Target Annotations and Features

Overview

Open Targets defines a target as "any naturally-occurring molecule that can be targeted by a medicinal product." Targets are primarily protein-coding genes identified by Ensembl gene IDs, but also include RNAs and pseudogenes from canonical chromosomes.

Core Target Annotations

1. Tractability Assessment

Tractability evaluates the druggability potential of a target across different modalities.

Modalities Assessed:

Small Molecule

Prediction of small molecule druggability
Based on structural features, chemical precedence
Buckets: Clinical precedence, Discovery precedence, Predicted tractable

Antibody

Likelihood of antibody-based therapeutic success
Cell surface/secreted protein location
Precedence categories similar to small molecules

PROTAC (Protein Degradation)

Assessment for targeted protein degradation
E3 ligase compatibility
Emerging modality category

Other Modalities

Gene therapy, RNA-based therapeutics
Oligonucleotide approaches

Tractability Levels:

Clinical Precedence - Target of approved/clinical drug with similar mechanism
Discovery Precedence - Target of tool compounds or compounds in preclinical development
Predicted Tractable - Computational predictions suggest druggability
Unknown - Insufficient data to assess

2. Safety Liabilities

Safety information aggregated from multiple sources to identify potential toxicity concerns.

Data Sources:

ToxCast

High-throughput toxicology screening data
In vitro assay results
Toxicity pathway activation

AOPWiki (Adverse Outcome Pathways)

Mechanistic pathways from molecular initiating event to adverse outcome
Systems toxicology frameworks

PharmGKB

Pharmacogenomic relationships
Genetic variants affecting drug response and toxicity

Published Literature

Expert-curated safety concerns from publications
Clinical trial adverse events

Safety Flags:

Organ toxicity - Liver, kidney, cardiac effects
Target safety liability - Known on-target toxic effects
Off-target effects - Unintended activity concerns
Clinical observations - Adverse events from drugs targeting gene

3. Baseline Expression

Gene/protein expression across tissues and cell types from multiple sources.

Data Sources:

Expression Atlas

RNA-Seq expression across tissues/conditions
Normalized expression levels (TPM, FPKM)
Differential expression studies

GTEx (Genotype-Tissue Expression)

Comprehensive tissue expression from healthy donors
Median TPM across 53 tissues
Expression variation analysis

Human Protein Atlas

Protein expression via immunohistochemistry
Subcellular localization
Tissue specificity classifications

Expression Metrics:

TPM (Transcripts Per Million) - Normalized RNA abundance
Tissue specificity - Enrichment in specific tissues
Protein level - Correlation with RNA expression
Subcellular location - Where protein is found in cell

4. Molecular Interactions

Protein-protein interactions, complex memberships, and molecular partnerships.

Interaction Types:

Physical Interactions

Direct protein-protein binding
Complex components
Sources: IntAct, BioGRID, STRING

Pathway Membership

Biological pathways from Reactome
Functional relationships
Upstream/downstream regulators

Target Interactors

Direct interactors relevant to disease associations
Context-specific interactions

5. Gene Essentiality

Dependency data indicating if gene is essential for cell survival.

Data Sources:

Project Score

CRISPR-Cas9 fitness screens
300+ cancer cell lines
Scaled essentiality scores (0-1)

DepMap Portal

Large-scale cancer dependency data
Genetic and pharmacological perturbations
Common essential genes identification

Essentiality Metrics:

Score range: 0 (non-essential) to 1 (essential)
Context: Cell line specific vs. pan-essential
Therapeutic window: Selectivity between disease and normal cells

6. Chemical Probes and Tool Compounds

High-quality small molecules for target validation.

Sources:

Probes & Drugs Portal

Chemical probes with characterized selectivity
Quality ratings and annotations
Target engagement data

Structural Genomics Consortium (SGC)

Target Enabling Packages (TEPs)
Comprehensive target reagents
Freely available to academia

Probe Criteria:

Potency (typically IC50 < 100 nM)
Selectivity (>30-fold vs. off-targets)
Cell activity demonstrated
Negative control available

7. Pharmacogenetics

Genetic variants affecting drug response for drugs targeting the gene.

Data Source: ClinPGx

Information Included:

Variant-drug pairs
Clinical annotations (dosing, efficacy, toxicity)
Evidence level and sources
PharmGKB cross-references

Clinical Utility:

Dosing adjustments based on genotype
Contraindications for specific variants
Efficacy predictors

8. Genetic Constraint

Measures of negative selection against variants in the gene.

Data Source: gnomAD

Metrics:

pLI (probability of Loss-of-function Intolerance)

Range: 0-1
pLI > 0.9 indicates intolerant to LoF variants
High pLI suggests essentiality

LOEUF (Loss-of-function Observed/Expected Upper bound Fraction)

Lower values indicate greater constraint
More interpretable than pLI across range

Missense Constraint

Z-scores for missense depletion
O/E ratios for missense variants

Interpretation:

High constraint suggests important biological function
May indicate safety concerns if inhibited
Essential genes often show high constraint

9. Comparative Genomics

Cross-species gene conservation and ortholog information.

Data Source: Ensembl Compara

Ortholog Data:

Mouse, rat, zebrafish, other model organisms
Orthology confidence (1:1, 1:many, many:many)
Percent identity and similarity

Utility:

Model organism studies transferability
Functional conservation assessment
Evolution and selective pressure

10. Cancer Annotations

Cancer-specific target features for oncology indications.

Data Sources:

Cancer Gene Census

Role in cancer (oncogene, TSG, fusion)
Tier classification (1 = established, 2 = emerging)
Tumor types and mutation types

Cancer Hallmarks

Functional roles in cancer biology
Hallmarks: proliferation, apoptosis evasion, metastasis, etc.
Links to specific cancer processes

Oncology Clinical Trials

Drugs in development targeting gene for cancer
Trial phases and indications

11. Mouse Phenotypes

Phenotypes from mouse knockout/mutation studies.

Data Source: MGI (Mouse Genome Informatics)

Phenotype Data:

Knockout phenotypes
Disease model associations
Mammalian Phenotype Ontology (MP) terms

Utility:

Predict on-target effects
Safety liability identification
Mechanism of action insights

12. Pathways

Biological pathway annotations placing target in functional context.

Data Source: Reactome

Pathway Information:

Curated biological pathways
Hierarchical organization
Pathway diagrams with target position

Applications:

Mechanism hypothesis generation
Related target identification
Systems biology analysis

Using Target Annotations in Queries

Query Template: Comprehensive Target Profile

query = """
  query targetProfile($ensemblId: String!) {
    target(ensemblId: $ensemblId) {
      id
      approvedSymbol
      approvedName
      biotype

      # Tractability
      tractability {
        label
        modality
        value
      }

      # Safety
      safetyLiabilities {
        event
        effects {
          dosing
          organsAffected
        }
      }

      # Expression
      expressions {
        tissue {
          label
        }
        rna {
          value
          level
        }
        protein {
          level
        }
      }

      # Chemical probes
      chemicalProbes {
        id
        probeminer
        origin
      }

      # Known drugs
      knownDrugs {
        uniqueDrugs
        rows {
          drug {
            name
            maximumClinicalTrialPhase
          }
          phase
          status
        }
      }

      # Genetic constraint
      geneticConstraint {
        constraintType
        score
        exp
        obs
      }

      # Pathways
      pathways {
        pathway
        pathwayId
      }
    }
  }
"""

variables = {"ensemblId": "ENSG00000157764"}

Annotation Interpretation Guidelines

For Target Prioritization:

Druggability (Tractability):
- Clinical precedence >> Discovery precedence > Predicted
- Consider modality relevant to therapeutic approach
- Check for existing tool compounds
Safety Assessment:
- Review organ toxicity signals
- Check expression in critical tissues
- Assess genetic constraint (high = safety concern if inhibited)
- Evaluate clinical adverse events from drugs
Disease Relevance:
- Combine with association scores
- Check expression in disease-relevant tissues
- Review pathway context
Validation Readiness:
- Chemical probes available?
- Model organism data supportive?
- Known drugs provide mechanism insight?
Clinical Path Considerations:
- Pharmacogenetic factors
- Expression pattern (tissue-specific is better for selectivity)
- Essentiality (non-essential better for safety)

Red Flags:

High essentiality + ubiquitous expression - Poor therapeutic window
Multiple safety liabilities - Toxicity concerns
High genetic constraint (pLI > 0.9) - Critical gene, inhibition may be harmful
No tractability precedence - Higher risk, longer development
Conflicting evidence - Requires deeper investigation

Green Flags:

Clinical precedence + related indication - De-risked mechanism
Tissue-specific expression - Better selectivity
Chemical probes available - Faster validation
Low essentiality + disease relevance - Good therapeutic window
Multiple evidence types converge - Higher confidence

/opentargets-database