Related tools
RFantibody
Structure-based de novo antibody and nanobody design pipeline combining antibody-tuned RFdiffusion, ProteinMPNN sequence design, and antibody-tuned RoseTTAFold2 filtering.
ODesign
All-atom generative AI for designing protein binders. Specify target binding sites and generate diverse binding proteins with fine-grained control over interaction parameters.
ProGen2
ProGen2 is Salesforce Research's protein language model suite for prompt-based de novo protein sequence generation. It samples novel amino acid sequences from a plain-text context string using top-p sampling and temperature control.
BioPhi
Antibody humanization and humanness evaluation platform from Merck. Sapiens mode uses deep learning trained on the Observed Antibody Space (OAS) to humanize antibody sequences, while OASis mode evaluates humanness using 9-mer peptide search against human antibody databases.
BoltzGen
BoltzGen is a state-of-the-art AI model for designing protein and peptide binders against any biomolecular target. Using generative diffusion models, it creates novel binders (proteins, peptides, nanobodies) with nanomolar-level binding affinity.
ProFam
ProFam-1 is a protein family language model for family-conditioned sequence generation. Provide a protein family FASTA/MSA and generate new sequences with model likelihood scores for downstream ranking and screening.
EvoDiff
EvoDiff is a diffusion-based protein sequence generation framework from Microsoft Research. ProteinIQ currently wraps the EvoDiff-Seq OA_DM_38M model for unconditional protein generation, motif scaffolding, and user-sequence inpainting.
PocketFlow
PocketFlow is a structure-based molecular generative model that designs novel drug-like molecules within protein binding pockets. It uses autoregressive flow modeling with chemical knowledge to generate 100% chemically valid, highly drug-like compounds.
RFdiffusion
RFdiffusion is a state-of-the-art protein structure generation tool that uses diffusion models to design proteins de novo, create binders, scaffold motifs, and generate symmetric oligomers with atomic precision.
RFdiffusion 2
RFdiffusion2 is an atom-level enzyme active site scaffolding tool that generates protein scaffolds around your input motif. REQUIRES an input PDB structure containing the active site residues to scaffold. For ligand-aware design, ligands must be embedded in the input PDB as HETATM records.
What is DiffAb?
DiffAb is a deep generative model that designs antibody complementarity-determining regions (CDRs) using diffusion probabilistic models and equivariant neural networks. Published at NeurIPS 2022, it was the first deep learning method to generate antibodies explicitly targeting specific antigen structures, representing an early application of diffusion models to protein design.
The model jointly generates both sequences and 3D structures of CDR loops while maintaining physical constraints through equivariance—a mathematical property ensuring predictions remain valid under rotations and translations. This antigen-aware approach distinguishes DiffAb from prior methods that lacked structural context about the target.
How to use DiffAb online
ProteinIQ hosts DiffAb on GPU infrastructure, providing browser-based access to antibody CDR design without local installation or environment configuration.
Inputs
| Input | Description |
|---|---|
Antibody-Antigen Complex | PDB file or RCSB PDB ID. Must contain antibody heavy chain, light chain, and antigen. |
Heavy chain ID | Chain identifier for the antibody heavy chain (default H). Use the chain ID as it appears in your PDB file. |
Light chain ID | Chain identifier for the antibody light chain (default L). Use the chain ID as it appears in your PDB file. |
Design settings
| Setting | Description |
|---|---|
Design mode | Co-design single CDR: Generate sequence and structure for one CDR loop. Co-design multiple CDRs: Simultaneously optimize all heavy chain CDRs (H1, H2, H3). Fixed backbone: Design sequence only while keeping the backbone structure fixed. |
Target CDR | Which CDR loop to redesign. Options: HCDR1, HCDR2, HCDR3 (default), LCDR1, LCDR2, LCDR3. HCDR3 typically has the greatest impact on antigen binding specificity. |
Number of samples | How many design variants to generate (1–10, default 3). More samples provide better exploration of the design space but increase computation time. |
Random seed | Optional seed for reproducible results. Leave empty for random generation. |
Output
DiffAb returns designed antibody structures in PDB format with:
- Redesigned CDR sequences and 3D coordinates
- Predicted binding poses in the antibody-antigen complex
- Multiple design candidates ranked by model confidence
Download the PDB files to analyze structures in PyMOL, ChimeraX, or other molecular viewers.
How DiffAb works
DiffAb uses a diffusion probabilistic model that learns to denoise antibody CDR structures through iterative refinement. Starting from random noise, the model progressively generates both amino acid sequences and atomic coordinates that fit the binding site.
The neural network architecture is equivariant under SE(3) transformations (rotations and translations), meaning it treats spatial geometry consistently regardless of how the complex is oriented. This ensures physically realistic antibody conformations.
During training, DiffAb learned from antibody-antigen complexes in the Protein Data Bank, capturing patterns in how CDRs bind to different epitopes. The model can operate in three modes:
Co-design generates sequences and structures simultaneously, allowing backbone flexibility to optimize binding geometry.
Fixed backbone designs only the sequence given a predetermined structure, useful for optimizing existing antibody scaffolds.
Multi-CDR co-design jointly optimizes multiple CDR loops, capturing cooperative effects between adjacent loops that single-CDR design misses.
Interpreting results
DiffAb generates multiple design candidates. Evaluate them using:
- Visual inspection: Check for steric clashes, unnatural geometries, or poor packing against the antigen
- Energy calculations: Score designs with physics-based force fields like Rosetta or FoldX
- Developability filters: Screen for liabilities like aggregation-prone patches, deamidation sites, or glycosylation motifs
- Experimental validation: Express promising candidates and measure binding affinity via ELISA, SPR, or BLI
The model outputs are starting points for optimization, not final therapeutics. Most designs require experimental testing and iterative refinement.
Limitations
DiffAb maintains a rigid antigen structure during design. Real antibody-antigen interfaces often involve conformational changes upon binding that the model cannot capture.
The framework chain IDs must be specified correctly. If your PDB uses non-standard chain naming, the tool may fail to identify antibody regions properly.
Generated sequences may contain developability liabilities. The model optimizes for structural fit to the antigen, not manufacturability, stability, or immunogenicity. Additional filtering and engineering are typically required before therapeutic development.