AbSeekTM Integrates Protenix: Opening an Era of Open Multi‑molecule Structure Prediction

Industry Insights

For antibody drug discovery and structural biology, multi‑molecule structure prediction faces common challenges:

• Fragmented multi‑molecule scenarios: monomer‑centric models fail to cover protein–protein, protein–ligand, and protein–nucleic‑acid systems, causing workflow fragmentation and high switching costs.
• Prediction–experiment disconnect: models struggle to incorporate distance restraints, epitopes, or ligand sites, preventing a closed loop between prediction and validation.
• Black‑box and non‑reproducibility: closed‑source models hinder auditing and retraining; parameters and data pipelines are inaccessible, stalling innovation and compliance.
• Expensive compute for complex systems: multi‑chain modeling is slow and memory‑heavy, limiting scalable screening and rapid iteration.
• Challenging antibody–antigen tasks: interface modeling and scoring are unstable; metrics like DockQ and RMSD fluctuate, weakening candidate prioritization.
• Unmet enterprise needs: demand for a unified architecture, cost control, reproducible pipelines, and seamless integration with existing workflows.

Introduction

Addressing these industry pain points and deployment needs, the Abseek^TM computational platform now officially deploys Protenix, an open‑source foundation model for multi‑molecule structure prediction developed by ByteDance AML AI4Science. As an open replication of AlphaFold3, Protenix accurately predicts 3D structures of complex systems—including protein–protein, protein–ligand, and protein–nucleic acid—under a unified architecture, enabling end‑to‑end modeling from monomers to complexes. Its introduction directly targets core challenges—non‑reproducible black‑box models, expensive compute for complex systems, and prediction–experiment disconnect—significantly improving efficiency and accuracy in antibody–antigen modeling and binding prediction, and ushering Abseek^TM into an era of open AI structure prediction. Try it in the Molecular Structure Prediction module.

What is Protenix?

Protenix is a multi‑modal structure prediction model that supports end‑to‑end modeling of proteins, nucleic acids, and small molecules within a unified framework. Built upon the AlphaFold3 architecture, it offers fully open training, inference, and data pipelines, opening multi‑molecule cooperative prediction capabilities to the research community.

In its technical report, Protenix demonstrates strong overall performance:

• Protein–ligand docking success rate (RMSD ≤ 2 Å) exceeds 80%.
• Protein–protein complex prediction success rate (DockQ > 0.23) averages above 70%.
• Antibody–antigen complex prediction success rate (DockQ > 0.23) reaches up to 35.5%.

Protenix is more than an algorithm; it symbolizes open science—transforming complex structure prediction from a “black box” into a reproducible, verifiable, and continually evolving intelligent tool.

What Changes After Deployment on AbSeekTM?

Deploying Protenix brings a systemic upgrade—from model architecture to user experience—comprehensively enhancing multi‑molecule modeling capabilities.

1. From monomers to complexes: one input, multiple predictions

Traditional models often focus on a single molecular type, such as AlphaFold2 for protein monomers. Protenix supports multi‑modal inputs, enabling simultaneous handling of:

• Protein–protein complexes (including antibody–antigen systems).
• Protein–ligand binding systems.
• Protein–DNA/RNA complexes.
• Structures with modified residues, metal ions, and cofactors.

After deployment, users simply upload sequences or structural files to generate multiple types of complex models—truly one prediction, many scenarios.

2. High‑performance optimization: faster, steadier, more economical

Protenix integrates BF16 mixed‑precision and custom CUDA kernel acceleration on the inference side. Compared with multi‑chain AlphaFold2, it achieves:

• 30–50% faster training.
• Inference reduced to under 10 minutes.
• Nearly half the memory footprint.

3. Integrating experimental data: predictions closer to reality

Protenix can incorporate experimental restraints such as distance constraints, known epitopes, or ligand sites. With partial experimental data, the model adjusts structural sampling to approach real conformations. Studies show:

• With one set of restraints, DockQ success rate improves by ~20%.
• With four sets, prediction accuracy more than doubles.

The interactive UI lets users enter restraints via forms—no manual files needed. Visit the AbSeekTM Platform to try it.

Who Should Use Protenix?

1. Biopharma companies

• Accelerate candidate screening and structural validation: automate from sequences to complex modeling, reducing compute and labor costs.
• Support affinity analysis and mutation assessment: quantify structural differences and binding changes with confidence metrics.
• Enhance project transparency: integrate structures into internal pipelines to close the prediction–experiment loop.

2. Research institutes and universities

• Lower modeling barriers: no high‑end GPUs or command‑line work; use the GUI.
• Facilitate collaboration: input experimental restraints directly during prediction.
• Broaden research: support atypical sequences, synthetic peptides, chimeric antibodies, and nucleic‑acid complexes.

3. AI for Science researchers

• Fully reproducible architecture and datasets: open training sets, parameters, and algorithmic details.
• Support retraining and transfer learning: fine‑tune models for specific tasks.
• Advance open‑source ecosystem: complement OpenFold, HelixFold3, and Chai‑1 to build an open science community.

From Model to Ecosystem

With Protenix deployed, AbSeekTM completes a full pipeline from sequence to structure and prediction to experiment. The model is not only a technical innovation but a transformation of the research ecosystem.

Going forward, we will continue to push:

• Improve antibody–antigen complex accuracy and optimize interface modeling.
• Strengthen experimental data integration for better understanding of real biological systems.
• Promote open scientific collaboration so more researchers can share, improve, and re‑create Protenix.

Try now: on the AbSeekTM Molecular Structure Prediction module, choose Protenix, upload sequences or complex files, and quickly generate high‑confidence predicted structures.

Open collaboration: we welcome universities, research institutes, and industry partners to explore innovative applications of Protenix in antibody drug discovery, structural biology, and multi‑modal molecular modeling.