Protenix Supported Models

Protenix provides various pre-trained models to suit different computational resources, inference speeds, and prediction accuracy requirements. This document details the characteristics and configurations of these models.

Naming Convention

Model names follow the format: protenix_{model_size}_{features}_{version}

• model_size: Size of the model, including base, mini (lightweight), and tiny (minimal).
• features: Functional characteristics such as default, constraint (distance constraints), esm (includes ESM embeddings), etc. Multiple features are separated by -.
• version: Version number, e.g., v0.5.0, v1.0.0.

Supported Models Summary

Model Name	ESM	MSA	Constraint	RNA MSA	Template	Params	Training Data Cutoff
protenix-v2	❌	✅	❌	✅	✅	464.44 M	2021-09-30
protenix_base_default_v1.0.0	❌	✅	❌	✅	✅	368.48 M	2021-09-30
protenix_base_20250630_v1.0.0 *	❌	✅	❌	✅	✅	368.48 M	2025-06-30
protenix_base_default_v0.5.0	❌	✅	❌	❌	❌	368.09 M	2021-09-30
protenix_base_constraint_v0.5.0	❌	✅	✅	❌	❌	368.30 M	2021-09-30
protenix_mini_esm_v0.5.0	✅	✅	❌	❌	❌	135.22 M	2021-09-30
protenix_mini_ism_v0.5.0	✅	✅	❌	❌	❌	135.22 M	2021-09-30
protenix_mini_default_v0.5.0	❌	✅	❌	❌	❌	134.06 M	2021-09-30
protenix_tiny_default_v0.5.0	❌	✅	❌	❌	❌	109.50 M	2021-09-30

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Model Detailed Descriptions

1. Base Models

• Characteristics: Full-parameter models with the highest prediction accuracy.
• Key Configurations:
  • N_cycle: 10 (Number of recycle iterations).
  • sample_diffusion.N_step: 200 (Diffusion steps for higher quality sampling).
• Use Case: Scientific research requiring maximum precision.

2. Mini & Tiny Models

• Characteristics: Significant reduction in parameters and faster inference speed.
• Key Configurations:
  • N_cycle: 4
  • sample_diffusion.N_step: 5
• Difference: Mini has more layers in Pairformer and Transformer modules compared to Tiny.
• Use Case: High-throughput screening or scenarios with limited computational resources.

3. Constraint Model (protenix_base_constraint_v0.5.0)

• Characteristics: Allows incorporating additional experimental constraints during inference (e.g., Pocket, Contact).
• Included Features:
  • pocket_embedder: Handles binding pocket information.
  • contact_embedder: Handles contact point information.
• Use Case: Predictions with available structural priors.

4. ESM & ISM Models

• Characteristics: Integrates the single-sequence protein language model (ESM2-3B), performing better when MSAs are unavailable.
• Difference: ESM uses standard ESM2 embeddings, while ISM uses specific ISM embeddings.
• Note: For efficiency, these models do not use MSA by default.

5. Protenix-v2 (Enhanced Capacity Model)

• Characteristics: An enhanced-capacity version of the base model, featuring increased representation dimensionality (e.g., c_z=256) and expanded parameter space (~464M), along with substantial training and optimization improvements.
• Key Configurations:
  • N_cycle: 10
  • sample_diffusion.N_step: 200
• Use Case: Designed for tasks requiring richer representations and improved modeling fidelity.