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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5942_Библиотеки_им_академика_М_И_Перельмана
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(a)
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3.3 Protocols 81
(b)
Figure 3.6 Using DrugBank Online’s advanced search functionality. This figure shows the
process of conducting an advanced search. (a) The completed advanced query includes
multiple search conditions and display fields. (b) Search results are displayed in list format,
with each result containing the fields used in the search conditions and requested in the
display fields.
and management of cardiovascular diseases. As per World Health Organization
(WHO) guidance around International Nonproprietary Names (INN), inhibitors
of ACEare given the sux “-pril” [23]. For the rst search condition, the eld and
predicate can remain in their default state (“Name” and “matches”) – this will
search through drug names in DrugBank and return any results that match the
query entered in the “search drug name” textbox. To complete the rst search condition, type *pril into the textbox – because the asterisk can be used as a wildcard
matching any number of characters, this search term will nd any drug names
that end with the string “pril.”
82 3 DrugBank Online: A How-to Guide
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a. The rst dropdown box for a given search condition species the eld in
which the user wishes to search. The advanced search function supports a
number of search elds, including drug identiers and chemical properties
(e.g. brands/products, CAS number, InChI, chemical formula, and predicted
logP) as well as drug type and availability (e.g. small molecule, approved, and
withdrawn; see Section 3.2.2.2), among others.
b. The second dropdown box for a given search condition species the predicate,
which simply tells the search how to query the chosen eld for the inputted
text. Predicates provide additional search exibility by allowing users to build
more complex queries – for example, the predicate in the above exercise may
be set to “does not match” to generate a list of drugs that do not contain the
string “pril.” Supported predicates are dependent on the selected search eld,
and in general include functions like “does not equal,” “starts with,” and “is
present,” among others.
i. Manipulating the predicate in the above example allows us to run a similar
search without using the wildcard (*) character. With the search eld set
to “Name,” we can set the predicate to “ends with” and type “pril” in the
textbox, this anchors the search to the end of the string and will nd any
instances in which a drug name ends with the sux “-pril.”
ii. Wildcard searching is supported when the predicate is set to either “match-
es” or “does not match.” Users can input an asterisk (*) to match any number of characters or a question mark (?) to match a single character.
3. After completing the rst search condition, select Add Search Condition to
include another. For this exercise, we want to search only for approved ACE
inhibitor drugs, so click the search eld dropdown and select “Approved.”
a. When the selected search eld can only evaluate to true or false (i.e. the drug
is either approved or is not), the available predicates will also change to reect
this. After selecting “Approved,” leave the predicate set to its default state, “is
true.”
4. With our search conditions set, we next need to set display elds. These are addi-
tional elds that will appear alongside our search results, which are not part of
the actual query. Click the Add Display Field button to create our rst display
eld.
a. Display elds can be selected from the same list available for search elds (e.g.
Name, CAS number, and InChI). They do not require a predicate or the input
of a query, as they are simply additional pieces of data that we would like to
display alongside our search results.
5. We will set three display elds: one each for CAS number, UNII, and average
mass. First, create two more display elds by clicking the Add Display Field
button two more times. In the rst display eld, select “CAS Number.” In the
second, select “UNII,” and in the third select “Average mass.”
a. Similar to creating search conditions, users can add as many display elds as
necessary to achieve the desired results.
6. Once the appropriate search conditions and display elds are set (Figure 3.6a),
clicking the Search button will generate the search results below the search
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widget. Each result will rst display the data found by the search conditions – in
this case, “Name” and whether the drug is “Approved” – followed by the
specied display elds. By default, each returned drug will also populate with its
approval status (e.g. approved, withdrawn, and investigational) in its top-right
corner (Figure 3.6b).
7. Users with a free DrugBank Online account can export the results of an advanced
search as a CSV le.
a. To create a new DrugBank Online account, click the Sign Up button near the
top of the advanced search page and follow the instructions provided. If you
have an existing DrugBank Online account, click Login and enter your username and password.
b. Once signed in, users can export their search results in CSV format by clicking
the Export button found at the top-right of the search results.
8. The advanced search function also supports searches of DrugBank’s drug target data. To search through targets instead of drugs, click the Target Advanced
Search button in the top-right of the advanced search page.
a. The functionality of the target advanced search is essentially identical to that
of the drug search. Users can add one or more search conditions, specifying a
search eld and, if necessary, a predicate and text query for each. Display elds
can also be added to target searches, and search results can be exported using
the method described in Step 7.
b. Rather than returning a list of drugs, target searches return a list of
biomolecules that may interact with drugs (e.g. receptors and enzymes). The
available search elds for this dataset are dierent than those available for the
advanced drug search, with more focus on target-specic data like UniProt
ID and taxonomy.
DrugBank Online’s advanced search functionality serves to illustrate the power
and potential of DrugBank data. The exibility aorded by this advanced search, as
well as the ability to export its results, means that users can generate highly focused
and specic datasets for use in a variety of applications, such as ML (see Section
3.3.3). A less focused exploration of drugs and compounds with similar traits can be
achieved by browsing through DrugBank Online’s drug categories as described in
the following section.
3.3.1.3 Browsing Drugs Using DrugBank Online’s Drug Categories
As described in Section 3.2.2.4, drugs in DrugBank are assigned categories that
serve to group similar drugs together based on shared characteristics. Drugs may
be grouped into categories based on mechanistic similarities (e.g. “Proton Pump
Inhibitors”), pharmacokinetic properties (e.g. “CYP3A4 Substrates”), structural
similarities (e.g. “Catecholamines”), or clinical use (e.g. “Antifungal Agents”).
Grouping like drugs together within drug categories can help to elucidate commonalities between member drugs, for example, a common target that might represent
an MoA or a common metabolic pathway through which member drugs may be
metabolized.
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1. From the DrugBank Online landing page, navigate to the drug category browser
(https://go.drugbank.com/categories) by clicking the Browse tab in the navigation bar at the top of the page and selecting Categories.
a. Individual categories can also be accessed directly from a drug card by clicking
the hyperlinked title of the category of interest from the “Categories” section
of the drug card (see Section 3.2.2.4).
2. Drug categories are presented as a searchable table that can be ltered by the
approval status and/or market availability of the drugs within them.
a. Each category in the table contains the name of the category, a truncated
description of the category, the number of drugs within the category, and the
total number of targets associated with those drugs.
i. The category table can be additionally ltered via these columns. Users can
search through category names and descriptions by inputting their search
term(s) in the text boxes at the top of each column. Inputting a value into
the text box at the top of the “# of drugs” or “# of targets” column will lter
the table to show only the categories, which contain a number of drugs or
targets greater than or equal to the value input at the top of the column.
b. Clicking the hyperlinked category name will direct the user to a category-
specic page with additional information about the selected category.
3. For this exercise, we will navigate to the “ACE inhibitors” category in order to
view the same drugs returned in the advanced search query outlined in Section
3.3.1.2. In the textbox at the top of the category column, type “enzyme inhibitors”
and click the magnifying glass or hit “Enter” to lter the list down to a handful
of categories (Figure 3.7a). Navigate to the category page for “ACE inhibitors” by
clicking the hyperlinked category name in the leftmost column.
4. Every category in DrugBank has a number of data elds that can be viewed
from the page of that specic category. Information about the category as a
whole includes its name, accession number (a 6-digit number prexed with
“DBCAT”), a description of the category, and its equivalent ATC classication
[12] (Figure 3.7a).
a. Some categories in DrugBank are associated with multiple accession numbers,
which will be indicated by additional bracketed accession numbers following
the rst. This means that two (or more) categories were, at some point, deemed
synonymous and merged together.
b. Within each category, users can browse through its member drugs and their
associated targets, or search through drugs and targets using the search bar in
the top right of the respective sections. In the “Drugs” section of the page, each
drug contained within the category will populate with its name (hyperlinked
to the relevant drug card) and a brief description of the drug. In the “Drug
& Drug Targets” section, the targets for each drug will display alongside the
name of the drug and the type of relationship between the drug and its target
(see Section 3.2.2.6 for more information on types of drug–target interactions).
Both the drug name and the target names in this section are hyperlinked to
their respective pages on DrugBank Online.
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3.3 Protocols 85
(b)
Figure 3.7 Browsing drug categories using DrugBank Online.Thisfigureshowsan
example of DrugBank’s browsing feature for drug categories. (a) The drug category browser
with search results narrowed to show only categories with “enzyme inhibitor” in the title.
Categories can be broadly filtered by group or market availability (of the drugs within
them), or more specifically searched via the text boxes at the top of each column.
(b) DrugBank’s drug category page for ACE inhibitors. Note that both the “Drugs” and “Drugs
and Drug Targets” lists can be searched using the search box to the upper-right of each list,
and can be reordered using the up-down arrow icons at the top of each column.
Organizing drugs into categories allows users to examine groups of similar drugs at a
higher level of abstraction. Previously hidden relationships might become apparent
when browsing drugs in this way – for example, we may notice a target or enzyme
common to several members of a given drug category that can provide clues or
additional context in the process of drug discovery or in the evaluation of a newly
synthesized molecule.
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3.3.2 Identifying Chemicals and Relevant Sequences
Text searching, including both basic (Section 3.3.1.1) and advanced (Section 3.3.1.2)
searching, was covered in Section 3.3. It is also possible to query DrugBank using
richer data structures including chemical structures and nucleic acid or protein
sequences.
3.3.2.1 Searching Using Chemical Structure Search
DrugBank Online’s chemical structure search, powered by ChemAxon (https://
chemaxon.com/), allows users to search for drugs based on their similarity to a
specied chemical structure. This type of search functionality is particularly useful
for chemists who are interested in nding similar molecules to newly synthesized
or identied compounds. It is also useful for searching for compounds that have
the same parent molecule or belong to the same drug class.
1. From the DrugBank Online landing page, navigate to the chemical structure
search (https://go.drugbank.com/structures/search/small_molecule_drugs/
structure) by clicking on the Search tab in the navigation bar and selecting
Chemical Structure (Figure 3.8a).
a. By default, the search parameters are set to nd drugs based on their “Similar-
ity” with a similarity threshold of 0.7 and will return a maximum of up to 100
results. These parameters can be adjusted as described in the next step.
b. Structures can be manually drawn into the MarvinJS drawing applet using the
provided tools in the drawing box. If a SMILES, InChI, or similar identier is
known, it can also be pasted into the canvas. For a complete explanation of the
MarvinJS drawing applet, refer to its ocial documentation [24] or click the
MarvinJS Tutorials button at the lower-right of the window.
c. To view an example of a pre-drawn structure, click the “Load example” button
located on the lower right side of the window.
2. To modify or rene a structure similarity search, users can edit the search options
located to the right of the drawing canvas.
a. Users can use radio buttons to specify that a query structure be searched based
on its “Similarity” to other molecules, whether it is a “Substructure” contained
within other drugs, or to specify that it must be an “Exact” match to other drugs
in DrugBank.
b. Additionally, users can adjust query parameters to specify a similarity thresh-
old, a minimum and maximum molecular weight, the maximum number of
displayed results, and the types of drugs returned.
c. The similarity threshold allows users to set a minimum similarity score for the
results of a chemical structure similarity search. A similarity score is a value
between 0 and 1 that represents the degree of similarity between the queried
structure and each returned structure, with a greater value indicating greater
similarity. These scores are generated by rst creating a chemical hashed
ngerprint – a bit string encoding structural features – of the structure
being queried, which by default is a 1024-bit ngerprint with a maximum
pattern length of seven. Using this ngerprint, the Tanimoto similarity
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(a)
(c) (d)
Figure 3.8 Using DrugBank Online’s chemical structure similarity search.Thisfigure
illustrates the process of conducting a chemical structure similarity search. (a) The Marvin
JS drawing canvas for drawing and inputting chemical structures to query. All search
options shown are in their default state. (b) The chemical structure of testosterone is drawn
on the canvas. Note the indexed atoms, which can aid in drawing and communicating more
complex structures – atoms indices are not displayed by default but can be turned on in the
settings menu indicated by the cogwheel icon at the top of the canvas. (c) Chemical
structure similarity search results using testosterone (b) as the queried structure. Results
are displayed in descending order of similarity to the queried structure, evident here by the
inclusion of testosterone itself as the first result. (d) A screenshot of DrugBank’s drug card
for testosterone, with the Similar Structures button below the structure image, highlighted.
(b)
metric between the queried structure and other structures in the database
is calculated. A more technical explanation of similarity scores is available
via ChemAxon’s documentation [25]. Note that the minimum allowable
similarity threshold is 0.3 – attempting to set it any lower will instead run the
search using the default value of 0.7.
3. For this exercise, we will assume the role of a researcher interested in developing
a novel anabolic steroid. We will draw the structure of testosterone, a simple
anabolic steroid, directly in the MarvinJS canvas in order to examine previously
synthesized testosterone derivatives and identify potential novel derivatives
that have yet to be tested. We will leave the stereochemistry of our molecule
unspecied – when the queried structure does not contain stereo information, the
search results will include molecules both with and without stereo information.
88 3 DrugBank Online: A How-to Guide
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a. To start, we will draw the four-ring steroid nucleus common to all steroid
compounds, which comprises three cyclohexane rings and one cyclopentane
ring. Select the cyclohexane ring from the bottom of the canvas and attach
two along their vertical axis, with the third attached to the top-right face of
the rightmost ring. Select the cyclopentane ring and attach it to the right side
of the third cyclohexane ring.
i. At this stage, it is useful to index (i.e. number) the atoms for ease of refer-
ence. Click the “View settings” button at the top of the canvas, represented
by a cogwheel icon, check the “Index atoms” checkbox, then hit “Ok.”
b. Next, we need to add some functional groups. Select the bond tool from the
left side of the canvas and add a single bond to carbons 3, 6, 12, and 17 by
clicking on each carbon. Note that, by default, the addition of a single bond
to an atom will attach a methyl group to the other end of that bond. Carbons
6 and 12 require methyl groups, but carbons 3 and 17 require a ketone and
hydroxyl group, respectively.
c. Select the oxygen atom from the right side of the canvas, and click on the
methyl group attached to carbons 3 and 17. This action will substitute the
carbon atom at these positions with oxygen and results in a hydroxyl group
attached to both carbons 3 and 17.
d. Finally, we will add a double bond between carbons 4 and 5, and to the
hydroxyl group at carbon 3 to create a ketone. Select the bond tool again and
click on the existing bond between carbons 4 and 5 to make it into a double
bond. Similarly, click on the single bond between carbon 3 and its hydroxyl
group to convert it into a double bond and the hydroxyl group into a ketone.
i) The complete structure should look identical to the one shown in
Figure 3.8b.
4. Prior to executing the search, click the “Approved” checkbox to limit the results
to only compounds, which have been approved for use in humans. The remainder of the search options can stay in their default setting. After conrming your
structure and search options, click the “Search” button to run the search.
5. The list of search results will appear below the MarvinJS structure editor and
will be organized in descending order of similarity to the queried structure
(Figure 3.8c). Each result will display along with a number of data points,
including the DrugBank ID, a similarity score (with higher scores indicating
better matches), a vector image of the matched structure, the name and CAS
number of the matched drug, its approval status, and its formula and molecular
weight.
a. Clicking the DrugBank ID will direct you to the DrugBank drug card entry for
that compound.
b. Clicking the vector image of the returned structure will open a new window
with a larger image.
6. The abovementioned protocol has described the steps involved in drawing a structure for which to search in MarvinJS. As mentioned previously, the structure
search can also generate structures to query based on certain chemical notation
formats like SMILES or InChI. If a compound already has a known SMILES or
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InChI string, copying and pasting this string directly into the canvas is generally
much easier and faster than drawing a structure from scratch.
7. A structure similarity search can also be performed from directly within a
DrugBank drug card. In the “Identication” section of the drug card, next to the
“Structure” heading, is an image of the drug’s structure (Figure 3.8d). Clicking
the button labeled “Similar Structures” directly below will immediately run a
structure similarity search using all of the default search options and return a
list of similar structures and their similarity scores.
This kind of chemical structure-based searching has a number of potential applicationsinregardtodrugdiscovery.Intheexampleabove,oursearchreturnedalistof
approved drug molecules with a structure similar to testosterone. One potential next
step might be to examine structural dierences in these testosterone derivatives as
compared to their relative potencies in order to determine the importance of certain
functional groups and their position within the molecule. Even this relatively simple
approach can provide the context required to guide further research and narrow the
focus of future drug discovery eorts.
When the structure is determined for a newly discovered or synthesized bioactive
compound, it can often provide clues as to the compound’s potential actions – in
other words, structural similarity to an existing compound might imply a similar
mechanism. Taking our admittedly simplied example from above, suppose we were
unaware that our starting compound was testosterone. By running a structure similarity search and looking at the results, we could immediately identify our mystery
compound as some type of steroid, and could then make inferences about things like
its MoA and pharmacokinetics based on known properties of similar compounds.
This search can also be used to identify potential protein targets (viewable by clicking
the hyperlinked DrugBank ID), predict side eects, and predict unexpected interactions with unintended protein targets.
3.3.2.2 Using Sequence Search to Find Similar Targets
It is possible to search DrugBank for similar protein sequences to a known sequence,
including targets, enzymes, carriers, and transporters. This can be useful to understand the types of molecules that are known to interact with your sequence (in cases
of an exact match) or sequences similar to your sequence. The similarity search is
powered by BLAST [26].
As an example, assume you have identied a putative target sequence based
on in silico or in vitro means, and wish to understand the chemical nature of
drugs that may bind to it. Navigate to the search page (https://go.drugbank.com/
structures/search/bonds/sequence), either by selecting Search -> Target Sequences
from the main navigation bar or manually entering the URL; you should see an
input form (Figure 3.9a). Next:
1. Enter one or more DNA or protein sequences to search in the main text input
box at the top of the form. These sequences should be in FASTA format (hovering
over the small question mark icon at the top right provides a full explanation of
this input format).
90 3 DrugBank Online: A How-to Guide
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(a)
(b)
Figure 3.9 Using the DrugBank Online sequence search. This figure provides an overview
of using the DrugBank Online sequence searching tool. (a) The sequence input form, which
accepts one or more FASTA-formatted nucleic acid or protein sequences. Users can adjust a
subset of BLAST parameters using the entry fields and radio buttons. The filters allow users
to restrict the search to sequences associated with subsets of drugs based on approval
status and to specific types of sequences (target, enzyme, carrier, or transporter; see Section
3.2.2.6). (b) The first result displayed after searching using the human C-X-C chemokine
receptor type 5. Note the hit metrics in the top right and the BLAST output alignment
present below; exact matches are denoted by the one-letter code between sequences, while
similar residues are denoted with a plus symbol (“+”). The bottom table lists the drugs with
which the identified sequence has known interactions in DrugBank.
2. Adjust the BLAST parameters, if desired. Note that not all parameters available
in BLAST, such as the choice of substitution matrix, are available to change. The
“Expectation value” controls the cuto for returning hits; increasing this value
will result in more hits but many more will be only slightly similar to the target
sequence. The default gap opening cost is set at one (as opposed to the normal
BLAST default of 11); this may result in hits with more gaps than otherwise
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