Using multiple ontologies to characterise the bioactivity of small molecules

Use of Multiple Ontologiesto Characterise the Bioactivity

of Small MoleculesYing Yan1

Janna Hastings2,3

Jee-Hyub Kim1

Stefan Schulz4

Christoph Steinbeck2

Dietrich Rebholz-Schuhmann1

1 Text Mining, European Bioinformatics Institute, UK2 Chemoinformatics and Metabolism, European Bioinformatics Institute, UK

3 Swiss Centre for Affective Sciences, University of Geneva, Switzerland4 Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria

WoMBO @ ICBO, Buffalo, July 2011

Wednesday, April 12, 2023 2Multiple Ontologies for Small Molecule Bioactivity – WoMBO 2011

Bioactivity is what small molecules do in biological systems

Small molecules bind to receptors

Biochemical pathway is altered

On a macro scale, a phenotypic effect is observed


ChEBI is an ontology of small molecules and their properties

ChEBI Ontology

chemical entity role

carboxylic acid

application

antibacterial drug

cefpodoxime (CHEBI:606443)

pharmaceutical

biological role

chemical role

chemical substance

molecular entitygroup

carbonyl compoundsolvent

has role

cyclooxygenaseinhibitor

carboxy group

has part


Roles

ChEBI role assertions are sparse

Chemical entities(26000)

Mapped roles(600)

Chemical entities

mapped to roles

(3000)

has role


Bioactivity is reported in the scientific literature

“Resveratrol inhibits cyclooxygenase-2 transcription and activity in phorbol ester-treated human mammary epithelial cells”

“Curcumin inhibits cyclooxygenase-2 transcription in bile acid-and phorbol ester-treated human gastrointestinal epithelial cells”


ChEBI bioactivities are pre-coordinated


Bioactivity refers to multiple semantic types

Enzymes / proteins in general

Biological processes

Cellular or anatomical locations

Organism type


The language of bioactivity

inhibitor activator modulatoragonist antagonist regulator

suppressor adaptor stimulatortoxin factor messenger blocker

Relation extraction via trigger words as features

chemical target


Targets and types of interaction

beta-adrenergic receptor inhibitor

targettype ofinteraction


Several syntactical structuresNoun phrase or adjective/adverb composition: Kinase suppressor, HIV transcriptase inhibitor

Prepositional phrase modifier: Suppressor of fused protein Oct-1 CoActivator in S phase protein

Verb phrase as noun phrase modifier: Carbonic-anhydrase inhibitors causing adverse effects in therapeutic use

Relative clauses as modifier: Factor that binds to inducer of short transcripts protein 1


Text mining approach

1. Syntactic parsing2. Chemical tagging (Oscar, Jochem)3. Named entity recognition

(UniProtKB, Organ, Organisms and GO Biological Process)

4. Target disambiguation (nested types)5. Pruning ‘noisy’ results using rules

source: MEDLINE abstracts


Pruning out noiseLargest challenges:• Difficulty in small molecule term recognition• Small molecule – protein disambiguation

Remove triples from the candidate list when the putative small molecule term:• is a role term according to ChEBI

(e.g. antibiotic)• has the suffix -ase (normally enzyme names)• has less than three characters


Results: distribution (feature/target)


Organ and Organism: Target vs. Location

Organ and organism often provide contextual/ locational information

However there are some true positives (as bioactivity targets)

Caesium ion antagonism to chlorpromazine- and L-dopa- produced behavioural depression in mice.

bothrops jararaca inhibitor thyroid stimulator


Noise

• On the other hand, …

• Influence of peritoneal dialysis on factors affecting oxygen transport…

• Without influence on WDS were: hysotigmine, atropine …

• The cellulase component was not markedly inhibited by …

body part?

species?

bioactive?


Tagging chemicals

Jochem – dictionary-based approach: better precision, lower recall

Oscar3 – machine learning approach: better recall, much more noise


The ontology of bioactivity

chemical entity bioactivity

Target

Macromolecule

has_role

has_target

is_a

Organ

Organism

Biological process


Macromoleculesm1 is a beta adrenergic receptor:

m1 subclassOf bearer of some(realized by only

(Inhibition and(has target some BetaAdrenergicReceptor)))


Biological processesm2 is a mitosis stimulator:

m2 subclassOf bearer of some(realized by only (Stimulation and

(has target some (participant of some Mitosis))))


Organ as targetm3 is a thyroid stimulator:


(has target some (has locus some ThyroidGland))))


Species as definitional constraintm4 is a mouse thyroid stimulator:


(has target some (has locus some (ThyroidGland

and part of some Mouse)))))


Contextual vs. DefinitionalOrganisms, organs and body parts appear frequently as

contextual, locational modifiers for bioactivities

In these cases, the above formalism is too strict

We therefore introduce an additional relationship: has context

between a bioactivity and an organism, organ, body part

Non-definitional: the bioactivity can take place in many organisms, but was discovered through investigations in one organism.


Relating context to chemical-bioactivity associations

Context applies not to bioactivity alone

but to small molecule – bioactivity associations

(i.e. a ternary relationship)


Next-generation curation tools

Text mining support for human curation knowledge discovery effort

Multiple ontology-based reasoning for automated consistency checking and error

detection


Conclusions

• Language model for extracting small molecule bioactivity information from text

• Ontology model for accurately representing such information, and allowing automated reasoning across ontologies from chemicals to their targets


Future work

• Gold standard for chemical bioactivity in text to be used to evaluate our approach and to train machine learning tools

• Extending the relationship extraction approach to include chemical roles, applications and structural relationships

Wednesday, April 12, 2023 27

AcknowledgementsThanks

Colin Batchelor (RSC), Adam Bernard (EBI)

FundingBBSRC, grant agreement number

BB/G022747/1 within the "Bioinformatics and biological resources" fund

Using multiple ontologies to characterise the bioactivity of small molecules

Education

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