gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/opentargets-database/references/target_annotations.md

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:

1. Clinical Precedence - Target of approved/clinical drug with similar mechanism
2. Discovery Precedence - Target of tool compounds or compounds in preclinical development
3. Predicted Tractable - Computational predictions suggest druggability
4. 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:

1. Druggability (Tractability):

   • Clinical precedence >> Discovery precedence > Predicted
   • Consider modality relevant to therapeutic approach
   • Check for existing tool compounds

2. 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

3. Disease Relevance:

   • Combine with association scores
   • Check expression in disease-relevant tissues
   • Review pathway context

4. Validation Readiness:

   • Chemical probes available?
   • Model organism data supportive?
   • Known drugs provide mechanism insight?

5. 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