Homology Modeling in Drug Discovery: Overview, Current Applications and Future Perspectives

Abstract

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply.
There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial.
Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors, which consists mainly of proteins in their structure, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates.
Homology modeling plays an important role in making drug discovery faster, easier, cheaper and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.
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wileyonlinelibrary.com/journal/cbdd Chem Biol Drug Des. 2019;93:12–20.
© 2018 John Wiley & Sons A/S
1
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INTRODUCTION
The worldwide Protein Data Bank (wwPDB) (https://www.
wwpdb.org/) contains approximately 144,000 experimen-
tally determined protein three- dimensional (3D) structures
currently.[1] In contrast, the last reference sequence, which
is a nonredundant sequence, release of National Center for
Biotechnology Information (NCBI) (https://www.ncbi.nlm.
nih.gov/) consists of annotated 155 million sequences in-
cluding approximately 106 million protein sequences.[2]
This represents a protein sequence number that is 736 times
larger than the protein 3D structure deposited in the wwPDB.
In 2006, the annotated sequence in NCBI was nearly 120
times larger than experimentally solved 3D structures de-
posited in wwPDB.[3] This means the number of protein
sequences has increased six times faster than the number of
experimentally determined protein 3D structures. Since the
protein data banks available contain redundancy but the se-
quences in NCBI are nonredundant, the difference is higher
than the numbers given here. This growing gap between the
sequences available and the protein 3D structures determined
is in an alarming condition. Thus, computational structural
determination methods are needed in filling this widening
gap between the number of sequences available and protein
3D structures solved experimentally.
Since crystal structure of the first protein myoglobin was
solved in 1960, there has been an improvement in the qual-
ity of the 3D structures determined. This has been achieved
with the introduction of experimental methods like X- ray
crystallography and NMR spectroscopy.[4] However, these
experimental methods cannot be used for each protein. For
NMR analysis, protein molecules should be small, and for
Received: 9 May 2018
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Revised: 29 June 2018
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Accepted: 4 August 2018
DOI: 10.1111/cbdd.13388
REVIEW
Homology modeling in drug discovery: Overview, current
applications, and future perspectives
Muhammed Tilahun Muhammed1,2
|
Esin Aki-Yalcin3
1Department of Pharmaceutical
Chemistry,Faculty of Pharmacy,Suleyman
Demirel University, Isparta, Turkey
2Department of Basic
Biotechnology,Institute of
Biotechnology,Ankara University, Ankara,
Turkey
3Department of Pharmaceutical
Chemistry,Faculty of Pharmacy,Ankara
University, Ankara, Turkey
Correspondence
Muhammed Tilahun Muhammed,
Department of Pharmaceutical
Chemistry, Faculty of Pharmacy, Suleyman
Demirel University, Isparta, Turkey.
Email: muh.tila@gmail.com
Abstract
Homology modeling is one of the computational structure prediction methods that
are used to determine protein 3D structure from its amino acid sequence. It is consid-
ered to be the most accurate of the computational structure prediction methods. It
consists of multiple steps that are straightforward and easy to apply. There are many
tools and servers that are used for homology modeling. There is no single modeling
program or server which is superior in every aspect to others. Since the functionality
of the model depends on the quality of the generated protein 3D structure, maximiz-
ing the quality of homology modeling is crucial. Homology modeling has many ap-
plications in the drug discovery process. Since drugs interact with receptors that
consist mainly of proteins, protein 3D structure determination, and thus homology
modeling is important in drug discovery. Accordingly, there has been the clarifica-
tion of protein interactions using 3D structures of proteins that are built with homol-
ogy modeling. This contributes to the identification of novel drug candidates.
Homology modeling plays an important role in making drug discovery faster, easier,
cheaper, and more practical. As new modeling methods and combinations are intro-
duced, the scope of its applications widens.
KEYWORDS
3D structure, current application, drug discovery, homology modeling, structure prediction

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MUHAMMED AnD AKI- YALCIn
X- ray crystallography, the molecules should be crystallized.
Additionally, these methods are time- consuming. Thus, there
is deficiency in high- resolution 3D structure of proteins, es-
pecially membrane proteins due to the difficulties in purifi-
cation and crystallization of such proteins in relative to other
small water- soluble proteins.[5] Since membrane proteins
constitute important proportion of therapeutic drug targets,
advances in the determination of membrane proteins will
speed up the drug discovery process. Here, computational
protein 3D structure prediction can play a crucial role.
Homology modeling (comparative modeling) is one of
the computational structure prediction methods that are used
to determine 3D structure of a protein from its amino acid
sequence based on its template. The basis for homology
modeling is two major observations. First, protein 3D struc-
ture is particularly determined by its amino acid sequence.
Second, the structure of proteins is more conserved and the
change happens at a much slower rate in relative to the se-
quence during evolution. As a result, similar sequences fold
into identical structures and even sequences with low relation
take similar structures.[6]
Homology modeling is considered to be the most accurate
of the computational structure prediction methods.[7] Three-
dimensional structure predictions made by computational
methods like de novo prediction and threading were com-
pared to homology modeling using root- mean- square devia-
tion (RMSD) as a criteria. Homology modeling was found to
give 3D structures with the highest accuracy.[8] Furthermore,
it is a protein 3D structure prediction method that needs less
time and lower cost with clear steps. Thus, homology model-
ing is widely used for the generation of 3D structures of pro-
teins with high quality. This has changed the ways of docking
and virtual screening methods that are based on structure in
the drug discovery process.[9]
In this review, the main features of steps of homology
modeling are presented. The popular tools and servers that
have been used for homology modeling in recent years are
also summarized. Overview of the striking homology model-
ing applications in the prediction of protein 3D structures and
recent applications in the drug discovery are also discussed.
This review also provides insight into the opportunities and
possible challenges in homology modeling.
2
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STEPS OF HOMOLOGY
MODELING
Homology modeling is a structure prediction method that
consists of multiple steps. Homology modeling has common
standard procedures with minor differences. The standard
steps of homology modeling are summarized in Figure 1, and
the detail explanation is given below the figure.
2.1
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Identification And Selection
Of Templates
In this step of the process, target (query) sequence is used
for the identification of template structures in the PDB
(https://www.rcsb.org/)[10] or similar databases. There are
popular tools in searching for eligible templates for target
sequence with different approaches. Among them, Basic
Local Alignment Search Tool (blast)[11] is the one which
provides pairwise sequence–sequence alignment. This ser-
vice is available inside databases like NCBI[2] and UniProt
(http://www.uniprot.org/).[12] The other approaches used in
template identification are profile–profile alignments[13] and
hidden Markov models (HMMs).[14] Some other advanced
approaches use profile-profile alignments and HMMs in
combination with structural properties.
After template candidates are identified, the best structures
must be selected. Sequence similarity level of the template se-
quence in relative to the target sequence is important in gen-
erating 3D structures with high accuracy. However, sequence
similarity is not the only factor that determines the accuracy of
the structures generated in homology modeling. Regarding the
minimum sequence similarity limit in homology modeling,
there are ambiguities about the exact value but >25% suggests
that the template and target will take similar 3D structures.[15]
Apart from high sequence similarity, various factors are
considered in choosing an eligible template. These factors
include phylogenetic similarity between template and target
sequences. Templates from identical or analogous phyloge-
netic tree to the target sequence may result in a 3D structure
with high accuracy.[16] The other factors are environmental
factors such as pH, solvent type, and existence of bound li-
gand. These are also important in choosing the most eligible
template as it has a role in ensuring the most optimal condi-
tions in building an accurate target structure. The resolution
of the experimental structure under consideration is also a
factor in choosing the eligible template.[17]
2.2
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Sequence Alignments And
Alignment Correction
After the most appropriate alignments are selected, align-
ments and correction of them in case it is necessary are
undertaken. The alignments are target–template and tem-
plate–template when more than one template is used. The
error in the alignment of a residue causes shifting of α carbon.
A single residue gap in an α helix section triggers rotation of
the rest of the residues in the helix. As a result, the alignment
of sequences in the right way is crucial in homology mod-
eling.[18] Careful checkups and correction while performing
alignments may enhance building 3D protein structures with
high quality. The most widely used alignment methods are

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clustalw (http://www.genome.jp/tools-bin/clustalw),[19] t-
coffee (http://tcoffee.crg.cat/),[20] 3dcoffee (http://phylog-
eny.lirmm.fr/phylo_cgi/),[21] and muscle (https://www.ebi.
ac.uk/Tools/msa/muscle/).[22]
2.3
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Model Building
Various methods are used to generate 3D models for the
target sequence based on its templates. Model building ap-
proaches can be classified as rigid- body assembly methods,
segment matching methods, spatial restraint methods, and
artificial evolution methods.
In rigid- body assembly, the protein structure is broken
down into basic conserved core regions, loops, and side
chains. This approach depends on the natural dissection that
enables the building of a protein 3D structure by bringing
these rigid bodies together which are picked up from the
aligned template protein structures.[23] This can be done by
tools like 3d-jigsaw,[24] builder,[25] and swiss-model.[26]
In segment matching method, a cluster of atomic posi-
tions obtained from the template structures are used as lead-
ing positions. Selection of segments from known structures
in a database for matching the segments is done based on
the sequence identity, geometry, and energy. Then, the entire
FIGURE 1 Steps in homology
modeling [Colour figure can be viewed at
wileyonlinelibrary.com]

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MUHAMMED AnD AKI- YALCIn
atom model is generated by using the leading structure as a
pillar to lay the segments. This can be done by using segmod/
encad.[27]
Spatial restraint method builds the model by meeting
restraints came from the template structure. The restraints
are framed onto the target structure depending on the align-
ment. These restraints are determined by stereochemical
restraints on bond length, bond angle, dihedral angles, and
van der Waals contact distances. This can be performed with
modeller.[28]
Artificial evolution method uses rigid- body assembly
method and stepwise template evolutionary mutations to-
gether until the template sequence is the same as the target
sequence. This can be performed with nest.[29]
Table 1 displays summary of general features of the pop-
ular tools and servers that can be used for model building.
Researchers reported that when the sequence identity is
high, the homology models derived from different packages
are comparable to each other. When the sequence identity
is lower, the results tend to vary, with some packages per-
forming noticeably better than others.[30] The quality of the
models is related to the performance of packages in sequence
alignment and model building. modeller is found to be one
of the best tools in homology modeling.[31] In addition to this,
critical assessment of methods of protein structure prediction
(CASP) assesses modeling methods in a number of different
categories. i-tasser was ranked as the best server for protein
structure prediction in recent CASP experiments.[32] These
TABLE 1 Popular homology modeling tools and servers
Homology modeling
tools or servers URL address Short description
modeller http://www.salilab.org/modeller/ Is a homology modeling tool that generates protein 3D structures
with spatial restraints method. It is available freely, has powerful
features, and gives reliable results[33]
i-tasser https://zhanglab.ccmb.med.umich.
edu/I-TASSER/
Is a server that provides an Internet- based service for protein
structure prediction. It was found to be one of the best methods in
the servers section of CASP experiments[26]
swiss-model http://swissmodel.expasy.org/ Is a server that gives protein 3D structure from its amino acid
sequence. It provides user- friendly Web interface. This server uses
model quality estimation to select the most appropriate templates
and gives the expected accuracy of the models built
approximately[34]
Molecular Operating
Environment (moe)
https://www.chemcomp.com/MOE-
Molecular_Operating_Environment.htm
Is a combination of segment matching and modeling of insertion or
deletion regions approaches. In addition to 3D structure prediction,
it has advanced loop modeling, advanced alignment methods, and
powerful alignment visualizer and editor[35]
phyre2http://www.sbg.bio.ic.ac.uk/phyre2/html/
page.cgi?id=index
This modeling uses various detection tools to generate 3D structures.
It has special features like ligand binding prediction and variant
analysis among the protein amino acid sequence[36]
hhpred http://toolkit.tuebingen.mpg.de/hhpred This tool builds 3D structures using pairwise comparison of profile
hidden Markov models (HMMs) from a single or multiple query
sequence[37]
robetta http://www.robetta.org/ Based on the ROSETTA fragment insertion method, it gives both ab
initio and homolog models of protein regions[38]
Protein Model Portal
(pmp)
http://www.proteinmodelportal.org/ PMP provides interactive interface for model building and quality
assessment[39]
icm https://www.molsoft.com/homology.html Is one of the homology modeling tools that give 3D structure with
good accuracy. Its features include fast model building, loop
prediction, model validation, and refinement[40]
prime https://www.schrodinger.com/prime Is a powerful package for accurate protein structure prediction. In
addition to building structures with high accuracy, it provides
advanced simulation. It makes homology modeling and fold
recognition merge into a package. It has an easy- to- use interface[41]
scwrl4http://dunbrack.fccc.edu/scwrl4/index.php It is a tool that is rapid with good accuracy and easy to use[42]
intfold http://www.reading.ac.uk/bioinf/IntFOLD/ It is an independent server that predicts intrinsic disorders, domains,
and protein–ligand binding sites[43]

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tools and servers have their own pros and cons. As a result,
there is no single modeling tool or server which is superior in
every aspect to others.
2.4
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Loop Modeling
Gaps or insertions called loops are present in sequences of
homologous proteins. The structures of loops are not con-
served during evolution. Even without deletions or inser-
tions, different loop conformations in query and template
are often found. The specificity of the function of a protein
structure is often determined by the loops. Accuracy of loop
modeling is an important factor which determines the value
of the generated models for further applications. Since loops
show higher structural variability than strands and helices,
the prediction of their structure is more difficult than strands
and helices.[44]
There are two important methods that are used in de-
veloping the loops. One is database search approach and
the other is conformational search approach. The database
search method browses all the known protein structures to
detect segments providing the critical core regions. The con-
formational search approach depends on a scoring function
optimization.[8] Loop searches are done for loops of length
4–7 residues these days. This is because the conformational
variation increases as the length of the loop increases.
To deal with these drawbacks, de novo methods that are used
for loop conformation predictions by looking for conforma-
tional space have been developed. Monte Carlo simulations,
simulated annealing, genetic algorithms and molecular dy-
namics simulations often in combination with knowledge-
based potentials are examples for this. In such methods, the
length of loop that can be modeled is not limited but as the
length increases possible conformation number increases
rapidly which makes the modeling very time- consuming.[45]
There are servers such as archpred (http://www.bioinsilico.
org/ARCHPRED/)[46] and congen (http://www.congenom-
ics.com/congen/doc/)[6,47] that are used in loop modeling.
2.5
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Side- Chain Modeling
Side- chain modeling is usually done by putting side chains
onto the backbone co- ordinates that are derived from a par-
ent structure and/or from ab initio modeling simulations.
In practice, side- chain prediction works at high levels of
sequence identity. Protein side chains are present in a lim-
ited number of structures with low energy known as rota-
mers. Depending on defined energy functions and search
strategies, rotamers are selected in accordance with the
preferred protein sequence and the given backbone co-
ordinates. The accuracy of prediction is usually high for
the hydrophobic core residues but low for water- exposed
residues on the surface.[48] Tools like ramp (http://www.
ram.org/computing/ramp/)[41] and scwrl[49] can be used in
side- chain modeling.
2.6
|
Model Optimization
Optimization of the model usually begins with an energy
minimization utilizing molecular mechanics force fields.[50]
At each energy minimization, a few big errors are eliminated
but many other small errors are introduced at the same time
and start accumulating. Therefore, restraining the atom posi-
tions, implementing energy minimization with a few hundred
steps, and using more precise force fields like quantum force
fields[51] and self- parameterizing force fields[52] can be uti-
lized to decrease the errors in model optimization. For further
model optimization, methods such as molecular dynamics
and Monte Carlo can be used.[53]
2.7
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Model Validation
Accuracy of the constructed model can determine its further
application in various areas. Thus, verification and validation
of models are necessary. Depending on sequence similarity,
environmental parameters, and the quality of the templates,
the generated models have different accuracy.
Analysis of the stereochemistry of the model is one
basic requirement. This analysis is done with parameters
such as bond length, torsion angle, and rotational angle.
whatcheck (https://swift.cmbi.umcn.nl/gv/whatcheck/),[54]
procheck (https://www.ebi.ac.uk/thornton-srv/software/
PROCHECK/),[55] and molprobity (http://molprobity.bio-
chem.duke.edu/)[56] are popular tools used for the deter-
mination of the stereochemistry of the model in homology
modeling. Ramachandran plot (http://mordred.bioc.cam.
ac.uk/~rapper/rampage.php) is also a powerful determinant
of the quality of protein structure. Residues with a problem of
stereochemistry will fall out of the acceptable regions of the
Ramachandran plot.[57]
There are also tools that focus on the determination of the
spatial features of the model based on 3D conformations and
mean force statistical potentials. verify3d (http://servicesn.
mbi.ucla.edu/Verify3d/)[58] and prosaii (https://www.came.
sbg.ac.at/prosa.php)[7] are examples for this. These tools con-
sider model construction environmental parameters in rela-
tive to the expected environmental conditions.
3
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APPLICATIONS OF
HOMOLOGY MODELING
Homology modeling has a vast range of applications, and
its importance is increasing as the number of structures de-
termined increases. It has applications in structure- based
drug design, analysis of mutations, insight into binding

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mechanisms, identification of active sites, looking for li-
gands and designing of novel ligands, modeling of sub-
strate specificity, protein–protein docking simulations,
molecular replacement in experimental structural refine-
ments, rationalizing of known experimental results and
planning of future computational experiments by using the
generated models.[59]
Homology modeling has many applications in drug dis-
covery process. This makes the drug discovery process faster,
easier, cheaper, and more practical. In general, homology
modeling applications in the drug discovery need high- quality
models. As a result, high sequence similarity, good side- chain
modeling, and loop modeling are crucial in determining fur-
ther applications of the model build in the drug discovery.
As an illustration of the case application, for example,
homology modeling was used to discover novel acetohy-
droxy acid synthase (AHAS, EC 2.2.1.6) inhibitors against
Mycobacterium tuberculosis. Several studies demonstrated
that the plant AHAS inhibitors of sulfonylurea chemicals
such as sulfometuron methyl (SMM) exhibit antituberculosis
activity. However, the 3D structure of M. tuberculosis AHAS
remains to be elucidated. Thus, homology modeling was
performed based on the Saccharomyces cerevisiae AHAS
to build a 3D structure of M. tuberculosis AHAS. Through
docking simulation and similarity searches, 23 novel AHAS
inhibitors of Escherichia coli AHAS II enzymatic activity
were identified. Five of the identified chemicals showed
strong inhibitory effects against multidrug- resistant and
extensively drug- resistant strains. Three of the compounds
exhibited more activity than the positive control SMM.[60]
In recent years, 3D structure of targets in cancer that can
be used for discovering effective chemotherapeutic agents
has been generated using homology modeling.[61,62] Reliable
3D structures of G- protein- coupled receptors (GPCRs) which
are targets of nearly a third of FDA- approved drugs have been
built similarly.[63] Another recent application of homology
modeling is 3D structure determination of RNA polymerase
of the Ebola virus that helps in the detection of potential ther-
apeutic agents.[64] Furthermore, 3D structure of NS5 protein
of the Zika virus has been determined by homology model-
ing that leads to the discovery of its potential inhibitors.[65]
Recent case applications of homology modeling in drug dis-
covery are summarized in Table 2.
4
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OPPORTUNITIES AND
POSSIBLE CHALLENGES IN
HOMOLOGY MODELING
The number of high- quality protein 3D structures has in-
creased in the last decades. The introduction of new experi-
mental methods like cryo- electron microscopy (Cryo- EM) is
anticipated to increase the number of 3D structures determined
experimentally.[80] As the experimentally determined num-
ber of high- quality 3D protein structures of protein families
increases, the role of homology modeling in determining the
3D structures of the rest of the sequences in these families in-
creases. However, 3D structures of all protein distinct folds in
nature have not been completed yet. As a result, there are some
difficulties in building 3D structures of proteins in which the
structures of their protein families have not been determined.[18]
There are dozens of methods used for model building in
homology modeling. New methods with new algorithms have
been developed. Various studies have demonstrated that there
is no single modeling program or server which is superior in
every properties to others.[81] So, selecting the method/s to
be used according to the protein in hand and specific aim of
future applications of the model is important.
In classical homology modeling, the model is built mainly
based on sequence similarity. In the experimental structure de-
termination, ligands are absent as they are often lost during the
purification process. Thus, the resulting models that are built
without considering the ligand information in the template
represent an unliganded state. This shortcoming has been dealt
with the introduction of ligand- sensitive approaches. However,
such approaches need expertise and manual interventions that
take time. Hence, the introduction of fully automated homology
modeling tools that can deal with such problems is an import-
ant issue.[82] Furthermore, there are efforts to integrate it with
postmodeling applications. For instance, there are works to
integrate modeling tools with thermostabilizing mutations.[83]
Homology modeling may leave some unresolved ques-
tions in the computational models. This can be reduced by
using models that came from more experimentally deter-
mined structures which allow better conceivable templates for
targets. As consistent, accurate, and progressive methods for
the improvement of models by shifting the co- ordinates par-
allel to the native state are developed, coverage increases.[84]
Another limitation of homology modeling is the presence of
loops and inserts as it is difficult to model them without template
data.[85] In order to have a model with high accuracy, optimiza-
tion of the loop region and side chains is important. Optimization
encompasses refinement of the generated models with molecular
dynamics simulations. In case there is low sequence similarity
level between target and template, using multiple templates is
advantageous. But using multiple templates may lead to aberra-
tions in the alignment unless templates which are from identical
or analogous phylogenetic tree are used as the target sequence.[86]
Using psi-blast algorithm instead of normal blast may provide
optimal template selections in evolutionary distant cases.
At the end of the homology modeling process, many mod-
els of a target are built in general. Having many models is
an opportunity, but identification of the best model needs
further investigation. In order to identify the best model, the
constructed models are compared using various parameters,
discrete optimized protein energy (DOPE) score,[87] template

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MUHAMMED AnD AKI- YALCIn
modeling (TM) score,[88] and RMSD value[89] are used for
comparison. The determinant parameter is decided depend-
ing on the purpose of modeling results.
5
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CONCLUSIONS
The gap between protein sequences available and protein 3D
structures determined experimentally is growing. Homology
modeling aims at building 3D structure of proteins from their
sequences by using templates with an accuracy which is simi-
lar to the experimental methods. Thus, it has a big role in
filling the widening gap.
In recent years, there are many advances in the tools and
servers of homology modeling that improve the accuracy of
modeling results. This has an impact on each step of homol-
ogy modeling. Better alignment methods, loop modeling,
side- chain modeling, and validation techniques have been
introduced. As the accuracy of models generated increases,
their application in the drug discovery process also increases.
So, homology modeling contributes much in the drug discov-
ery. Furthermore, in the near future, integration of homology
modeling with other computer- aided drug design methods
and postmodeling applications is expected.
Homology modeling is used in determining 3D structures
of proteins, and it has many applications in the drug discov-
ery process.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest in this work.
AUTHOR INFORMATION
Muhammed Tilahun Muhammed (born in Motta - Ethiopia)
is an Ethiopian pharmacist and lecturer in Suleyman Demirel
TABLE 2 Recent applications of homology modeling
Protein (molecule) Application Program/server
Human angiotensin II type I receptor[66] Guide for designing novel therapeutic agents as
angiotensin receptor antagonists
blast, clustalw, sybyl, modeller,
i-tasser, procheck, surflexdock
HSP70 from GWD[67] Determination of 3D structure of hsp70 chaperone
protein which is a target of new wide spectrum
cancer therapeutics candidates
blast, swiss-model, qmean, psvs
DNA- dependent protein kinase
(DNA- PK)[68]
Screening of potential candidates as DNA- PK
inhibitors
whatif, prosa, autodock vina,
discovery studio, clustalw, phyre2,
whatcheck
Human concentrative nucleoside
transporter 3 (hCNT3)[69]
Identification of sodium binding site and the
determinant residues of nucleoside selectivity
moe, gold, glide, charmm
Peroxisome proliferator- activated receptor
gamma (PPARγ)[70]
Identification of new ligand molecules that reduce
PPARγ receptor in Type 2 diabetes complications
prime, glide xp, schrodinger
GABA transporter 1 (GAT1)[71] Discovering GAT1 inhibitor molecules that are
potential anticonvulsant and antidepressant agents
clustalw, prime, glide xp, schrodinger
α- Glucosidase[72] Designing of new classes of α- glucosidase inhibitors blast, prime, procheck, sitemap, glide
xp, schrodinger, maestro
CD20 antigen[73] Insight into the structure of CD20 antigen which is a
target in developing new monoclonal antibodies
psi-blast, t-coffee, swiss-model,
i-tasser, phyre2, muster, rampage
Histamine H2 receptor[74] New perspectives into the development of a new
potent drug against peptic ulcer by targeting H2
blast, clustalx, modeller, procheck,
autodock, string
Protein kinase D 1 (PKD1)[75] Designing new PKD1 inhibitors schrodinger suit, maestro, modeller,
pdbsum, glide xp
Ribonucleotide reductase[76] Screening of novel drugs for drug- resistant leprosy
therapy
swiss-model, hhpred, profunc, errat,
whatif, prosa, glide xp, schrodinger
Ecto- nucleoside triphosphate diphospho-
hydrolases (E- NTPDases)[77]
Structural insights into the binding of E- NTPDases to
substrates and inhibitors
moe, blast, rampage, marvin sketch,
flex-x, gromacs
Parkinson’s linked mutant leucine- rich
repeat kinase 2 (LRRK2)[78]
Identification of multiple novel points within neuronal
death signaling pathways that could be targeted by
potential therapeutic candidates
moe, glide 1, maestro, charmm
Alpha- galactosidase A (α- Gal A)[79] Detection of six GLA variants that cause α- Gal A
activity deficiency and protein wild- type structure
loss
alamut visual, swiss model, pymol

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MUHAMMED AnD AKI- YALCIn
University, Faculty of Pharmacy. He graduated from Ankara
University, Faculty of Pharmacy, with bachelor degree in
2012. He graduated from Middle East Technical University
Department of Biomedical Engineering with a master of sci-
ence in 2015. He is a Ph.D. candidate in Ankara University,
Institute of Biotechnology. He is working on homology mod-
eling, computer-aided drug design, and organic synthesis.
ORCID
Muhammed Tilahun Muhammed http://orcid.
org/0000-0003-0050-5271
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How to cite this article: Muhammed MT,
Aki-Yalcin E. Homology modeling in drug discovery:
Overview, current applications, and future perspectives.
Chem Biol Drug Des. 2019;93:12–20. https://doi.
org/10.1111/cbdd.13388