Download protein modelling

Presented
by
Sadhana S
 Protein
structure prediction/protein modelling
is the prediction of the three-dimensional
structure of protein from its amino acid
sequence
i.e., the prediction of its folding & its
secondary, tertiary, & quaternary
structure from its primary structure
Why to predict protein structure?

Owing to significant efforts in genome sequencing
over nearly three decades, gene sequences from many
organism have been deduced.

Over 100 million nucleotide sequences from over
300 thousand different organisms have been
deposited in the major DNA databases, DDBJ/
EMBL/GenBank
totaling almost 200 billion
nucleotide bases.

Over 5 million of these nucleotide sequences have
been translated into amino acid sequences and
deposited in the UniProtKB database.

However, the protein sequences themselves are
usually insufficient for determining protein function
as the biological function of proteins is intrinsically
linked to three dimensional protein structure.

The most accurate structural characterization of
proteins is provided by X-ray crystallography and
NMR spectroscopy.

Owing to the technical difficulties and labor
intensiveness of these methods, the number of protein
structures solved by experimental methods lags far
behind the accumulation of protein sequences

Many proteins are simply too large for NMR
analysis and cannot be crystallized for X-ray
diffraction.
 Protein
modeling(computational methods) is
the only way to obtain structural information if
experimental techniques fail.
 The
ultimate goal of protein modeling is to
predict a structure from its sequence with an
accuracy that is comparable to the best results
achieved experimentally.
Can we predict structure from sequence?
Computational Methods
The three major approaches for threedimensional (3D) structure predictions are
 Ab
initio methods
 Threading
methods
 Comparative
modelling / homology modelling
What is Homology Modelling?

It is the prediction of the three-dimensional
structure of a given protein sequence (target)
based on an alignment to one or more known
protein structures (templates).

If similarity between the target sequence and
the template sequence is detected, structural
similarity can be assumed.
Homology Modelling
Homology modeling, also known as Comparative
modeling of protein is the technique which allows to
construct an unknown atomic-resolution model of the
"target" protein from:
1. Its amino acid sequence and
2.An experimental 3Dstructure of a related homologous
protein (the "template").
Basis for homology modelling?
1.
Structure of a protein is uniquely determined
by its amino acid sequence
2.
Structure is much more conserved than
sequence during evolution.
 Proteins
sharing high sequence similarity should
have similar protein fold.
 Higher
the similarity, higher is the confidence
in the modeled structure.
Homology modeling is a multistep process
that can be summarized in seven steps:
1. Template recognition & initial alignment
2. Alignment corrections
3. Backbone generation
4. Loop modeling
5. Side-chain modeling
6. Model optimization
7. Model validation
TEMPLATE RECOGNITION
 Achieved
by searching the PDB of known protein
structures using the target sequence as the query.

Templates can be found using the target
sequence as a query for searching using FASTA
or BLAST, & PSI-BLAST or PDB-BLAST

Select the best template(min.30%) from a
library of known protein structures derived
from the PDB.
ALIGNMENT

Purpose – to propose the homologies between the sites in
two or more sequences

Insertions & deletions are placed
Types
1. Pairwise alignment
2. Multiple alignment

 Correct
alignment is necessary to create the most
probable 3D structure of the target.
 If
sequences aligns incorrectly, it will result in false
positive or negative results.
 Important
 gap
steps to consider:
penalties
 Scoring alignments
 Alignment algorithms
Alignment Corrections
 Alignments
are scored (substitution score) in
order to define similarity between 2 amino
acid residues in the sequences
A
substitutions score is calculated for each
aligned pair of letters.
 Alignment
FASTA
algorithms- DPA, BLAST &
Structure of alignment 1 and 2 with the template
Alignment Outcome
The
(true) alignment indicates the
evolutionary process giving rise to the
different sequences starting from the
same ancestor sequence and then
changing through mutations (insertions,
deletions, and substitutions)
BACKBONE GENERATION

One simply copies the coordinates of those
template residues that show up in the
alignment with the model sequence
 If
two aligned residues differ- only backbone
coordinates(N, C-alpha, C & O) are copied
 It
they are same- side chain is also included
Backbone Generation
 For
SCRs - copy coordinates from known
structures.
 For variable regions (VR) - copy from known
structure, if the residue types are similar;
otherwise, use databases for loop sequences.
Loop Modelling
Knowledge based- PDB is searched
2. Energy based- energy function is used to
judge the quality of loop
 Molecular modeling/dynamic programs are
used
1.
Loop Modelling
Side Chain Modelling
1. Use of rotamer libraries (backbone dependent)
2. Molecular mechanics optimization
- Dead-end elimination (heuristic)
- Monte Carlo (heuristic)
- Branch & Bound (exact)
Model refinement/optimization

Idealization of bond geometry

Removal of unfavorable non-bonded contacts

Performed by energy minimization with force fields such
as CHARMM, AMBER, or GROMOS

Major errors are removed
Evaluation/validation of the
model
 Internal
evaluation
 Self-consistency checks
 Assessment of stereochemistry of the model
 PROCHECK & WHATCHECK
 External
evaluation
 Tests whether a correct template was used
 PROSA & VERIFY3D
Applications

Designing mutants to test hypotheses about the function
of a protein.

Identifying active & binding sites.

Predicting antigenic epitopes.

Simulating protein-protein docking.

Confirming a remote structural relationship.
Web servers
Swiss- model server (http://www.expasy.ch/swissmod/)
 CPHModels
(http://www.cbs.dtu.dk/services/CPHmodels/)
 SDSC1 (http://www.cl.sdsc.edu/hm)
 FAMS
(http://www.physchem.pharm.kitasatou.ac.jp/FAMS/fams.html)
 ModWeb (http://www.guitar.rockefeller.edu/modweb)

References

Zhumur Ghosh & Bibekanand mallik. bioinformaticsPrinciples & applications. Oxford university press

S C Rastogi, N.Mendiratta, & P Rastogi. Bioinformaticsmethods & applications. Eastern economy edition.
Prentice hall of India. New Delhi

Philip.E.Bourne & Helge Wiessig. Structural
Bioinformatics. John Wiley & Sons. NewYork

C A Orengo, D T Jones & J M Thornton. Bioinformaticsgene, proteins, & computers. BIOS . Scientific Publishers