Homology modeling is a technique used to predict the 3D structure of a protein from its amino acid sequence by comparing it to proteins with similar sequences whose structures are already known. It involves searching a database for template structures, aligning the target sequence to the template, building a model by transferring 3D coordinates from the template to the target sequence, and validating the resulting model. Homology modeling works best when the sequence identity between the target and template is over 30% since protein structure is more conserved than sequence over evolution.

1. Homology modelling
Presented by: Mayank Mehendiratta
Roll no. : 1114
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2. CONTENTS
INTRODUCTION
HOMOLOGY MODELLING
STEPS INVOLVED
ZONES OF SEQUENCE ALIGNMENTS
EXAMPLE
ADVANTAGES AND DISADVANTAGES
REFERENCES
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3. INTRODUCTION
• 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.
• Homology modelling determines structure model based on experimentally
determined structure closely related to sequence.
• It predicts structure based on sequence homology.
• It is also known as comparative modelling.
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4. HOMOLOGY MODELLING
• Predicts 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 , structure similarity can be assumed.
• In general,30% sequence identity is required to generate an useful model.
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5. HOMOLOGY MODELLING
• It is based on two major observations:
The structure of a protein is uniquely determined by its amino acid sequence
During evolution, the structure is more stable and changes much slower than
the associated sequence, so that similar sequences adopt practically identical
structures.
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6. STEPS INVOLVED
STEPS
INVOLVED
Template recognition and initial alignment
Alignment correction
Backbone generation
Loop modelling
Side-chain modelling
Model optimization
Model validation
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7. 1.Template recognition and initial
alignment
• Template recognition and selection involves searching the PDB for
homologous proteins with determined structures.
• The search can be done using simple sequence alignment programs such as
BLAST or FASTA as the %age identity between the target sequence and a
possible template is high enough to be detected with these programs.
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8. 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.
• Suppose we want to align the sequence LTLTLTLT with YAYAYAYAY.
• There are two equally poor possibilities and only a third sequence,
TYTYTYTYT, that aligns easily to both of them can solve the issue.
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9. 2.Alignment correction
• Fig: A pathological alignment problem. Sequences A and B are impossible to
align, unless one considers a third sequence C from a homologous protein.
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10. 3.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 the backbone coordinates (N,Cα,C and O)
can be copied.
• If they are the same, one can also include the side chain.
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11. 4.Loop modelling
• In the majority of cases, the alignment between model and template
sequence contains gaps.
• Either gaps in the model sequence or in the template sequence (insertions).
• In the first case, one simply omits residues from the template, creating a hole
in the model that must be closed.
• In the second case, one takes the continuous backbone from the template,
cuts it, and inserts the missing residues.
• Both cases imply a conformational change of the backbone.
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12. 5. Side chain modelling
• 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.
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13. 6. Model optimization
• To predict the side-chain rotamers with high accuracy, we need the correct
backbone, which in turn depends on the rotamers and their packing.
• The common approach to such a problem is an iterative one: predict the
rotamers, then the resulting shifts in the backbone, then the rotamers for the
new backbone, and so on, until the procedure converges.
• 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.
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14. 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.
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15. ZONES OF SEQUENCE ALIGNMENTS
• Fig: The two zones of sequence
alignments. Two sequences are
practically guaranteed to fold into
the same structure if their length
and percentage sequence identity fall
into the region marked as ‘‘safe.’’
• An example of two sequences with
150 amino acids, 50% of which are
identical, is shown (gray cross).
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16. EXAMPLE
• The structure of sequence A (150 amino acids long).
• We compare sequence A to all the sequences of known structures stored in the
PDB (using, for example, BLAST), and luckily find 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 , 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.
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17. FIG: The steps to homology
modeling. The fragment of the
template (arabinose-binding
protein) corresponding to the
region aligned with the target
sequence forms the basis of the
model (including conserved side
chains). Loops and missing side
chains are predicted, then the
model is optimized (in this case
together with surrounding water
molecules)
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18. ADVANTAGES
• It can help to guide the mutagenesis experiments or hypothesize structure
function relationships.
• The positions of conserved regions of the protein surface can help to
identify putative active sites, binding pockets and ligands.
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19. 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%.
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20. REFERENCES
• Homology modelling by Elmar Krieger, Sander B. Nabuurs, and Gert Vriend
• https://www.slideshare.net/AyeshaChoudhury/homology-modelling-
75836997
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