ApiPred

Predict fitness phenotypes and invasion machinery in apicomplexan proteomes from sequence alone.

ApiPred uses ESM-2 protein language model embeddings to predict protein essentiality in tachyzoite culture, invasion compartment membership, and structural context for any apicomplexan species, trained on Toxoplasma gondii experimental data and validated across Plasmodium berghei.

How it works

graph LR
    A[FASTA proteome] --> B[ESM-2 embedding<br>1,280 dims per protein]
    B --> C[Essentiality model]
    B --> D[Invasion model]
    B --> E[Reference database]
    C --> F[CRISPR fitness score<br>Essential / Important / Dispensable]
    D --> G[Invasion probability<br>Compartment prediction]
    E --> H[Top 3 similar known proteins<br>Structural novelty flag]

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Performance

Metric	Value	Note
CRISPR score prediction (Spearman rho)	0.56	5-fold CV, 3,796 T. gondii proteins
Fitness classification (ROC AUC)	0.77	Essential (CRISPR < -3) vs non-essential
Invasion classification (ROC AUC)	0.95	5-fold CV, 2,634 proteins with known compartments
Cross-species fitness transfer (Spearman rho)	0.31	Tg-predicted scores vs Pb experimental growth rates

Note: the invasion AUC (0.95) is evaluated on 2,634 proteins with confident hyperLOPIT compartment assignments, excluding 1,198 proteins of unknown localisation. Including unknowns as negatives reduces AUC to 0.90.

Validation

(A) Cross-species transfer: model trained on T. gondii CRISPR data predicts P. berghei experimental growth rates across 1,136 ortholog pairs (rho = 0.40 for actual Tg scores; rho = 0.31 for ApiPred-predicted scores). (B) Within-species 5-fold cross-validation on 3,796 T. gondii proteins (rho = 0.56). (C) Per-compartment fitness: invasion compartments (red) are dispensable in tachyzoite culture while housekeeping machinery (ribosomes, proteasome) is essential, confirming biological coherence.

Example output

# T. gondii proteins
python predict.py --input examples/test_tg.fasta --output predictions_tg.tsv

# P. falciparum proteins (cross-species)
python predict.py --input examples/test_pf.fasta --output predictions_pf.tsv

T. gondii example:

Protein	Fitness	CRISPR Score	Invasion	Top Match	Compartment
TGME49_250340	important	-2.49	yes	centrin 2	apical 2
TGME49_243250	important	-2.41	no	myosin H	apical 2
TGME49_226220	important	-1.71	yes	alveolin domain protein	IMC
TGME49_246930	important	-1.63	yes	calmodulin CAM1	apical 2
TGME49_300100	dispensable	-0.61	yes	RON2	rhoptries 1
TGME49_262730	dispensable	0.46	yes	ROP16	rhoptries 1

Each protein also gets its top 3 structurally similar characterised proteins (with descriptions, compartments, and CRISPR scores) and a structural novelty flag.

Installation

git clone https://github.com/jsmccabe1/apipred.git
cd apipred
pip install -r requirements.txt

Pre-trained models are included in models/. No additional downloads required.

Quick start

# Predict with pre-trained models
python predict.py --input my_proteome.fasta --output predictions.tsv

# Use GPU (recommended for >100 proteins)
python predict.py --input my_proteome.fasta --output predictions.tsv --device cuda

# Verify installation
python predict.py --input examples/test_tg.fasta --output /tmp/test.tsv

Training from scratch

To retrain models from the source T. gondii data:

python train_model.py --data-dir /path/to/Apicomplexa/

This requires the Apicomplexa analysis pipeline data directory containing:

• results/embeddings/all_proteins/protein_embeddings.npy
• data/processed/protein_features.tsv (includes Sidik et al. CRISPR scores)
• data/processed/protein_compartments.tsv (hyperLOPIT assignments)

Generates three files in models/:

• essentiality_model.joblib - CRISPR score regressor + fitness classifier
• invasion_model.joblib - invasion compartment classifier
• reference_db.npz - 2,634 characterised T. gondii proteins for structural context

Output columns

Column	Description
protein_id	FASTA header ID
description	FASTA header description
length	Protein length (aa)
predicted_crispr_score	Predicted CRISPR fitness (more negative = more essential in culture)
essential_probability	P(essential), where essential = CRISPR score < -3
essentiality_class	essential / important / dispensable
invasion_probability	P(invasion compartment)
predicted_invasion	yes / no
similar_1_id	Most structurally similar characterised protein
similar_1_desc	Its description
similar_1_compartment	Its subcellular compartment
similar_1_similarity	Cosine similarity (0-1)
similar_1_crispr	Its experimental CRISPR score
similar_2_*, similar_3_*	2nd and 3rd most similar
max_similarity_to_known	Highest similarity to any characterised protein
structural_novelty	novel (sim < 0.95) or known_fold

Training data

• Fitness labels: Sidik et al. 2016, genome-wide CRISPR screen in T. gondii tachyzoites (3,796 proteins with scores)
• Invasion labels: Barylyuk et al. 2020, hyperLOPIT spatial proteomics (425 invasion, 2,209 non-invasion, 1,198 unknown excluded)
• Embeddings: ESM-2 (esm2_t33_650M_UR50D, 650M parameters)
• Models: Gradient boosting (scikit-learn), 5-fold cross-validated

Limitations

• Fitness predictions reflect tachyzoite culture conditions (Sidik et al. 2016); genes dispensable in vitro may be essential in vivo or in other life stages
• Cross-species transfer validated for P. berghei blood stages only; accuracy on more distant species (Cryptosporidium, gregarines) is untested
• ESM-2 context window is 1,022 tokens; longer proteins use sliding window mean-pooling
• Structural context is relative to characterised T. gondii proteins; truly novel folds may not be detected
• Invasion predictions trained on hyperLOPIT compartment labels; may be less accurate for non-Toxoplasma species
• ESM-2 was trained on UniRef50 which includes apicomplexan proteins, so the embeddings aren't fully independent of the training labels

Citation

If you use ApiPred, please cite:

McCabe, JS. (2026) Protein language model embeddings reveal the structural logic of apicomplexan host cell invasion. In preparation.

And the underlying methods:

• Lin Z et al. (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379:1123-1130.
• Sidik SM et al. (2016) A genome-wide CRISPR screen in Toxoplasma identifies essential apicomplexan genes. Cell 167:1423-1435.
• Barylyuk K et al. (2020) A comprehensive subcellular atlas of the Toxoplasma proteome via hyperLOPIT. Cell Host & Microbe 28:752-766.

License

MIT