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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5728_Библиотеки_им_академика_М_И_Перельмана
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Fig. 12.8: The regions of the top six similar proteins where they show similarity with the query protein. The red regions of these proteins represent the regions of
high similarity with our query protein.
12.3 Steps involved in the process of homology modeling 279
Fig. 12.9: Sequencing result of all the proteins similar to the query protein. The protein with PDB ID 2J9F shows similarity in the starting region and the protein
with PDB ID 1DTW shows similarity in the later region of the query protein.
280 Chapter 12 Homology modeling
12.3.4.1 Rigid body assembly method
In this approach, rigid regions are first identified inside the aligned template struc-
tures. These rigid regions of the template structures that show similarity with the
Fig. 12.10: Multiple sequence alignment results of query protein with the top two similar target proteins
(with the proteins having PDB ID 2J9F and 1DTW).
12.3 Steps involved in the process of homology modeling 281
query protein are then used for building a backbone of the query structure. Once the
backbone is created, other nonconserved regions, which are loop and side chains, are
assembled on this backbone iteratively to obtain the final 3D structure of the query
protein. This algorithm is used by programs that were developed initially for homol-
ogy modeling. 3D-JIGSAW, SWISS-MODEL, and BUILDER are the most widely used and
popular programs for model building based on this algorithm [24].
12.3.4.2 Segment matching approach
This method is slightly different as compared to the conventional methods of model
building. In this approach, first, the query protein sequence is divided into small seg-
ments. The structures of these small segments are then searched in the databases of
known structures, based on the energy function, geometry constraints, or a combina-
tion of both [25]. Once the structure of the small segments is obtained from the data-
base, then these segments are combined to form the backbone of the query protein,
followed by developing loop and side chains on the backbone. SegMod/ENCAD is one
such program used for the segment-matching-based modeling of the proteins.
12.3.4.3 Satisfaction of spatial restraint method
Based on the structure of the template protein, certain spatial restraints are applied
to the query protein for building its 3D structures. The restrains that can be applied in
this type of modeling include:
– Angle restraints: This is the allowed angles between the atoms of a molecule. If
the template structure follows certain fixed bond angles, then this restrain is ap-
plied to the query protein model building.
– Distance restraint s: This restrain is applied when a de sired bond length is re-
quired between the atoms of query protein based on the pattern observed from
the template protein.
– Dihedral angle restrains: The dihedral angle is another important criterion that
is taken into consideration while developing the query protein model. A certain
value of the torsion angle is set that must be satisfied by the query protein while
building its 3D structure.
Once the model is created based on the spatial requirement elucidated from the tem-
plate structure, further energy minimization of the 3D structure of the query protein
is performed, based on molecular mechanics. MODELLER is the most common and
very popular software for the homology modeling works using this algorithm [26].
12.3.4.4 Artificial evolution approach
Artificial evolution approach is a hybrid approach to homology modeling in which
both the rigid body assembly method and stepwise template evolutionary mutati on
282 Chapter 12 Homology modeling
are employed simultaneously. The mutation in the template is done until it matches
with the sequence of the query protein. NEST is the program that is based on this al-
gorithm for generating the structure of the unknown proteins [27].
Figure 12.11 depicts the 3D model developed of the earlier employed query pro-
tein, dehydrogenase enzyme isolated from the species Mucilaginibacter terrenus. This
model is developed using the SWISS-MODEL program as explained above.
12.3.5 Refinement of the model developed
This is a very important and crucial step that should be given proper attention for the
development of a high-quality model of the query protein. The main objective of this
step is to provide further refinement to the generated structure through different ap-
proaches. Basically, three steps are performed during the refinement of the generated
model, including modeling of the loop, side chain modeling, and tuning of the final
structure of the query protein [28–30].
12.3.5.1 Modeling of the loops
Loops are the regions of the query protein that are not conserved. The sequence of
amino acids in these loops has no evolutionary similarity with any template and
hence their alignment cannot be performed. Loops are the regions of the structure
that combine two secondary structural parts of the protein (α-helix and β-strand). On
average, about one-third region of the total structure of the protein corresponds to
the region of the loop. Modeling of the loops is also of utmost importance as some-
times these regions encode for a special feature of the query protein. In drug discov-
ery, loops play an important role when the loop region coincides with the active
Fig. 12.11: The protein tertiary structure derived from homology modeling of our query protein
dehydrogenase enzyme isolated from the species Mucilaginibacter terrenus.
12.3 Steps involved in the process of homology modeling 283
binding site of the query protein. For the prediction of the loops, the following two
approaches are employed.
12.3.5.1.1 Database search approach
This is one of the most common methods employed for loop modeling and is also
known by the name knowledge-based, template-based, or homology-based approach.
In this method, the sequence of amino acids of the query protein is compared with
the structures of loops having similar sequences available in the database. This
method is suitable for modeling small-length loops. The most widely acceptable loop
modeling servers, based on this approach, are GalaxyLoop (https://galaxy.seoklab.org/
cgi-bin/submit.cgi?type=LOOP), DaReUS Loop (https://bioserv.rpbs.univ-paris-diderot.
fr/services/DaReUS-Loop/), and Fread (https://opig.stats.o x.ac.uk/webap ps/sabdab-
sabpred/sabpred/more/#FREAD).
12.3.5.1.2 Ab initio method of loop modeling
Ab initio method of loop modeling is an exhaustive method for modeling the loop re-
gion of the query proteins. In this approach, a conformational search of the loops is
performed based on the fo rce field-based scoring. The conformation that shows the
least energy i s considered the most acceptable loop. RCD+ (https://rcd.iqfr.csic.es/),
LOOPY (https://doi.org/10.1073/pnas.102179699), MODELLER, and LoopBuilder are pro-
grams and web servers that employ the ab initio method of loop modeling.
12.3.5.1.3 Hybrid methods of loop modeling
In the hybrid methods of loop modeling, a hybrid algorithm is employed for generating
the loops of the query protein. This type of program is developed on both the database
and the ab initio algor ithm for performing successful modeling of the loops. These
types of programs offer high speed and can also be performed on a large number of
residues containing loops. Sphinx (https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/
sabpred/more/#Sphinx) is one such popular web server that is developed on the hybrid-
based approach.
12.3.5.2 The modeling side chain of the model
Once the loops are constructed on the backbone of the structure of the query protein,
the next computational challenge is to derive the side chains of amino acids on this
backbone. Side-chain amino acids can exist in a limited number of possible conforma-
tions. Therefore, instead of considering all the possible conformational space, only
possible limited conformations of the amino acids are evaluated. These conformers of
the amino acid side chains are termed rotamers. Side chain predictability of the side
chain of the backbone of the model developed is based on two components:
284 Chapter 12 Homology modeling
12.3.5.2.1 Rotamer library
With an increasing number of entries in the database of the protein data bank (PDB),
ithasmadeitpossibleforresearcherstocreateseparatelibrariesoftherotamers
from this database. Two types of rotamers libraries are developed by different re-
searchers, the backbone-dependent and the backbone-independent libraries. Out of
these two, backbone-dependent library developed by Shapovalov and Dunbrack is the
most reliable and widely accepted library.
12.3.5.2.2 Search methods and scoring
A search method is employed for searching the optimal rotamers of the amino acid
chains. Accuracy and speed are two criteria based on which search methods are se-
lected for finding out the best rotamers. The most popular methods employed for this
purpose include simulated annealing-based search, dead-end elimination (DEE), and
fast sparse phase retrieval (FASP).
Energy-based scoring of the rotamers is then performed to select the energeti-
cally most favorable rotamer. For this purpose, force field-based scoring is the most
widely accepted method for scoring the rotamers. Programs such as RAMP (http://
www.ram.org/computing/ramp), CIS-RR (http://jianglab.ibp.ac.cn/lims/cisrr/cisrr.
html), and SCWRL (http://dunbrack.fccc.edu/Software.php) are used widely for the
amino acid side chain modeling of the developed model.
12.3.5.3 Optimization of the developed model
The final developed model, after the insertion of loops and side chains into the back-
bone of the initial model, required further optimization to obtain the most energeti-
cally acceptable model. The initial structure of the query protein obtained through
comparative modeling contains many errors in terms of the spatial arrangement of
the atoms, bond angles, bond lengths, and torsion angles. This correction is obtained
through various approaches to obtain a refined 3D model of the query protein. The
main objective of this step is to obtain a model close to nearly experimental values in
terms of spatial arrangement. The various approaches that are used for model optimi-
zation include:
12.3.5.3.1 Force field-based energy minimization
Force field-based energy minimization is the most popular approach for optimization
of the structure of the query protein. The energy of the 3D structure of the query pro-
tein is minimized further with the help of physics-based/knowledge-based force fields
to obtain an energetically favored structure. 3Drefine, i3Drefine, and Seok servers are
the most popular tools or web servers employed for energy minimization of the initial
model generated by the query protein.
12.3 Steps involved in the process of homology modeling 285
12.3.5.3.2 Molecular dynamics (MD)-based optimization
Molecular dynamics-based optimization of the 3D structure of the query protein has
gained a lot of popularity in the last decade. In this approach, Newton’s law of motion
is employed for refining the structure further using atom-based force fields. The first
refinement of the approximate 3D structure of the query protein was reported in
2008 by Jiang Zhu. Replica-exchange molecular dynamics (REMD) technique is the
most commonly employed method for the MD-based optimization of the raw structure
of the query protein. Galaxyrefine (https://galaxy.seoklab.org/cgi-bin/submit.cgi?type=RE
FINE) is one such web server that does this MD-based refinement with good accuracy.
Figure 12.12 depicts the 3D refined structure of the generated model of the dehydroge-
nase enzyme isolated from the species Mucilaginibacter terrenus using the Galaxyrefine
server. It is evident by comparing Figs. 12.11 and 12.12 that there is a significant differ-
ence in the folding of the earlier and refined structures of the same query protein.
12.3.5.3.3 Monte Carlo-based optimization of the structure
The Monte Carlo-based optimization approach is used as a supplement for further re-
finement of the model that is already optimized through any of the above two meth-
ods. Monte Carlo is a probability-based simple approach in which a new model is
proposed from the initial model and the new model is accepted or rejected based on
certain criteria. This whole process is repeated until the most acceptable model of the
query protein is obtained.
Fig. 12.12: Secondary structure of query protein after performing the refinement steps to the previously
derived model.
286 Chapter 12 Homology modeling
12.3.6 Assessment and validation of the developed model
The whole process of homology modeling is based on combining many small pro-
cesses to obtain a 3D structure of the protein. Inaccuracy in any of the processes or
steps can lead to errors in the finally generated model of th e query protein. Hence,
assessment and validation of the final model of the query protein is of immense ne-
cessity. The developed model of the query protein is assessed and validated for its
spatial aspects, which are done by analyzing the bond lengths, rotational angles, and
torsion angles of the developed model. WhatCheck9 (https://swift.cmbi.umcn.nl/gv/
whatcheck/), PROCHECK (https://www.ebi.ac.uk/thornton-srv/software/PROCHECK),
and Molprobity (http://molprobity.biochem.duke.edu/) are three most important web
servers for performing the assessment and validation of the final model. Table 12.2
depicts some of the important validation parameters of the generated 3D model calcu-
lated using the Molprobity server.
All these parameters are crucial in assessing the quality of the generated model. Out
of these, Ramachandran plot is of utmost importance showing us the number of
amino acids falling in the energetically favorable and forbidden regions. Figure 12.13
depicts the Ramachandran plot of the generated 3D model of the dehydrogenase en-
zyme isolated from the species Mucilaginibacter terrenus. In this plot, we can see four
regions, red-colored (most favored region), yellow-colored (additional favored region),
light yellow-colored (generously allowed region), and white-colored (forbidden or dis-
allowed region) [31].
Tab. 12.2: Validation parameters of the generated 3D model calculated using the Molprobity server.
S. no. Parameter Value of
our model
Acceptance criteria
Molprobity score . (th
percentile)
The structure in the th percentile is considered the
worst predicted and the one in the th percentile is
considered the best among the structures with similar
resolution
Favored rotamers
(.%)
Should be greater than %
Amino acids in
Ramachandran’s most
favored region
(.%)
Should be greater than %
Rama distribution Z-score . ± . Should be less than
12.3 Steps involved in the process of homology modeling 287
Fig. 12.13: Ramachandran plot of the query protein with 94.5% amino acids in the most favorable region.
288 Chapter 12 Homology modeling
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