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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5419_Библиотеки_им_академика_М_И_Перельмана
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9
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Mining for Bioactive Molecules in Open Databases
Guillem Macip, Júlia Mestres-Truyol, Pol Garcia-Segura,
Bryan Saldivar-Espinoza, Santiago Garcia-Vallvé, and
Gerard Pujadas
Departament de Bioquímica i Biotecnologia, Carrer Marcel⋅lí Domingo 1, Universitat Rovira i Virgili,
Research group in Cheminformatics & Nutrition, 43007 Tarragona, Catalonia, Spain
9.1 Introduction
An important goal of drug-discovery researchers is to determine which molecules in
chemical compound databases have a specic bioactivity. This goal can be achieved
either by experimentally testing compound libraries to nd molecules that show the
desired bioactivity (a process known as high-throughput screening; HTS) [1, 2] or
by computationally predicting the bioactivity of interest in les containing the structure of hundreds of thousands, or even millions, of chemical compounds encoded in
the form of electronic archives (a process known as virtual screening; VS) [3, 4]. In
VS, the researcher does not need to physically have the chemical compounds since
their description in electronic format is sucient. The cost of VS is therefore much
lower than the cost of HTS, where the bioactivity of a large number of compounds
is measured experimentally.
Like all screening techniques, VS is sometimes compared to looking for a needle
in a haystack. However, using VS to nd molecules that exhibit the desired activity
is much easier than nding the needle since VS is a targeted search (unlike searching for a needle, where there is no privileged direction and all search directions are
equally possible). This targeted search can be guided by the characteristics of existing
drugs for the same target or by the structure of the macromolecular target itself.
Figure 9.1 shows an example of a typical VS workow that is frequently used
in our laboratory [5–7] (other VS pipelines may also be successfully employed
[8, 9]). This VS workow consists of several sequential lters (i.e. ADMET/PAINS,
protein–ligand docking, pharmacophore, and shape/electrostatic) where the output
molecules of one lter are the input molecules of the next lter, and so on. The
inverted pyramid shape in Figure 9.1 also shows that, as the VS progresses, the
number of available molecules decreases since molecules that do not pass a lter are
discarded and not evaluated by the remaining lters. If the VS is designed correctly,
it is expected that, as the molecules pass through the various lters, the resulting
271
Open Access Databases and Datasets for Drug Discovery, First Edition.
Edited by Antoine Daina, Michael Przewosny, and Vincent Zoete.
© 2024 WILEY-VCH GmbH. Published 2024 by WILEY-VCH GmbH.
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Figure 9.1 Overview of a typical virtual
INITIAL DATABASE
ADMET/PAINS FILTER
PROTEIN–LIGAND DOCKING
PHARMACOPHORE FILTER
SHAPE/ELECTROSTATIC
FILTER
VS HITS
screening (VS) workflow previously used in our
laboratory. The shape of the funnel is related to
the number of molecules evaluated at each
stage of the VS. Since these are elimination
stages in which compounds that do not pass a
filter are discarded and therefore not evaluated
by the subsequent filters, the number of
available molecules becomes smaller and
smaller.
molecular sample will be enriched in compounds with the desired bioactivity. In
other words, the probability that a randomly chosen molecule is an active one must
be lower in the starting database than at the end of the VS.
In this chapter, we describe the main methods used during a VS, determine which
open databases are useful during this process, and explain how to validate the accuracy of the bioactivity predictions made by the VS. All the resources mentioned
in this chapter are listed in Table 9.1, which also contains information on how to
access them.
9.2 Main Tools for Virtual Screening
VS have traditionally been classied as either Ligand-Based VS (LBVS) or
Structure-Based VS (SBVS) depending on whether the 3D structure of the target
is used (SBVS) or not used (LBVS) during the VS workow [3]. However, ligandand structure-based methods can also appear combined in the same VS pipeline
[10–12]. Interestingly, if the experimental 3D structure of the target is unavailable
but the structure from a homologous protein is known, then a homology model of
the target can be built and SBVS methods can be used to nd new active compounds
in chemical databases [5, 13]. With the AlphaFold program, [14–16] it is currently
possible to predict the structure of almost any protein (even if no experimental 3D
structure of homologous proteins of the target of interest is known) with a high
degree of accuracy. This means that almost all the technologies used in the VS
(SBVS or LBVS) are applicable to any target (though the application of AlphaFold
results in the eld of drug discovery is still at a very early stage and rather uncertain
[17–19]). The following sections describe the lters in Figure 9.1.
9.2.1 ADMET and PAINS Filtering
To be eective as a drug, a bioactive molecule must be able not only to reach its
target in the organism at a sucient concentration but also to remain there in bioactive form long enough for the expected biological events to occur [20]. ADME, the
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Table 9.1 Resources mentioned in this chapter.
Main tools for virtual screening
ADMET and PAINS ltering
SwissADME (http://www.swissadme.ch/)
FAF-Drugs4 (https://fafdrugs4.rpbs.univ-paris-diderot.fr/)
Protein–ligand docking
Rigid protein–ligand docking
AutoDock Vina (https://vina.scripps.edu/)
Induced t protein–ligand docking
SLIDE (https://github.com/psa-lab/SLIDE)
PELE Web server (https://pele.bsc.es/pele.wt)
Ensemble protein–ligand docking
DINC-COVID (http://dinc-covid.kavrakilab.org/)
Edock-ML (http://edock-ml.umsl.edu/)
Pharmacophore search
Structure-based pharmacophore
Pharmit (http://pharmit.csb.pitt.edu)
ZINCPharmer (http://zincpharmer.csb.pitt.edu/)
Ligand-based pharmacophore
PharmaGist (http://bioinfo3d.cs.tau.ac.il/PharmaGist/)
Shape/electrostatic similarity
ESP-Sim (https://github.com/hesther/espsim)
ElectroShape (https://ub.cbm.uam.es/chemogenomics/)
Protein-structure databases
PDB (https://www.rcsb.org/)
PDB-REDO Databank (https://pdb-redo.eu/)
SWISS-MODEL Repository (https://swissmodel.expasy.org/repository)
AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/)
Validating binding site and ligand coordinates in three-dimensional protein
complexes
VHELIBS (https://github.com/URVquimioinformatica-COS/VHELIBS)
Databases for searching new drugs
COCONUT (https://coconut.naturalproducts.net/)
GDBs (https://gdb.unibe.ch/downloads/)
ZINC20 (https://zinc20.docking.org/)
Databases of active molecules
The Binding Database (https://www.bindingdb.org/)
ChEMBL Database (https://www.ebi.ac.uk/chembl/)
PubChem (https://pubchem.ncbi.nlm.nih.gov/)
(continued)
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Table 9.1 (Continued)
Databases of decoy molecules
DUD-E (http://dude.docking.org/)
DEKOIS (http://www.pharmchem.uni-tuebingen.de/dekois/)
VDS (http://compbio.cs.toronto.edu/VDS/)
Tools for building custom-based decoy sets
DUD-E tool (http://dude.docking.org/generate)
DecoyFinder (https://github.com/URVquimioinformatica-COS/DecoyFinder)
Format descriptions
SDF (https://depth-rst.com/articles/2020/07/13/the-sdle-format/)
SMILES (https://www.daylight.com/dayhtml/doc/theory/theory.smiles.html)
PDB le format (http://www.wwpdb.org/documentation/le-format-content/format33/
v3.3.html)
acronym for absorption, distribution, metabolism, and excretion [21], describes how
pharmacokinetic behavior is simplied into discrete parameters to be addressed in
the drug discovery/preclinical phases. All four criteria inuence the levels and kinetics of drug exposure to tissues and thus the performance and pharmacological activity of the compound as a drug. When, in addition to ADME properties, the potential
or actual toxicity of the compound is taken into account, the acronym ADME-Tox,
ADME/T, or ADMET is used instead [22]. PAINS, the acronym for Pan Assay Interference Compounds, on the other hand, refers to compounds that interfere with
assay read-out or that can also react nonspecically with numerous targets in such
a way that they often generate false positives [23].
Filters to remove from the initial database those molecules that are predicted to
be PAINS or that have unfavorable ADMET properties are not part of the VS workow itself but part of the initial pretreatment phase of the molecules on which the
screening of bioactive compounds will be performed (being a PAINS or having unfavorable ADMET properties is an intrinsic characteristic of the molecule itself and
independent of the target against which the VS will be performed). Open access
servers such as SwissADME [20, 24] and FAFDrugs4 [22, 25] can predict which
input molecules (in SMILES format for SwissADME and SDF/SMILES format for
FAFDrugs4 [26, 27]) may be PAINS or have bad ADMET properties.
9.2.2 Protein–Ligand Docking
Protein–ligand docking predicts the coordinates of the complex between a protein
and a drug from their individual structures [28]. During this process, the protein
structure is usually considered rigid (or small conformational changes are allowed
in a very localized part of its structure), while the small molecule is considered able to
change its initial conformation to adapt to the protein-binding site. However, since
some targets show signicant exibility in their binding site, considering them as
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rigid during protein–ligand docking is a too strict approach. To overcome this limitation, several strategies have been developed, including induced t docking [29–33]
and ensemble docking [34, 35]. The most important dierence between these two
approaches is that, while induced docking attempts to directly simulate the coupled
motion of the receptor and ligand, ensemble docking uses a set of dierent target
structures (either experimental or from molecular dynamics simulations) to perform
calculations. AutoDock Vina [36, 37] is often used for protein–ligand rigid docking
whereas SLIDE [32] and Edock-ML [35] are examples of induced t and ensemble
protein–ligand docking, respectively.
9.2.3 Pharmacophore Search
A pharmacophore is a 3D abstract description of molecular features (i.e. pharmacophoric sites) that are necessary for molecular recognition and biological activity
of a ligand by its target. A pharmacophore may also contain exclusion volumes that
indicate the positions of the binding site that the ligand cannot ll because they are
already occupied by the atoms of the target itself [3].
The simplest way to obtain a pharmacophore is from the three-dimensional (3D)
structure of a complex between the target of interest and a drug that has the desired
activity on this target (referred to as a structure-based pharmacophore).
When several drugs are known to be bioactive with respect to the same target but
the 3D structure of the target is not known, a ligand-based pharmacophore can be
obtained (assuming that all these ligands also share the target binding site and the
binding mode). Here, it is assumed that the combination of conformers (one per
ligand) with the maximum number of common pharmacophoric sites corresponds
to the way all these ligands bind to the 3D structure of the target (the common
sites are also assumed to represent the intermolecular interactions responsible for
their recognition and bioactivity with respect to the target). If inactive compounds
are also known, they can be used to add exclusion volumes to the ligand-based
pharmacophore.
Once a pharmacophore has been obtained, it can be used to search for small
molecules that occupy the maximum number of pharmacophore sites and at the
same time do not occupy exclusion volumes. Molecules that t the pharmacophore
will be candidates for having the same bioactivity for the target as the molecule (or
molecules) from which the pharmacophore was obtained.
PharmaGist [38, 39] is a free resource for constructing ligand-based pharmacophores, while Pharmit (see Figure 9.2) [41–43] and ZINCPharmer [44, 45] are
free resources for building structure-based pharmacophores. Moreover, all three
resources can be used to screen small molecule databases.
Pharmacophores can also be used to examine the poses resulting from a previous protein–ligand docking step. In this way, it is possible to assess whether the
ligands, once oriented within the target binding site, are able to establish the intermolecular interactions needed to show high activity toward the drug target. In fact,
this is another way to exploit the main strength of protein–ligand docking algorithms (i.e. their ability to nd the bioactive pose) and obviate their main weakness
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Figure 9.2 The Pharmit web interface for a pharmacophore search. A pharmacophore
query for SARS-CoV-2 M-pro in complex with a perampanel derivative (PDB 7L10 [40]) is
shown on the Pharmit web interface [41].
(i.e. their inability to correctly calculate the anity of each pose for the target). To
do this, one would only need to make sure that, for each ligand, the output of the
docking program provides a sucient number of poses to ensure that the correct
one is among them (regardless of whether the scoring function places it at the top
of the predicted anity ranking for the target). This strategy has been successfully
used to identify bioactive compounds for dierent targets [10, 11] and is similar
to using constraints during protein–ligand docking (though, in our opinion, using
pharmacophores allows greater exibility in the VS strategy than using these constraints because, among other things, pharmacophore site types are more diverse
than protein–ligand docking constraints).
9.2.4 Shape/Electrostatic Similarity
Two molecules that, upon binding to a given target, share a 3D shape and an electrostatic charge distribution on their surface (even though they have a dierent chemical structure) may have similar bioactivity for that target [46, 47]. It is therefore
possible to compare the shape and electrostatic surface area of the poses that have
passed through the previous lters (these would be the pharmacophore-compliant
docking poses; see Figure 9.1) with the experimental poses of drugs that are cocrystallized with the target of interest. However, care must rst be taken to superimpose
the structure of all these complexes onto the structure of the target used to obtain the
docking poses and the pharmacophore. Only in this way can it be guaranteed that
the comparison is made between molecules with the same relative orientation in
the target binding site. Moreover, if more than one complex between a highly active
drug and the target of interest is available, each docking pose must be compared
with each experimental pose, and the highest similarity must be chosen. Then, if
more than one docking pose is available for the same ligand, only the one that shows
the greatest similarity to one of the experimental poses to which it is compared will
be kept (while the rest will be discarded) [6, 7, 11]. Finally, poses with a similarity
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ZINCO3851930H11 at 3BYZ
2D chemical structure
Shape similarity
(ET_shape= 0.560)
Electrostatic surface
similarity
(ET_pb = 0.640)
Figure 9.3 Shape and electrostatic similarity. Shape and electrostatic comparison between
the experimental pose of the H11 ligand in the PDB entry 3BYZ [48] and the docked pose of
the ZINC03851930 ligand in the 3BYZ binding site.
below a certain threshold will be discarded. This threshold must be selective enough
to reduce, as much as possible, the number of false positives during VS validation
(even if this means increasing the number of false negatives) [11].
Figure 9.3 shows the comparison between two poses, one of which is experimental (corresponding to the H11 ligand in the 3BYZ structure [48]), and the other
corresponds to the docking pose of the ZINC03851930 ligand in the same structure.
The shape similarity between the two poses is 0.560 (where 1.0 corresponds to
a perfect overlap, i.e. the same shape), and the Poisson–Boltzmann electrostatic
similarity is 0.640 (where 1.0 corresponds to an identical electrostatic potential
overlap).
ESP-Sim [49] and ElectroShape [47] are examples of free resources for shape/
electrostatic similarity searches.
9.2.5 Protein-Structure Databases
Many tools used during a VS require the 3D structure of the target to which the drug
is to bind. These 3D structures may have been obtained experimentally (using techniques such as X-ray diraction, nuclear magnetic resonance [NMR], or electron
microscopy) or may result from predictions obtained directly from their sequence
(using techniques such as homology modeling or protein threading). Several public
databases exist from which these 3D structures can be obtained. The most important
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ones are the Protein Data Bank (PDB) [50] and the PDB-REDO Databank [51] for
experimentally obtained structures and the SWISS-MODEL Repository [52] and the
AlphaFoldProtein Structure Database (AlphaFold DB) [15] for computationally predicted structures.
9.2.6 The Protein Data Bank
The PDB, which was created in 1971 at Brookhaven National Laboratory (Upton,
NY), originally contained seven structures [53]. Its purpose was to act as an international repository in which to deposit the coordinates of proteins determined by X-ray
diraction and make these data available on request. Since then, it has also included
protein structures determined by other techniques (NMR and electron microscopy)
and the 3D structures of other biomolecules (nucleic acids, oligosaccharides,
and their respective complexes with proteins). A PDB entry, which contains all
the information relating to a particular 3D structure deposited in the PDB, is
designated by a 4-character alphanumeric identier called a PDB identier or PDB
ID, which always begins with a number from 1 to 9 (e.g. 6LU7). There are currently
over 193,000 entries in the PDB, of which 168,000 correspond to proteins, 10,600
correspond to protein–nucleic acid complexes, 10,200 to protein–oligosaccharides
complexes, 3900 to nucleic acids, and 22 to oligosaccharides. Of these 193,000
entries, 167,000 were determined by X-ray diraction, 13,700 were determined by
NMR, and 12,000 were determined by electron microscopy [54]. The coordinates of
the PDB structures are contained in text-only les (one le for each PDB entry) in a
PDB-specic format [55] that is soon to be replaced by the mmCIF format [56]. All
PDB les can be downloaded without restriction from the PDB or PDBe websites
[57, 58].
9.2.7 The PDB-REDO Databank
The PDB-REDO databank [59] contains optimized versions of existing PDB entries
that simultaneously satisfy two criteria: (i) the entry has been obtained by X-ray
diraction; and (ii) the author deposited the corresponding structure factor les
along with the coordinates. All PDB entries that satisfy both criteria are treated with
a consistent protocol that reduces eects due to dierences in age, software, and
depositor, thus making PDB-REDO a great dataset for large-scale structure analysis
studies [51, 60]. For example, an evaluation of the goodness of t of the coordinates
of 39,820 protein/ligand complexes to the electron density map using the VHELIBS
program showed that both binding-site and ligand coordinates were much more
reliable if they were obtained from PDB-REDO than if they were obtained from
PDB [61]. Specically, while VHELIBS rated 14,304 binding sites and 25,350 ligands obtained from PDB-REDO as “Good” (the other categories were “Dubious”
and “Bad”), these values decreased by 50% when the data were obtained from PDB
[62] and EDS [63] (7671 and 12,419, respectively). It is therefore recommended that,
whenever possible, priority should be given to working with structures obtained
from PDB-REDO rather than those obtained directly from PDB.
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9.2.8 The SWISS-MODEL Repository
The purpose of the SWISS-MODEL Repository is to provide access to an
up-to-date collection of annotated 3D protein models generated for all UniProtKB
sequences [64] for which no experimental structures are available [52]. Since the
structures in this repository were obtained by homology modeling (and these types
of models can only be obtained by the automated SWISS-MODEL pipeline for those
proteins that have a minimum of 30% sequence identity with at least one protein
of known 3D structure that acts as a template during model construction), this
repository contains only structural models for a fraction of the UniProtKB proteins.
Interestingly, in cases where templates corresponding to the same sequence segment exhibit signicant conformational dierences, several models are generated
to reect structural diversity.
To inform users about the expected local accuracy, the quality of the various
models is evaluated and annotated using the QMEAN tool (only high-quality
models – i.e. those with an overall QMEANDisCo score above 0.5 – are imported
into the SWISS-MODEL repository) [65]. If the homology-modeled structure
for a sequence is unavailable, the user can build it interactively through the
SWISS-MODEL workspace [66, 67]. Currently, the repository contains 2,258,794
homology models that can be freely downloaded [68].
9.2.9 The AlphaFold Protein Structure Database
AlphaFold DB was released in July 2021 [15, 69]. This database contains the 3D
structures predicted by the AlphaFold program (CASP14 winner) for proteins
of experimentally unknown 3D structure [16]. Since, unlike the SWISS-Model,
AlphaFold does not need homologous protein structures to make its predictions [14], AlphaFold DB contains structural models of proteins that are not
in the SWISS-Model Repository. The rst release of this database covered the
human proteome and the proteomes of 20 other key organisms (e.g. Arabidopsis
thaliana, Escherichia coli, and Oryza sativa), while the second release added the
majority of manually curated UniProt entries (e.g. Swiss-Prot) [70]. AlphaFold DB
currently contains over 200 million entries that include the human proteome and
the proteomes of 47 other key organisms that are important in research and global
health [69].
Regarding the quality of the predicted 3D structures, AlphaFold produces a
per-residue estimate of their condence on a scale from 0 to 100. This condence
estimate, called pLDDT, corresponds to the score predicted by the model on the
lDDT-Cα metric. This value is stored in the B-factor elds of the AlphaFold database
les in PDB format, but, unlike the B-factor, the higher the value of pLDDT, the
better the quality of the prediction. Regions with pLDDT > 90 are thus expected to
be modeled with high accuracy, while those with pLDDT between 70 and 90 are
expected to be modeled well, and those with pLDDT between 50 and 70 are of low
condence and should be treated with caution.
In theory, it would be advisable to use only AlphaFold DB structures during
drug discovery or development if their binding sites have a pLDDT value between
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