Computational Antibody Papers
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TitleKey points
- Characterization of binding hot spots on 50 high-resolution antibody-antigen complexes from the ABAG-Docking benchmark.
- FTMap Algorithm: FTMap identifies these spots by docking 16 small organic probes using a Fast Fourier Transform (FFT) approach. It clusters the best poses and identifies ‘consensus sites’ where multiple probe types overlap, indicating regions that contribute disproportionately to binding energy.
- Aromatic residues on the paratope drive hot spot formation, particularly Trp, Tyr, and His, along with Phe. Trp and Tyr are especially critical on both sides of the interface due to their combined hydrophobic and polar (amphiphilic) character.
- Hot spots are more concentrated on the paratope than the epitope, supporting the idea that antibodies primarily drive these interactions
2026-02-26
Disulphide and sequence-encoded conformational priors guide nanobody structure prediction
- nanobodies
- structure prediction
- Authors propose a new method to train a nanobody structure predictor by using ‘blueprints’.
- They developed a classifier (NbFrame) to identify whether the HCDR3 loop adopts a kinked (framework-contacting) or extended (solvent-exposed) conformation. This allows the model to use sequence-encoded priors and explicit constraints during the folding process.
- The model itself is very lightweight and runs significantly faster than "heavy" models like AF or Boltz. NbForge achieves sub-second inference speeds, predicting structures in less than a second on both CPU and GPU. In comparison, models like AlphaFold3 or Boltz1 typically require tens of seconds to minutes per structure - MSA is a different story altogether.
- The model matches the HCDR3 prediction quality of heavy models while being much more efficient. While AF3 and Boltz1 are more accurate at modeling the rigid framework, NbForge achieves parity in the hypervariable HCDR3 region—the part most critical for binding. Its speed and high recovery of disulphide bonds make it ideal for triaging millions of candidates in large-scale discovery campaigns.
2026-02-26
Simple computational methods can outperform deep learning in designing diverse, binder-enriched antibody libraries
- binding prediction
- An update to the “Baselning the buzz” paper.
- Previously authors got a huge dataset of 500,000 anti-Her2 trastuzumab CDR-H3 highbinders/weakbinders/nonbinders.
- Here, authors tested 140 designs of Trastuzumab H3, from Ablang, ProteinMPNN ESM2 and the good old Blosum. These were filtered among others using the CNN predictor
- Blosum does the best as judged by SPR of the designs, but authors note that its designs have the biggest overlap with the training set.
- In contrast, more complex methods like AbLang, ESM-2, and ProteinMPNN were found to explore different, more diverse areas of the sequence space. This means they generated sequences that were more "distal" (further away) from the original DMS-informed data.
2026-02-26
Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization
- developability
- Results of the Gingko developability competition.
- Benchmarked 113 teams on predicting five key developability traits: hydrophobicity, thermostability, self-association, expression titer, and polyreactivity.
- Models were trained on the GDPa1 dataset (246 antibodies) and blindly tested on GDPa3 (80 diverse antibodies from OAS).
- While cross-validation (CV) results were promising, performance plummeted on the test set e.g., self-association dropped from a 0.653 CV Spearman's rho to 0.356.
- Hydrophobicity was the most predictable (rho = 0.708), while expression titer was the most challenging (rho = 0.310).
- Winning models varied by assay; for example, team AbDevelop won for self-interaction, while microcrisprtm2 led in thermostability.
2026-02-16
AbTune: layer-wise selective Fine-Tuning of protein language models for Antibodies
- structure prediction
- A strategy for layer-wise selective fine-tuning of general protein language models.
- Instead of full fine-tuning, they found that adapting only the first 50-75% of layers via LoRA provides optimal performance while saving computational costs.
- For example, they perform sequence-specific "test-time" training where they optimize the model using a Masked Language Modeling (MLM) objective on the target sequence itself before predicting its properties. This approach led to a 18.4% accuracy boost in predicting the notoriously difficult CDR-H3 antibody loop
2026-02-16
Surpassing AlphaFold3 via Architectural Refinement and Structural Consistency
- structure prediction
- New open source reproduction of AlphaFold3 that either matches or surpasses it.
- IntelliFold-2-Pro achieves a success rate of 58.2% (DockQ > 0.23 so about 4A irmsd) on antibody-antigen interactions, outperforming AlphaFold 3's 47.9%.
- For small molecule co-folding, IntelliFold-2-Pro reaches 67.7%, surpassing AlphaFold 3’s 64.9%.
- Interface Precision vs. Monomers: IntelliFold-2 shows marginal gains in protein monomer accuracy (LDDT of 0.89 vs AF3's 0.88).
- ‘Evolution’ of AlphaFold from Isomorphic labs.
- IsoDDE achieves 39% accuracy in high fidelity regime ((DockQ > 0.8)) which corresponds to near-experimental precision with an interface RMSD (iRMSD) typically below 1.0Å. That’s a 2.3x improvement over AF3.
- Using a single model seed, IsoDDE successfully predicts 63% of interfaces DockQ > 0.23 (correlating to an iRMSD$ of roughly 4.0Å or less, which is a 1.4x improvement over AF3's single-seed performance.
- IsoDDE accurately models the backbone of the highly variable CDR-H3 loop for 70% of antibodies (<2Å) in the test set, outperforming AF3’s success rate of 58% 1.2x.
- When scaled to 1,000 seeds, IsoDDE reaches an 82% success rate for correct interfaces and 59% for high-accuracy predictions. So to get results one cannot exactly do it on a laptop.
- It is a technical report, architecture is not discussed.
2026-02-05
Biophysically Grounded Deep Learning Improves Protein–Protein ΔΔG Prediction
- binding prediction
- non-antibody stuff
- Method addressing binding prediction strength training on low data noisy dataset.
- The researchers address the issue that the field's standard benchmark, SKEMPI2, has significant hidden data leakage where different protein complexes share over 99% sequence identity, leading to inflated performance estimates in models that simply memorize these patterns. Problem raised by many, addressed by hardly any.
- ProtBFF injects five interpretable physical priors, Interface, Burial, Dihedral, SASA, and lDDT, directly into residue embeddings using cross-embedding attention to prioritize the most structurally relevant parts of a protein.
- By evaluating models on stricter, homology-based sequence clusters (60% similarity), the authors proved that ProtBFF allows general-purpose models like ESM to match or outperform specialized state-of-the-art predictors, even in data-limited "few-shot" scenarios.
2026-02-05
Multiple protein structure alignment at scale with FoldMason
- non-antibody stuff
- structure prediction
- Protocol for ultra fast protein structure alignment.
- FoldMason represents protein structures as 1D sequences using a structural alphabet (3Di+AA), which allows it to perform multiple alignments using fast string comparison algorithms and a parallelized progressive alignment following a minimum spanning tree.
- It operates two to three orders of magnitude faster than traditional structure-based methods, achieving a 722x speedup over tools like MUSTANG and scaling to align 10,000 structures in a fraction of the time required by competitors for just 100.
- It matches the accuracy of gold-standard structure aligners and exceeds sequence-based tools, particularly in aligning distantly related proteins or flexible structures that global superposition-based methods struggle to handle.
- It is used for large-scale structural analysis of massive databases like AlphaFoldDB, building structure-based phylogenies for proteins that have diverged past the "twilight zone" of sequence similarity, and providing interactive web-based visualizations of complex MSTAs
- First fully open-source reproduction of the diffusion-based AlphaFold3 architecture that matches or exceeds its performance while strictly adhering to the same training data cutoff and model scale (especially on antibodies!).
- Unlike previous open-source models, it exhibits a consistent improvement in accuracy as more computational budget is allocated (you sample more).
- Protenix-v1 beats others in antibody-antigen interface prediction, outperforming AlphaFold3 52.31% vs. 48.75% success rate (dockq better than .23). That is nearly doubling the accuracy of open-source like Chai-1 23.12%.