Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures

1. HOMOLOGY MODELLING
SUBMITTED BY
AYESHA AKTAR K CHOUDHURY
DEPTT OF PHARM. SCIENCES
M.PHARM 2ND SEM, ROLL-10

2. HOMOLOGY MODELLING
INTRODUCTION:
 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 3D structure of a related homologous protein (the
"template").
 Prediction of the three dimensional structure of a given protein sequence i.e.
target protein from the amino acid sequence of a homologous (template)
protein for which an X-ray or NMR structure is available based on an
alignment to one or more known protein structures.
 If similarity between the target sequence and the template sequence is
detected, structural similarity can be assumed.
 In general, 30% sequence identity is required to generate an useful model.

3. SEQUENCE SIMILARITY & STRUCTURAL
SIMILARITY
As long as the length of two sequences and the percentage of identical residues fall in
the region marked as “safe” the two sequences are practically guaranteed to adopt a
similar structure.

4. HOMOLOGY MODELLING CONCEPT

5. STRUCTURE PREDICTION BY HOMOLOGY
MODELLING

6. AN EXAMPLE
 To know the structure of sequence A (150 amino acids long), 1ST of all compare
sequence A to all the sequences of known structures stored in the PDB (using, for
example, BLAST), if a sequence B (300 amino acids long) containing a region of
150 amino acids that match sequence A with 50% identical residues.
 As this match (alignment) clearly falls in the safe zone(50%) , we can simply
take the known structure of sequence B (the template), cut out the fragment
corresponding to the aligned region, mutate those amino acids that differ between
sequences A and B, and finally arrive at our model for structure A. Structure A is
called the target and is of course not known at the time of modeling.
HISTORY
 The first homology modelling studies were done using wire and plastic models of
bonds and atoms as early as the 1960’s. The models were constructed by taking the
coordinates of a known protein structure and modified by hand for those amino
acids that did not match the structure.
 In 1969 David Phillips, Brown and co-workers published the first paper regarding
homology modelling. They modelled a-lactalbumin based on the structure of hen-
egg white lysozyme. The sequence identity between these two proteins was 39%.

7. STEPS OF HOMOLOGY MODELLING
1.Template recognition
and initial alignment
2. Alignment correction
3. Backbone generation
4. Loop modeling
5. Side-chain modeling
6. Model optimization
7.Model Validation
Protein Sequence
Database SearchesSequence alignment
Good
Structure
homologue?
Secondary structure
prediction
Improve alignment
using secondary
structure prediction
Homology modelling
Minimisation
Check model
Three dimensional
structure

8. 1.Template recognition and initial alignment
 Template recognition & selection involves searching the PDB for
homologous proteins with determined structures. The search can be
performed using simple sequence alignment programs such as BLAST or
FASTA as the percentage identity between the Target sequence and a
possible template is high enough in the safe zone, to be detected with these
programs.
 To obtain a list of hits-the modeling templates and corresponding
alignments the program compares the query sequence to all the sequences
of known structures in the PDB using mainly two matrices:
1. A residue exchange matrix
2. An alignment matrix .

10. 2. Alignment correction
 Sometimes it may be difficult to align two sequences in a region where the
percentage sequence identity is very low. One can then use other sequences
from homologous proteins to find a solution.
 For ex: To align the sequence LTLTLTLT with YAYAYAYAY which is nearly
impossible, then only a third sequence, TYTYTYTYT, that aligns easily to
both of them can solve the issue.
 2 is correct, because it leads to a small gap, compared to a huge hole
associated with alignment 1.

12. 3.BACKBONE GENERATION
 When the alignment is correct, the backbone of the target can be created.
 The coordinates of the template-backbone are copied to the target.
 When the residues are identical, the side-chain coordinates are also copied.
4.LOOP MODELLING
 After the sequence alignment, there are often regions created by insertions
and deletions that lead to gaps in alignment. These gaps are modeled by
loop modeling, which is less accurate. Currently, two main techniques are
used to approach the problem:
 The database searching method - this involves finding loops from known
protein structures and superimposing them onto the two stem regions (main
chains mostly) of the target protein. Some specialized programs like
FREAD and CODA can be used.
 The ab initio method - this generates many random loops and searches for
one that has reasonably low energy and φ and ψ angles in the allowable
regions in the Ramachandran plot.

13. The red loop is modeled with the green
residues as anchor residues. The insertion
of 2 residues results in a longer loop.

14. 5.Side-Chain Modeling
 This is important in evaluating protein–ligand interactions at active sites
and protein–protein interactions at the contact interface.
 A side chain can be built by searching every possible conformation for
every torsion angle of the side chain to select the one that has the lowest
interaction energy with neighboring atoms.
 A rotamer library can also be used, which has all the favorable side chain
torsion angles extracted from known protein crystal structures.

15. 6: Model Optimization
 energy minimization procedure on the entire model, by adjusting the
relative position of the atoms so that the overall conformation of the
molecule has the lowest possible energy potential. The goal is to relieve
steric collisions without altering the overall structure.
 Optimization can also be done by Molecular Dynamic Simulation which
moves the atoms toward a global minimum by applying various stimulation
conditions (heating, cooling, considering water molecules) thus having a
better chance at finding the true structure.
 Energy = Stretching Energy +Bending Energy +Torsion Energy +Non-
Bonded Interaction Energy

16. 7.Model Validation
 Every homology model contains errors. Two main reasons are:
1. The percentage sequence identity between template and target. If it is
greater than 90%, the accuracy of the model can be compared to
crystallographically determined structures & if less than 30% large error
occurs
2. The number of errors in templates
 The final model has to be evaluated for checking the φ–ψ angles, chirality,
bond lengths, close contacts and also the stereo chemical
properties. Modeling Programs like Modeller, SWISS MODEL,
Schrodinger, 3D- JIGSAW.
 A successful model depends on template selection, algorithm used and the
validation of the model.

17. Advantages
 It can find the location of alpha carbons of key residues inside the folded
protein.
 It can help to guide the mutagenesis experiments, or hypothesize structure-
function relationships.
 The positions of conserved regions of the protein surface can help identify
putative active sites, binding pockets and ligands.
Disadvantages
 Homology models are unable to predict conformations of insertions or
deletions, or side chain positions with a high level of accuracy.
 Homology models are not useful in modeling and ligand docking studies
necessary for the drug designing and development process. However, it
may be helpful for the same, if the sequence identity with the template is
greater than 70%.

18. RAMACHANDRAN PLOT
 In a polypeptide the main chain (N-Calpha)
and (Calpha-C bonds) relatively are free to
rotate. These rotations are represented by the
torsion angles phi (φ) and psi(ψ ), respectively.
 A Ramachandran plot (or a [φ,ψ] plot),
originally developed in 1963 by G. N.
Ramachandran, C. Ramakrishnan, and V.
Sasisekharan,is a way to visualize backbone
dihedral angles ψ against φ of amino acid
residues in protein structure.
A Ramachandran plot can be used:
 One is to show in theory which values, or
conformations, of the ψ and φ angles are
possible for an amino-acid residue in a protein.
 second is to show the empirical distribution of
datapoints observed in a single structure in
usage for structure validation, or else in a
database of many structures.