically functional
quaternary structure
Quaternary Structures
Integral membrane proteins
• These exist within biological lipid membranes and
obey different structural principles compared with
globular proteins
• They contain runs of generally hydrophobic amino
acids, associated with membrane-spanning
segments (often but not exclusively helices),
connected by more hydrophilic loops that lie in
aqueous environments outside the membrane
• Membrane proteins are very important components
of cellular signaling and transport systems
Domains
• Proteins tend to have modular architecture
and many proteins contain a number of
domains, often with mixed types, for
example mixed integral membrane and
globular domains
Evolution
• In globular proteins, surface residues in
loops evolve (change) more quickly than
residues in the hydrophobic core
• In integral membrane proteins, the most
slowly evolving residues are those in the
membrane-spanning regions
Protein structure prediction
• Identifying all of the proteins in a human is one thing, but to
truly understand a protein’s function scientists must discern its
shape and structure
• The structural genomics initiative calls for use of quasiautomated x-ray crystallography to study normal and
abnormal proteins
• Conventional structural biology is based on purifying a
molecule, coaxing it to grow into crystals and then
bombarding the sample with x-rays. X-rays bounce off the
molecule’s atoms, leaving a diffraction pattern that can be
interpreted to yield molecule’s overall 3D shape
• A structural genomics initiative would depend on scaling up
and speeding up the current techniques
• By figuring out which of the unknown proteins
associated with previously identified ones, the
CuraGen and University of Washington scientists were
able to sort them into functional categories, such as
energy generation, DNA repair, aging
• Eventhough yeast is an excellent prototype,
Drosophila is good when desired to study an organism
with multiple cells
Other methods for protein
prediction
• Another method for studying proteomes is called
“guilt by association”: learning about the function
of a protein by assessing whether it interacts with
another protein whose role in a cell is known
• A group lead by Stanley Fields of University of
Washington reported that they had deduced 957
interactions among 1,004 proteins in baker’s yeast
[S. cerevisiae]
• A machine devised by Hochstrasser and his research group
goes one step further than the robots. It would
automatically extract the protein spots from the gels, use
enzymes to chop the proteins into bits, feed the pieces into
a laser mass spectrometer and transfer the information to a
computer for analysis
• With or without robotic arms, 2-D gels have their
problems. Besides being tricky to make, they do not
resolve highly charged or low mass proteins very well
• They also do a poor job of resolving proteins with
hydrophobic regions, such as those that span the cell
membrane. This is a major limitation, because membranespanning receptors are important drug targets
• Fields and his colleagues first devised a widely used method for
studying protein interactions called the yeast two-hybrid
system, which uses known protein “baits” to find “prey”
proteins that bind to the “baits”
• Another way to study proteins that has recently become
available involves so called protein chips. Ciphergen
Biosystems, a biotechnology company in Palo Alto, is selling a
range of strips for isolating proteins according to various
properties, such as whether they dissolve in water or bind to
charged metal atoms. Strips can then be placed in chip reader,
which includes a mass spectrometer, for identifying the proteins
What’s new
• Knowing the exact structural form of each of the
proteins in the human proteome should, in theory,
help drug designers devise chemicals to fit the slots
on the proteins that either activate them or prevent
them from interacting
• Such efforts, which are generally known as rational
drug design, have not shown widespread success so
far – but then only roughly one percent of all human
proteins have had their structures determined
• After scientists catalogue human proteome, it will be
the proteins – not the genes – that will be all the rage
Part
2
Protein Structure &
Function
Structure and function
• Proteins rely upon the shapes and properties
of key functional areas of their 3D
structures to carry out biological functions
• Knowledge of protein structure is the key to
understanding protein function and this is
one reason for its importance in
bioinformatics
MUTZM
WTZM
Structural and functional
constraints
• Evolution accepts change to amino acid
residues in proteins where they have a neutral
or advantageous effect on protein structural
stability or protein function
• Residues can be conserved for structural or
functional reasons
• Amino acids are conserved where they are
uniquely able to fulfill particular structural roles
• This often occurs with cysteine, glycine and
proline
RPIP
TOLC 150
FGF1
XRCC4 300
Evolution of the
overall protein fold
• If two naturally occurring protein sequences
can be aligned to show more than 25
percent similarity over an alignment of 80
or more residues, then they will share the
same basic structure
• The Sander-Schneider formula gives the
higher threshold percentage identifies
necessary to guarantee structural similarity
from shorter alignments
Conservation of structure
• Protein structures tend to be conserved even
when evolution has changed the sequence
almost beyond recognition
• Structural knowledge is therefore a key
factor in understanding protein evolution
Evolution of function
• While structure tends to be conserved by
evolution, function is observed to change
• There are many examples of proteins whose
sequence and structure are very similar, but
which have different functions
• When function has changed, key functional
residues change as well, and this is often
clear in multiple sequence alignments
Multiple sequence alignment
• Understanding how structures evolve can help us
understand multiple sequence alignments
• Key structural and functional residues are often
observed to be conserved
• Insertions and deletions are seen to occur
preferentially in hydrophilic surface loops by
comparison with regular secondary structure elements
• Loops are also subject to faster mutational change
• Conservation of hydrophobic core residues in
secondary structure elements is also common, as are
conservation patterns associated with amphipathic
helices
Part
3
Analysis &
Visualization
Software, data and WWW sites
• A large variety of software for structure
visualization, alignment and analysis is
available on the WWW
• All published protein structures are
submitted to a public database. Database
search and down can be performed at varios
WWW sites
• Rasmol, Chime and Cn3d are commonly
used programs for viewing structural data
Structural and functional analysis
of structures
• There is an enormous amount of software
available for structural data analysis, and also
several WWW sites holding pre-prepared
analyses
• Functional sites in protein structures typically
contain a few residues in defined spatial
positions
• Software and databases have been developed
to locate and search for similarity in such
sites
Structural alignment
• It can be very difficult to find correct, biologicallymeaningful alignments of very distantly related
protein sequences because they contain only a very
small proportion of identical monomers
• In such cases, structural information can help because
evolution tends to change structure less
• Superimposing the backbones of similar structures
implies structurally equivalent residues and this
process is known as structural alignment
Structural similarity
• Structural alignment methods often produce
measure of structural similarity
• The most common of these is the RMSD,
which is reported by most programs
• This the root mean square difference in
position between the  carbon atoms of
aligned residues in optimal structural
superposition
Why classify protein structures?…
• Classification groups together proteins with
similar structures and common evolutionary
origins
• Examples
– CATH, available at
http://www.biochem.ucl.ac.uk/bsm/cath
– SCOP, available at
http://scop.mrc-lmb.cam.ac.uk/scop
Structural classes
• Proteins can be assigned to broad structural
classes based on secondary structure content
and other criteria
• CATH has four such broad classes, but
SCOP uses more, giving a more detailed
description of structural class
Fold or topology
• All classifications gather together proteins
with the same overall fold or topology
• Proteins in the same fold or topology class
contain more or less the same SSEs,
connected in the same way and in similar
relative spatial positions
Homologs and analogs
• Homologs (homologous proteins) are
related by divergent evolution from a
common ancestor, and have the same fold
• Analogs (analogous proteins) have the same
fold, but other evidence for common
ancestry is weak
Super-folds
• Super-folds are proteins folds that seem likely to have
arisen more than once in evolution
• They are thought to have advantageous physiochemical properties
• They appear in SCOP and CATH as fold or topology
levels containing several homologous super-families
• Examples are the TIM barrel and immunoglobulin
fold
• Characteristics are that they tend to exhibit
approximate symmetries, and are characterized by
repeated super-secondary structures
Part
4
Protein Structure
Prediction
Why predict structure?…
• Structure prediction is interesting because
experimental structure determination is still
much slower than sequence determination
• Structure predictions help us to understand
function and mechanism and can be used for
rational drug design
• The early work of Levinthal and Anfinsen
made structure prediction a fascinating
scientific problem
Structure prediction methods
•
•
•
•
•
Comparative modeling
Secondary structure prediction
Fold recognition
Ab initio prediction
Transmembrane segment prediction
Theoretical basis of comparative
modeling
• Sequences with more than 25 percent
identity over an alignment of 80 residues or
more adopt the same basic structure
• The is the basis of prediction by
comparative modeling
Ingredients
• All that is needed is an alignment between a
sequence of unknown structure (target) and one or
more of known structure (template(s)) with the above
property
• Template structures can be found by standard
sequence similarity search methods
• Lack of suitable template structures is the main
limitation of the method, but structural genomics
projects are likely to change this in coming years
• The accuracy of the alignment is crucial if good
prediction is to be obtained
The process of prediction
• Known structure(s) (templates) are used as the basis
of prediction
• The process can then be viewed conceptually as
comprising placement of conserved core residues,
modeling of variable loops, side-chain positioning
and optimization, and model refinement
• Conserved residues and some side-chain positions
can be obtained directly from structural information
in the templates
• Modeling of variable loops often makes use of the
spare parts algorithm, and there are sophisticated
algorithms for side-chain placement to obtain an
optimally packed hydrophobic core
Protein
prediction – I
Protein prediction – II
Accuracy of comparative modeling
• Accuracy is controlled almost entirely by
the quality of the alignment
• Good alignments yield good predictions
with most of the main software packages
• Of all prediction methods, comparative
modeling produces the most accurate
models
Secondary structure prediction
• It predicts the conformational state of
each residue in three categories
– Helical
– Extended or strand
– Coil
Methods
• Many methods are based on ideas related to
secondary structure propensity, which is a number
reflecting the preference of a residue for a particular
secondary structure
• Early methods had accuracies of around 60 percent
(the percentage of residues predicted in the correct
helical/extended/coil state)
• Examples of early methods are the Chou-Fasman
rule-based method and the information-theoretical
GOR method
Multiple sequence information
• Using multiple alignments of related
sequences can improve prediction
accuracy enormously by revealing
patterns of conservation indicative of
certain secondary structures
Accuracy of state-of-the-art methods
• Currently methods claim an average
accuracy over trusted test sets of proteins
equal to more than 70 percent of residues
correctly predicted
• This increase in accuracy can be attributed
to the availability of more structural data,
and the use of more sophisticated
algorithms or methods
Prediction of trans-membrane
segments
• Membrane-spanning segments in integral
membrane proteins can be predicted with
reasonable accuracy
• Most methods make use of a search for contiguous
runs of hydrophobic residues that span a lipid
membrane
• Some methods also predict the orientation (in-out)
or topology of the membrane-spanning segments,
but this is usually less accurate
Availability of tools
• Most of the secondary structure and
trans-membrane segment prediction
tools are available from the ExPASy
WWW site, at http://www.expasy.ch
Fold recognition
• It aims to detect very distant structural and
evolutionary relationships
• It aims to detect when a protein adopts a known fold
even it does not have significant sequence similarity
to any protein of known structure
• Methods generally try to find the most compatible
fold in a library of known folds using both sequence
and structural information
• An alternative term for fold recognition is threading
Ab initio prediction
• These methods rely on first principles
calculation and are not yet sufficiently
well developed to be of real use in
practical structure prediction
Difficulties in modeling in silico
• Not all occurrences of a desired part or fragment are
to be found and changed but only particular one
• Proteins are globular and not solid objects. They
behave differently for different drug molecules
• Penetration through cell wall, then through nucleus to
DNA is not possible as this could effect the entire cell
– the only way is by module it over mRNA
Membrane protein
•
•
•
•
Total entries in PDB
20173
Proteins
18162
Membrane proteins
only 8
The membrane proteins are highly suitable for
docking drug into the proteins
Do not dissolve in water – tried enough with NMR
Crystallization of membrane proteins is a difficult task
as they cause damage to other structures of the cell
After failure of crystallography and NMR, it is the
turn of computers (for in silico protein modeling and
drug design)
Approach to protein modeling
• Conventional protein modeling technique is to
compute all the folding, side-chain arrangement
and visualize the final protein structure
• But a new method wherein the side-chain
arrangements are computed separately first and
then folding computations are done in parallel.
Finally, complete information is integrated. This
method proved to be million times faster as
compared to the conventional method
Template library of fragments
• The process of protein modeling becomes extremely
easier if all the common fragments or side-chains are
developed and stored as molecule template library
• This technique reduces the time consumed greatly as
well as speeds up the visualization
• Until date, about 180 templates of various organic
compounds have been identified and developed at IIT
Delhi
• All other compounds or molecules can be modeled by
suitably assembling them together
• It would also be easier to compare different proteins
with this approach
Protein folding
• It is a well known fact that any protein would fold
as and when it reaches 30 nm
• Also, it has been found that due to the globular
structure, proteins cannot take over 8 sheets and
strands
• It has been seen that due to the heavy molecular
weight, extra large protein molecules disintegrate
• Phosphates in DNA repel and hence form coil
structures, which add to difficulty in folding and
modeling them in silico
Folding through computers
• Keeping in view the possible number of sheets or
strand attachment to a protein, which can occur at
45˚ interval, in 3D there could be 26 possibilities
of folding
• The folds could be easily simulated or modeled
through use of a computer as it would take 226
minutes for folding a protein @ one fold per
minute, which is about 50 years!!
• With 100 processors running throughout, this
could be achieved in about 50 days