Computational Method for Drug Target Search
and Application in Drug Discovery
Yuzong Chen , Zerong Li , and C. Y. Ung
Abstract Ligand-protein inverse docking has recently been
introduced as a computer method for identification of potential
protein targets of a drug. A protein structure database is
searched to find proteins to which a drug can bind or weakly
bind. Examples of potential applications of this method in
facilitating drug discovery include: (1) identification of unknown
and secondary therapeutic targets of a drug, (2) prediction of
potential toxicity and side effect of an investigative drug, and (3)
probing molecular mechanism of bioactive herbal compounds
such as those extracted from plants used in traditional medicines.
This method and recent results on its applications in solving
various drug discovery problems are reviewed.
Index Terms—Drug binding, computer-aided drug design,
ligand-protein interactions, molecular modeling, adverse drug
reactions, therapeutic effects, medicinal plant drug, natural
product.
K
I. INTRODUCTION
nowledge of protein targets of drugs is useful in solving
various problems in drug discovery process 1,2. For
instance, therapeutic effects of a drug generally result
from its interaction with one or more proteins or nucleic acids
critical in a disease process 1,2. These proteins and nucleic
acids are called therapeutic targets. The therapeutic targets of
a number of clinical drugs are still unknown. Some drugs,
such as aspirin, are known to have multiple therapeutic
effects. For many of these drugs, not all of the targets
associated with the respective multiple effects are determined.
Lack of a complete knowledge about these targets hinders the
effort to fully explore the novel mechanism of these
therapeutic agents in the design of new drugs.
The adverse reaction of a drug with human body is often
induced by its interaction with some of the proteins critically
important in the normal processes 3. Identification of the
interaction with these proteins, known as toxicity and side
effect targets, has potential application in facilitating the
prediction of side effect and toxicity of an investigative drug
in early stage of drug development. A substantial portion of
the $350 million and 12 years spent on average for
commercial drugs have been squandered on many drug
candidates that failed to ever reach the market 4, 5. Given a
low success rate of drug candidates, detection of potential
toxicity and side effect in early stages of drug development
can potentially save money and time by focusing resources on
drug leads and candidates likely to be safe to patients.
Manuscript received March 31,2002 (e-mail: yzchen@cz3.nus.edu.sg).
Drug target identification also finds potential application in
facilitating the study of molecular mechanism and
pharmacology of medicinal plants. Herbal medicines have
gained increasing popularity
6 and they have been
extensively used in traditional medicines 7. While herbal
medicines are commonly used and the chemical constituents
of a large number of medicinal herbs have been extracted and
analyzed 8, the molecular mechanism as well as the basic and
clinical pharmacology is known for only a relatively small
number of medicinal herbs 9. Lack of knowledge about
mechanism and pharmacology is one of the main obstacles for
more extensive clinical application of medicinal herbs
particularly those from traditional medicines. It also hinders
efforts for new drug discovery based on novel therapeutic
approaches of herbal medicines. Given the accumulated
knowledge about their chemical constituents, the mechanism
and pharmacology of medicinal herbs may be derived from
investigation of the protein targets of these constituents and
their collective actions.
Information about protein targets of drugs have traditionally
been generated experimentally. Thus far, only one computer
method, ligand-protein inverse docking, has been introduced
10, 11. This inverse docking strategy requires a sufficient
number of proteins of known 3D structure. At present there
are 13,976 protein entries in Protein Data Bank (PDB) and the
number increases at a rate of well over 100 per month 12.
About 17% of these have unique sequence 13. Introduction of
high-throughput methods is expected to enable structural
determination of 10,000 proteins with unique sequence within
5 years 14. Thus the number of proteins is approaching a
meaningful level to cover a diverse set of potential targets.
Ligand-protein inverse docking involves a computerautomated search of a protein database to identify potential
proteins into which a small molecule can be docked 10. The
software, INVDOCK, has been developed for such a purpose
10. INVDOCK uses a flexible docking algorithm to attempt to
dock a molecule to each cavity of each of proteins in a
database. A method for automated identification of binding
cavities and the development of cavity models and related
database have also been developed along with INVDOCK 10.
Several studies have been conducted for testing the
performance of the INVDOCK in identification of therapeutic
and toxicity targets of clinical drugs and natural products from
medicinal plants 10, 11, 15. These developments are reviewed
in this paper.
II. COMPUTATIONAL METHOD
A. Ligand-protein inverse docking procedure
All small molecule drugs appear to bind to a cavity of a
protein or nucleic acid target. Thus, to facilitate computerautomated inverse docking, a protein cavity database has been
developed along with INVDOCK. This database is composed
of models of individual cavities in each protein entry of PDB.
The model of a cavity is a cluster of overlapping spheres that
fill-up that cavity 10.
INVDOCK is designed for automated search of every entry
of this protein cavity database to find potential protein targets
of a small molecule 10. Because of large number of cavity
entries, cross-the-board specification of possible binding sites
within each cavity is difficult. Hence all parts of each cavity
are subject to docking. Docking to sites inside each cavity
starts from known ligand binding sites, followed by more
interior sections and then remaining part. To save CPU time,
the program proceeds to the next protein entry when first
successful dock is obtained without exhaustive search for
optimum binding mode within each cavity. Although the
optimum binding-mode is not specifically sort, the prioritized
search strategy seems to dock a ligand to a site reasonably
close to the experimentally observed positions in most of the
ligand-protein structures studied. All cavity entries are subject
to search unless the related protein has been identified as a
putative target.
A molecule is flexibly docked into a cavity by an approach
similar to the multi-step strategy approach proposed by Wang
et al 16. Ligand conformation is sampled at a resolution
similar to that of Wang et al. Docking of a particular
conformer to a cavity is by the following steps: First the
ligand is aligned within the selected site by matching the
position of each ligand atom with the center of spheres.
Because of the relatively low-resolution nature of ligand
conformation sampling, certain degree of structural clash is
allowed at this stage. A molecular-mechanics conformation
optimization is then conducted by a limited torsion space
sampling of rotatable bonds in the ligand and those in the
side-chain of the receptor amino acid residues at the binding
site. Each rotatable bond is sampled in the range of ±15o. This
is followed by 50 iterations of Cartesian coordinate energy
minimization on all ligand and protein atoms at the binding
site so as to further optimize the ligand-protein complex.
Energy minimization is by a steepest decent method.
In both torsion optimization and energy minimization,
AMBER force fields 17 are used for covalent bond, bond
angle, torsion, and non-bonded Van der Waals and
electrostatic interactions. A large number of crystal structures
in PDB contain only non-hydrogen atoms. To save computing
time and to avoid the difficulty in modeling hydrogens, Morse
potential 18, which is a function of donor-acceptor distance, is
used to represent hydrogen bond terms. This potential has
been shown to give reasonable description of hydrogen bond
energy and dynamics in biomolecules 19, 20. The energy
function is:
V = 1/2 Sbonds Kr (R - Req)2 + 1/2 Sangles Kq (q - qeq)2 +
1/2 Storsions Vn [ 1 - cos(n(f-feq)) ] +
SH bonds [ V0 (1-e-a(r-r0) )2 - V0 ] +
Snon bonded [ Aij/rij12 - Bij/rij6 + qiqj /er rij]
In this function, R, q, and f denotes bond length, angle and
torsion angle respectively; Req , qeq and feq are taken as
equilibrium bond length, angle and torsion angle respectively
and their values are from the original PDB structure and the
structure of the drug respectively; Kr and Kq are covalent and
bond angle bending force constant respectively; Vn and n are
torsion parameters; r is hydrogen bond donor-acceptor
distance, and V0, a and r0 are hydrogen bond potential
parameters.
B. Scoring
Scoring of docked molecules is based on a ligand-protein
interaction energy function DELP composed of the same
hydrogen bond and non-bonded terms as those used for
structure optimization 10. Analysis of a large number of PDB
ligand-protein complexes shows that the computed DELP is
generally below DEThreshold = -aN kcal/mol, where N is the
number of ligand atoms and a is a constant ~1.0 10. The exact
value of a can be determined by fitting the linear equation
DEThreshold = -aN to the computed DELP for a large set of
PDB structures. This statistically derived energy value can be
used empirically as a threshold for screening likely binders. A
polynomial form of DEThreshold involving more parameters
may also be introduced to derive an energy threshold. DELP
can be required to be lower than DEThreshold when selecting
successfully docked structures.
Ligand binding is competitive in nature. A drug is less
likely to be effective if it binds to its receptor noncompetitively against natural ligands and, to some extent,
other drugs that bind to the same receptor site. This binding
competitiveness may be partially taken into consideration for
cavities known as ligand-bound in at least one PDB entry.
Ligands in PDB structures are known binders. Therefore PDB
ligands bound to the same receptor site as that of a docked
molecule may thus be considered as "competitors" of that
molecule. In INVDOCK selection of a putative protein target,
the computed DELP is not only evaluated against
DEThreshold but also compared to the ligand-protein
interaction energy of the corresponding PDB ligands that bind
to in the same cavity in this or other relevant PDB entries.
The Ligand-protein interaction energy for the relevant PDB
structures is computed by the same energy functions as that
for the docked molecule. In addition to the condition that it be
lower than DEthreshold, DELP of a docked molecule is
required to be lower than a "competitor" energy threshold
DECompetitor when selecting a putative target.
DECompetitor can be taken as highest ligand-protein
interaction energy of the corresponding PDB ligands
multiplied by a factor b. In order to be able to find weak
binders as well as strong binders, a factor b£1 is introduced to
scale the ligand-protein interaction energy of PDB ligands.
This is because a weak binder may have slightly higher
interaction energy than that of a PDB binder. No experimental
data has been found to determine the value of b. Hence b has
been tentatively determined by an analysis of the computed
energy for a number of compounds. A value of 0.8 has been
suggested for b leads to reasonable results statistically 10.
III. TESTING OF INVDOCK DOCKING ALGORITHM
The docking algorithm in INVDOCK has been tested on
nine ligand-protein complexes from the PDB 10. Most of
these structures have been used in testing different docking
programs 16, 21, 22. The rest are complexes of well-known
therapeutic agents. Therefore selection of these structures is
useful in evaluating INVDOCK. Table 1 gives the computed
root mean square deviation (RMSD) of each of the docked
ligand with respect to the corresponding ligand in the original
PDB structure. Six of the ligands are docked into the binding
site with a RMSD in the range of 0.80 to 2.05Å, which is
comparable to those obtained in other docking studies 16, 21,
22.
Although docked to the same position as that in the
corresponding crystal structure, the RMSD of the three of the
tested ligands is found to be substantially larger than obtained
by other studies. Each of these docked molecules is found to
largely overlap with the crystal ligand. However they are
either flipped or with one end oriented in a different direction
with respect to that in the crystal structure. The reason for
such a deviation in the orientation of docked molecule is
because INVDOCK does not attempt an exhaustive search for
optimum binding mode in a cavity. As a result, when docked
into a location with a similar steric contact as that of a PDB
ligand, the ligand-protein interaction energy of a molecule
may well pass the scoring threshold. This problem may be
resolved by the introduction of the search of optimum binding
mode and refinement of scoring function in the INVDOCK
algorithm. Non-the-less, our results seem to suggest that
INVDOCK algorithm is capable of docking a molecule to a
site reasonably close to the original binding location.
Docking quality has been shown to improve by an
extended INVDOCK algorithm that includes the search of
optimum binding mode. As shown in Table I, the RMSD of
the three ligands, with a larger RMSD in the original
INVDOCK run, is significantly improved from the range of
3.56 - 6.55 Å in the original INVDOCK computation to that
of 0.97 - 2.41 Å in the new computation. The same
computation on the other six ligands shows no significant
change between RMSD of optimum binding mode and that
from the original INVDOCK computation. However, from
Table I, the ligand-protein interaction energy of all the ligands
in the optimum binding mode is substantially decreased with
respect to that derived from original INVDOCK computation.
In most cases, this energy is closer to that in the original PDB
structure. This seems to indicate that INVDOCK optimization
and scoring scheme is capable of guiding the system to a
configuration fairly close to the native state.
IV. INVDOCK PREDICTION OF DRUG TARGETS
The effectiveness of INVDOCK prediction of drug targets
can be demonstrated from a recent study on a few clinical
agents 10. In this paper the results of one drug, tomoxifen, is
presented. Tamoxifen is an anticancer drug widely used for
treatment of breast cancer 23 and it has been approved as the
first cancer preventive drug. Tamoxifen metabolite 4Htamoxifen is believed to be the major contributor to the antioestrogenic effects of tamoxifen inside human body 23.
Potential human and mammalian protein targets of 4Htamoxifen identified by INVDOCK are given in Table 2 along
with the respective clinical implications from experiments. A
number of known protein targets of tamoxifen are found in the
Table. These include estrogen receptor23, protein kinase C 24,
collagenase 25, 17b hydroxysteroid dehydrogenase 26,
Alcohol dehydrogenase 27, and prostaglandin synthetase 28.
It has been observed that two other INVDOCK identified
proteins glutathione transferase and 3a hydroxysteroid
dehydrogenase exhibited altered activity by tamoxifen 29, 30,
which may be indicative of direct binding of tamoxifen to
these proteins. Experiments showed that the level of another
two identified proteins dihydrofolate reductase and
immunoglobulin is changed by tamoxifen 31, 32. Ligand
binding is known to self-regulate protein level in certain cases
33. It remains to be seen whether these two proteins are also
the target tamoxifen as implicated by INVDOCK search.
It is noted that known target of tamoxifen such as
calmodulin 24 is not identified by INVDOCK. One possible
reason might be that the available PDB structures of
calmodulin are not sufficiently close to tamoxifen-bound
conformation. None of these PDB structures is bound by a
ligand similar in structure to tamoxifen. The conformation of
calmodulin is known to change significantly by binding of
ligands 34. Because of the intrinsic flexibility of this protein,
it is highly likely that ligand binding to this protein involves
induced-fit. Our analysis of two PDB structures of calmodulin
bound by a different ligand (PDB id: 1a29 and 2bbm) shows
that the conformation of this protein is dependent on its
binding ligand. In a recent molecular docking study, a ligand
was docked into calmodulin by consideration of conformation
changes that mimic an induced fit 35. We also finds that 4Htamoxifen can be placed into calmodulin with slightly less
favorable steric interaction than allowed by the INVDOCK
scoring function. An appropriate conformation change in
calmodulin might allow for the removal of this un-favorable
interaction.
Limited number of protein entries available in our cavity
database is also expected to result in missed hit. For instance,
known tamoxifen metabolizing protein cytochrome P450 36 is
not identified in this work because no corresponding human or
mammalian entry is available in the cavity database. A search
of bacterial proteins in the database identified this protein
(PDB Id: 5cp4 and 1cpt) as a putative target.
As shown in Table 2, apart from the 10 putative protein
targets that have been implicated or confirmed experimentally,
INVDOCK identified 10 other proteins as putative targets of
4H-tamoxifen. These include aldose reductase, Arginase,
carbonic anhydrase, macrophage migration inhibitory factor,
purine nucleoside phosphorylase, DNA polymerase b,
hypoxanthine-guanine phosphoribosyltransferase, retinoic
acid oxidase, sepiapterin reductase, and tyrosine 3monooxygenase. No literature has been found to link
tamoxifen to each of these proteins. There is also no report
that indicates each of these proteins is not a target of
tamoxifen or its analogs. Further investigation is therefore
needed to determine whether or not 4H-tamoxifen can bind to
these proteins.
V. INVDOCK PREDCITION OF TOXICITY AND SIDE
EFFECT TARGETS
INVDOCK has been used to predict the toxicity and side
effect targets of eight drugs including aspirin, gentamicin,
ibuprofen, indinavir, neomycin, penicillin G, 4H-tamoxifen,
and vitamin C. These drugs were selected because they have
been subjects of extensive investigation including the probing
of toxicity and side effect protein targets of these drugs.
However, these drugs were selected before the literature of the
toxicity or side effects was searched.
As shown in Table 3, there are a total of 68 protein targets
for the eight therapeutic agents that have been confirmed or
implicated by available experimental studies. The detailed
target list for each drug can be found in an earlier publication
11. INVDOCK predicted 38 of these. In addition, there are 29
INVDOCK predicted targets that lack experimental validation.
Because of a limited scope of experimental study of toxicity
targets of these drugs, it is not expected that all toxicity targets
have been determined experimentally. Thus it is difficult to
give a complete assessment about which of the predicted
targets are false and how many targets are missed without the
knowlgedge of all the targets of these drugs. The evaluation
given here should therefore be considered as a preliminary
assessment based on currently available experimental data.
It is noted that some of the known toxicity targets of these
drugs are not predicted by INVDOCK. Table 11 shows that
there are 30 such INVDOCK missed targets. Of these missed
targets, 22 are either without available ligand-bound structure
of human or mammalian origin, or whose structure is
incomplete (e.g. contains only an irrelevant section/domain of
the protein). As this work only searches ligand-bound
structures of human or mammalian origin, these 22 targets are
beyond the capability of our inverse docking algorithm. In
addition, there are 3 other INVDOCK missed targets to which
the binding drug is linked by a covalent bond. As our docking
algorithm and force field are designed for ligand-protein
binding involving intermolecular hydrogen bond and nonbonded interactions only, INVDOCK may not be suitable for
the prediction of targets involving a covalent bond. Therefore
the miss of these three targets is not unexpected. Non-the-less,
there are five INVDOCK missed targets whose structure is
available.
The rate of correct predictions from this study may be
estimated from the ratio between the number of predicted
targets and that of experimentally known targets. Given that
the number of INVDOCK predicted and experimentally
determined targets is 38 and 68 respectively, the rate of
"correct prediction" is 56%. It is noted, however, the relevant
3D structure of 22 of the experimental targets is unavailable.
Therefore these targets are outside the scope of INVDOCK
and should be excluded in a more appropriate estimate of rate
of correct prediction. This way the rate becomes 83%. If one
further excludes the 3 other targets that involve drug-target
covalent bonding, the rate is changed to 89%.
Likewise, the rate of missed targets can be estimated from
the ratio between the number of INVDOCK missed targets
and that of experimentally determined targets. This rate is 5%.
The rate of "false-positive" may also be estimated as the ratio
between the number of INVDOCK targets without
experimental validation and that of the experimental targets,
which is 63% when those experimental targets without
relevant structure are excluded. Because the number of
experimental targets is incomplete, the computed rate of
correct prediction and that of the "false-positive" here does
not add up to 1. This suggests that, without complete
knowledge of all toxicity targets, these computed rates might
not fully describe the quality of INVDOCK computations.
VI. INVDOCK PREDICTION OF THERAPEUTIC AND
TOXICITY TARGETS OF ACTIVE COMPOUNDS FROM
MEDICINAL PLANTS
INVDOCK has also been tested for its applicability in the
prediction of therapeutic and toxicity targets of active
compounds extracted from medicinal plants 15. One example
is catechin, also known as cyanidol, an active compound from
green tea. It has been shown to inhibit the growth of human
breast cancer cells 37 and prostate cancer cells 38 partly
because of its inhibition of cyclin-dependent kinases 39. The
antitumor activity of this compound may also arise from its
inhibition of tyrosine phosphorylation of PDGF beta- 40,
induction of apoptosis 41 and inhibition of matrix
metalloproteinases 42. Catechin exhibits anti-inflammatory as
well as cancer chemopreventive effects 43. Some of these
effects of catechin are in part from its inhibition of TNF-alpha
and NF-kappaB. This compound also have antiplaque and
hepatoprotective effects via reduction of membrane fluidity
44. It has been reported that this compound has antioxidative
action mediated by the activation of glutathione peroxidase
45.
INVDOCK search produced seventeen potential therapeutic
targets, which are given in Table 4. Seven of these targets
have been confirmed by experiments, which showed that
catechin inhibits each of them. These include cyclindependent kinase and FGF receptor 39, neutrophil collagenase
42, protein kinase C 46, CAMP-dependent protein kinase 47,
TNF-alpha and NF-kappaB p65 43. Inhibition of each of the
first five proteins has potential anticancer implication, binding
to the sixth protein may produce anti-inflammatory effect, and
the interaction with the seventh and eighth proteins may lead
to anti-inflammatory as well as cancer chemopreventive
effects.
Available experimental data also seems to implicate another
five of INVDOCK identified therapeutic targets. Catechin has
been found to be an activator of cathepsin B and insulin 43. It
is possible that activation of each protein is by direct binding
of catechin. Activation of cathepsin may procude anticancer
effect, while activation of insulin may help reducing glucose
level and thus have implication in diabetes treatment. Catechin
is known to inhibit both the ras-transformed cells and the
activation of p38 mitogen-activated protein kinase 48, which
may have implication in anticancer property. One possible
reason for these inhibitory effects are due to the binding of
catechin to ras p21 protein and MAP kinase p38 respectively
as predicted by INVDOCK. Catechin is also known to have
immuno-enhancing effect on T and B cell functions 49, which
may also result from binding of catechin to immnoglobulin
lambda light chain as predicted by INVDOCK. Further
investigation is needed to determine whether these proteins
are targets of catechin.
Moreover, INVDOCK identified four additional potential
therapeutic targets. These are aldose reductase, X11, ganylyl
cyclase, and C-AMP-dependent protein kinase. No
experimental information has been found to either implicate or
invalidate them. Hence, further study is needed to determine
whether these proteins are targets of catechin. The potential
therapeutic effect of the binding of catechin to each of these
proteins is diabetes treatment for the aldose reductase, anticlotting for X11, and anticancer for ganylyl cyclase and CAMP-dependent protein kinase respectively.
Some known therapeutic targets of catechin are not found
by our INVDOCK search. These include matrix
metalloproteinase-2, matrix metalloproteinase-9, matrix
metalloproteinase-12 and glutathione peroxidase. This occurs
because of a lack of relevant structures in the database. Our
database does not yet have 3D structure of matrix
metalloproteinase-2, matrix metalloproteinase-9 and matrix
metalloproteinase-12. Although the 3D structures of
glutathione peroxidase are available in the database, these are
ligand-free structures that may not be a suitable system for
accurate analysis of the binding of a compound that affects the
function of that protein. Thus these structures are not used in
INVDOCK study.
INVDOCK search also identifies seven potential toxicity
and side effect targets of catechin. One target, prostagladin H2
synthase 1, has been confirmed by experiments 50, 51.
Inhibition of this protein may induce gastrointestinal toxicity.
As to the other six potential toxicity targets, no experimental
data have been found to either implicate or invalidate them.
Therefore further study is needed to determine whether or not
they are targets of catechin. Two such potential targets are
carbonic anhydrase II and V. Binding to each protein may
potentially induce acidity disorder in red blood cells. Another
potential target is alcohol dehydrogenase. Liver toxicity may
arise from catechin binding to this protein. The fourth
potential target is epididymal retinoic acid-binding protein.
The potential effect of catechin on this protein is the possible
interference with retinoic acid transport, which may lead to
cancer and cardiovascular disorder. The fifth and sixth
potential targets are fructose-1, 6-bisphosphatase and fructose2, 6-bisphosphatase. Red blood cell disorder and thus anemia
may potentially be induced by catechin binding to each
protein.
VII. FACTORS AFFECTING THE QUALITY OF
INVDOCK
Overall, more than 50% of INVDOCK identified potential
therapeutic protein targets and more than 27% of identified
toxicity targets have been implicated or confirmed by
experiments 10, 11, 15. Several reasons might contribute to
the discrepancy between INVDOCK screening results and
experimental data. It is not expected that exhaustive
experiments have been done to determine all protein targets of
a drug. Thus, discrepancy might arise for those identified
proteins that lack relevant experimental information. Some of
the PDB structures may be of little relevance to binding study
for a particular molecule. These include entries containing an
incomplete section or a chain, protein mutants that are
structurally different from the corresponding proteins
investigated in experiments, ligand-bound proteins whose
conformation is relevant only to a specific set of ligands, and
macromolecular complexes unrelated to a particular biological
process studied experimentally. Docking of a molecule to such
an irrelevant structure may thus generate a false hit.
Anticipated rapid progress in structural genomics 14 is
expected to provide a more diverse set of relevant structures.
Knowledge from study of protein functions 52 also facilitates
the selection of relevant structures in determination of putative
protein targets related to a particular cellular or physiological
condition.
Another possible cause of discrepancy between
INVDOCK screening results and experimental data is a lack
of consideration of protein profiles such as gene expression
pattern and protein levels. Some experimental studies of
ligand-protein interactions are based on the investigation of
cell lines or other assays. Observation of molecular events
related to a particular ligand-protein interaction requires that
the protein under study be at a sufficient level in the system
being investigated. If such a level is not reached at a particular
setting, then the corresponding experiment is not useful in
probing the binding of a molecule to that protein. Proteins not
expressed or at too low levels in a particular biological
process are unlikely to be therapeutically meaningful targets.
Advance in proteomics is providing rapidly growing
information about the profiles of proteins inside cells 53.
Incorporation of this information into an inverse docking
procedure can enable the prediction of more relevant protein
targets.
Pharmacokinetic and metabolic profile of a molecule may
also need to be considered in an inverse docking procedure.
The action of a therapeutic molecule requires it to achieve an
adequate concentration in the fluid bathing the target tissue.
The concentration of a molecule is determined by its
pharmacokinetic and metabolic profile. Therefore information
about this profile is important in prediction of putative protein
targets that a small molecule can reach at sufficient
concentration. Development in pharmacokinetics and drug
metabolism 54 is providing more and more information in this
regard.
The capability of an inverse docking approach in
identifying putative protein targets of a small molecule is
constrained by the relatively limited number of available
protein 3D structures. This is particularly true for membranebound receptor proteins that are key therapeutic and toxicity
targets for a variety of diseases or symptoms. This problem
can be partially alleviated by structural information generated
from rapid progress in high-throughput X-ray crystallography
of protein structures and in the development of new structural
determination methods 14.
The quality of an inverse docking procedure also depends
on the algorithm and force fields used in docking and scoring.
Ideally, in an inverse docking procedure, optimum binding
mode should be searched in a cavity. Although more CPUtime demanding, such a search may become practical the overall inverse docking search-speed can be improved. For
instance, the search-speed of INVDOCK can be significantly
improved by distributing database search onto multiple
computers. Cavity models may also be further refined to
reduce the search space. CPU time saved from these and other
improvements may be used for searching optimum binding
modes. There has been progress in refining docking/scoring
algorithm and force fields particularly in the areas of protein
flexibility 55 as well as ligand flexibility 16, 21, 22.
Knowledge and new methods/force fields generated from
these studies can also be incorporated into an inverse docking
procedure.
In a ligand-protein docking study, putative ligands are
typically selected from the lowest energy docked structures.
On the other hand, in an inverse docking procedure, putative
protein targets are selected based on an energy threshold.
Docked structures with ligand-protein interaction energy
lower than that threshold are considered as putative targets.
However, this energy threshold is more difficult to define. A
stricter condition on the energy threshold can easily result in
missed hits. Likewise, a too relaxed energy threshold may lead
to the over production of false hits. The empirical formula for
the energy threshold used in this study seems to give
reasonable results for the two agents studied. However,
additional study is needed to more extensively evaluate and
further refine the energy threshold.
VIII. CONCLUSION
Ligand-protein inverse docking appears to be a useful
approach for computer-aided identification of potential protein
targets of a small molecule. Testing results have shown the
potential of this approach in facilitating drug target
identification, toxicity prediction, and the probing of
molecular mechanism of compounds extracted from medicinal
plants. Performance and applicability of ligand-protein inverse
docking program need to be further enhanced by refinement
of docking and scoring algorithms, development of more
efficient cavity models, and by incorporation of new
information from advances in structural genomics,
proteomics, protein function, pharmacokinetics and drug
metabolism. Further development of this inverse docking
approach may bring into reach applications such as
identification of unknown and secondary therapeutic targets of
drugs and investigative drugs, toxicity and side effect
prediction, and the study of molecular mechanism and
pharmacology of medicinal plants.
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