Computational Antibody Papers
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TitleKey points
- BindCraft is an easy-to-use pipeline for computational protein binder design.
- It employs AlphaFold2-Multimer to hallucinate binders via backpropagation.
- Given a target structure and binder parameters (e.g., sequence length), the binder sequence is initialized with random logits and iteratively optimized via gradient descent through the AF2-Multimer network.
- After binder hallucination, the sequence and surface residues are further optimized using MPNNsol, and AF2-Monomer is used to repredict and filter high-confidence designs.
- Binder designs were validated experimentally through in vitro assays, X-ray crystallography, and cryo-EM.
- Reported success rates ranged from 25% to 100%, with most binders in the nanomolar affinity range, a few in the micromolar range, and backbone RMSDs of ~1.7 Å to 3.1 Å between design models and solved structures.
2025-04-28
Atom level enzyme active site scaffolding using RFdiffusion2
- protein design
- non-antibody stuff
- Improvement upon earlier RFDiffusion, enhancing stability and accuracy in designing enzyme active sites.
- Catalytic sites can now be specified at the atomic level instead of the residue backbone level used previously. This eliminates the need to explicitly enumerate side-chain rotamers.
- Training uses flow matching, a technique that simplifies and stabilizes the diffusion training process.
- Benchmarked on a set of 41 diverse enzyme active sites; RFdiffusion2 succeeded in all 41 cases, significantly outperforming the earlier RFDiffusion, which succeeded in only 16.
2025-04-28
Scaling unlocks broader generation and deeper functional understanding of proteins
- protein design
- non-antibody stuff
- Novel protein generative language model — ProGen3
- The model can do autoregressive generation N-to-C, C-to-N, and also supports span infilling.
- The architecture is a Transformer with a Sparse Mixture of Experts (MoE), activating about 27% of parameters per forward pass to improve computational efficiency.
- They studied how sampling affects training by trying different family-level weighting schemes. Uniform sampling across families (where small and large families have equal chance) gave better diversity and generalization, while unmodified sampling (letting big families dominate) performed worst.
- They validated the models by showing that generated proteins express well in wet lab experiments (split-GFP assays, spanning both highly novel and moderately novel sequence spaces).
- They used a large thermostability dataset to align model predictions to stability. This alignment is not standard fine-tuning — instead, preference optimization was applied, teaching the model to prefer sequences predicted to have higher stability. Upon experimental validation, aligned models indeed produced proteins with higher expression and stability.
2025-03-31
Precise, Specific, and Sensitive De Novo Antibody Design Across Multiple Cases
- protein design
- Benchmarking of a proprietary antibody design algorithm.
- The method generates novel antibodies against a target, for a specific epitopic constraint & can be used to re-design antibodies.
- Altogether they find good affinity scfv binders for six targets for which they found a complex in the PDB, like PD1 and Her2.
- The de novo antibody design methods were computationally benchmarked against a curated set of 32 experimentally resolved antibody–antigen complexes using metrics like the G-pass rate and orientation recovery (measured by Fw RMSD). This allowed the authors to compare their method (across different versions) against other approaches.
- They compare against RFAntibody and dyMEAN but in the computational tasks - reproducing the orientation of an existing antibody.
- Several rounds of biopanning are employed to enrich for high-affinity, target-specific binders from a pre-designed library and do not involve the introduction of new mutations.
- They benchmark the developability properties such as monomericity, yield and polyreactivity to show that their antibodies have good properties.
- They demonstrate that most of their designed binders have less than 50% H3 sequence identity to antibodies in the PDB.
- Computational details and binders are not given.
2025-03-21
NanoDesigner: Resolving the complex–CDR interdependency with iterative refinement
- protein design
- nanobodies
- Novel pipeline for computational protein design of nanobodies
- Several tools are collated and adjusted to nanobody case - IgFold for structure prediction, HDOCK for docking and ABDESIGN, DiffAb and dyMEAN for backbone/sequence prediction.
- They chiefly perform computational validation showing the performance on the RMSD/DockQ (re-docking) and the amino acid recovery. Results indicate that focusing on nanobodies provides benefit.
- The entire pipeline can be used for de novo design and optimization.
2025-03-21
NanoGen: A High-affinity Nanobody Generation Model with Guided Diffusion
- protein design
- nanobodies
- Novel method for nanobody sequence re-design using quite a small network.
- The model was pre-trained using a large-scale collection of nanobody sequences from the INDI dataset, heavy-chain antibody sequences from the OAS, and antibody complex structures from SabDab. For fine-tuning, affinity data was generated by with 17,500 nanobody–antigen interaction data points—7,500 generated via the ANTIPASTI model and 10,000 through random pairing—with a CD45 patent dataset used for testing. So all computational predictions are not real affinity points.
- NanoGen uses a two-stage training framework with a shared encoder-decoder architecture based on CNN layers that learns sequence patterns via a Masked Language Modeling task. During generation, a guided discrete diffusion process, augmented with Discrete Bayesian Optimization, is employed to refine the sequence outputs for enhanced binding affinity.
- The model was tested using sequence recovery (REC) and binding affinity improvement (pKD improvement). Benchmarking involved comparing NanoGen against baseline models such as ESM-2 650M, AbLangHeavy, and nanoBERT under both random masking and CDR-specific masking strategies on the CD45 patent dataset.
2025-03-21
How well do contextual protein encodings learn structure, function, and evolutionary context?
- generative methods
- protein design
- Novel inverse folding algorithm, studying the effect of pretraining on the effectiveness of Antibody design
- Authors check multiple inverse folding regimens, pretraining on general proteins, ppi interfaces and antibody-antigen interfaces and likewise finetuning on these.
- They only use the backbone atoms (N,C,Ca), with special provisions for Cb.
- They mask portion of the sequence and have the model guess its amino acids.
- The 37% recovery at 100% masking appears slightly lower than the same feat for proteinMPNN.
- Pretraining on antibodies still holds a signal towards antibody-antigen complexes, showing the power of such pre-training.
- One of the first studies showing that introducing structure to protein language models, improves the predictive ability.
- They fed ProteinMPNN (structural) inputs to ESM-1B to show that it improved recovery as opposed to using ESM-1B mask alone.
- To marry ProteinMPNN and ESM-1B they use an ‘adapter’. Adapters in machine learning are lightweight modules that modify or extend a model’s functionality without retraining all parameters; in LM-DESIGN, a structural adapter integrates structural information into protein sequence predictions by bridging the structure encoder and a pretrained language model (pLM).
- LM-DESIGN benchmarked against state-of-the-art protein inverse folding models, including ProteinMPNN, PiFold, GVP-Transformer, Structured Transformer, and GVP, while utilizing pretrained language models such as ESM-1b 650M and the ESM-2 series.
- LM-DESIGN was evaluated on CATH 4.2 and CATH 4.3 datasets using sequence recovery rates and perplexity, compared against baselines.
- LM-DESIGN outperformed individual models, improving sequence recovery by 4-12% points, surpassing ProteinMPNN and PiFold.
2025-02-17
IgGM: A Generative Model for Functional Antibody and Nanobody Design
- protein design
- generative methods
- Novel method to design antibodies de novo.
- Architecturally, it is a mix of language models, diffusion and structure prediction methods.
- Training happens by noising diffusion, firstly perturbing structure and making the model get it right and afterwards doing the same thing for sequences.
- After these two steps the model is distilled into a consistency model. This results in a model that can get the final coordinates/sequence in a single step rather than iterative denoising.
- Method achieves comparable accuracy to many methods out there, such as DiffAb, dyMEAN and others.
- On docking, the best performance is in the order of 4A iRMSD when using an AlphaFold3 antibody model - so still some challenges remain.
- No wetlab validation.
2025-02-03
Benchmarking Inverse Folding Models for Antibody CDR Sequence Design
- generative methods
- protein design
- nanobodies
- Benchmarking of sequence design methods that are structure-conditioned
- ESM-IF, LM-Design, ProteinMPNN and AntiFold were benchmarked.
- On sequence recovery, AntiFold beats others on antibodies, but LM-Design is better when VHHs are considered.
- AntiFold makes minimal use of the antigen information.
- ESM-IF and ProteinMPNN have some weak correlation with affinity data.