Computational Antibody Papers
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2025
TitleKey points
2025-11-17
BLOSUM Is All You Learn — Generative Antibody Models Reflect Evolutionary Priors
- generative methods
- binding prediction
- Benchmarking of 'old-school' substition matrices to see whether they can guide affinity maturation of antibodies.
- they took several datasets wherte the parental antibody is known and the mutants affinities are known as well
- they compared a diffusion based model, inverse folding and the substitution matrices.
- they note that blosum performs surprisingly well.
- 'training' the substitution matrix on the mutants with respect to the wild type yields better results than making it off sabdab or oas.
2025-11-17
Machine learning approaches for interpretable antibody property prediction using structural data
- binding prediction
- Description of two models for antibody property prediction , ANTIPASTI (CNN on structural correlation maps for affinity) and INFUSSE (Graph + ProtBERT hybrid for flexibility).
- ANTIPASTI predicts antibody–antigen binding affinity; INFUSSE predicts residue-level B-factors (flexibility).
- Both tested on curated antibody and antibody-antigen datasets (no new wet-lab validation, only structural data).
- B-factor prediction links sequence, structure, and local dynamics-showing that antibody flexibility is partly learnable from data. Trained only on antibody/antigen data and outperforms a baseline trained on generic proteins.
2025-09-30
mBER: Controllable de novo antibody design with million-scale experimental screening
- binding prediction
- generative methods
- protein design
- experimental techniques
- Novel de novo antibody design method with massive experimental testing.
- The computational method involves integration, not retraining, of existing tools. It combines AlphaFold-Multimer, protein language models (ESM2/AbLang2), and NanoBodyBuilder2 with templating/sequence priors to design/filter antibody-format binders.
- They perform massive testing. >1.1 million VHH binders designed across 436 targets (145 tested); ~330k experimentally screened.
- Hit rates look low per binder (~0.5–1%) but that’s ~50× above random libraries, and still yields thousands of validated binders.
- Target-level success is 45%, for how many targets we got binders; some epitopes reached 30–38% hit rates after filtering.
- The big caveat is the specificity of epitopes- it really makes a difference, with some epitopes producing nought.
2025-09-12
RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery
- binding prediction
- developability
- Combined in vitro/in silico method for optimization of binders.
- Start from a wild-type scFv (heavy chain), build a random-mutant library, FACS-sort on multiple antigens, deep-sequence bins + input, and use per-sequence enrichment (bin/library) as the supervised target for (antibody, antigen) training pairs.
- Train uncertainty-aware regressors (xGPR or ByteNet-SNGP) on those enrichment targets; run in-silico directed evolution (ISDE) from the WT, proposing single mutations and auto-rejecting moves with high predictive uncertainty while optimizing the worst-case score across antigens.
- Binding is protected by the multi-antigen objective + uncertainty gating during ISDE; risky proposals are discarded before they enter the candidate set.
- Filter candidates for humanness with SAM/AntPack and for solubility with CamSol v2.2 (framework is extensible to add other gates); final wet-lab set kept 29 designs after applying these filters and uncertainty checks.
- Beyond large in-silico tests, yeast-display across 10 SARS-CoV-2 RBDs shows most designs outperform WT; a representative clone (Delta-63) improves KD on 8/10 variants and competes with ACE2.
2025-07-18
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
- binding prediction
- Introduced GAMA, an attribution approach for autoregressive LSTM generative models that pinpoints which sequence positions drive binding in a one-antigen–many-antibody setting
- Benchmarked on 270 synthetic motif-implant datasets and simulated binder sequences from the Absolut! framework across multiple antigens, then applied to an experimental set of 8,955 Trastuzumab CDRH3 variants binding HER2
- On the Trastuzumab-HER2 dataset, GAMA flags CDRH3 positions 103, 104, 105, and 107 as most critical—overlapping three of the four crystallographically determined paratope residues
- Introduced a novel model, Chai-2, that shows over 100× improvement in de novo antibody design success rates compared to prior methods.
- The model is prompted with the structure of the target, epitope residues, and desired antibody format (e.g., scFv or VHH).
- Benchmarking was performed on 52 antigens that had no known antibodies in the PDB, ensuring evaluation on novel, unbiased targets.
- Generated antibodies were structurally and sequentially dissimilar to any known antibodies, indicating that Chai-2 designs novel binders, not memorized ones.
- For VHH (nanobody) formats, the model achieved an experimental hit rate of 20%, validated in a single experimental round.
2025-06-24
Benchmark for Antibody Binding Affinity Maturation and Design
- binding prediction
- language models
- generative methods
- databases
- Benchmark of machine learning models for antibody-antigen binding affinity.
- A curated dataset of over 150,000 antibody-antigen complexes with associated experimental affinity values is compiled from literature.
- The benchmark compares a wide range of model types: language models, inverse folding models, graph-based, and diffusion-based generative models.
- Inverse folding models that are globally structure-aware perform best.
- General protein models like ESM-IF and ProteinMPNN outperform antibody-specific models such as AntiFold, DiffAb, and dyMEAN.
- Surprisingly, ESM-3 underperforms relative to ESM-IF, despite incorporating structural signals and improving upon earlier ESM models.
2025-06-24
AbEpiTope-1.0: Improved antibody target prediction byuse of AlphaFold and inverse folding
- binding prediction
- structure prediction
- A novel method that repurposes AlphaFold-2.3 structure predictions and combines them with inverse folding–based machine learning models to assess antibody-antigen binding accuracy and specificity.
- They generate antibody-antigen complex models using AlphaFold-2.3 and evaluate them using the 'AbAgIoU' metric, which measures the overlap between predicted and true epitope/paratope residues — penalizing both missing and extra contacts.
- They demonstrate that the learned scores can distinguish true from incorrect antibody-antigen pairings (including swapped antibody scenarios), significantly outperforming random baselines.
- The method relies only on antibody and antigen sequences as input, using AlphaFold to model structures — making it applicable in real-world settings where experimental structures are unavailable.
2025-06-24
NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores
- binding prediction
- nanobodies
- Introduced a novel machine learning method (NanoBinder) to predict the binding probability of nanobody-antigen structural complexes.
- Positive (binding) complexes were sourced from the SAbDab database, which contains experimentally validated nanobody-antigen interactions.
- Negative (non-binding) complexes were generated by structurally aligning nanobodies from different binding complexes (with RMSD < 2 Å) and recombining them with unrelated antigens to create likely non-binding pairs.
- Extracted Rosetta energy features from each complex and trained several machine learning models, including Random Forests, SVMs, AdaBoost, and Decision Trees, to classify binders vs. non-binders. Random Forests showed the best performance.
- They selected antibodies with known antigen targets (e.g., IL-6) and grafted their CDRs onto nanobody scaffolds using Rosetta-based protocols. The resulting nanobody-antigen complexes were evaluated in silico using NanoBinder, and selected candidates were experimentally validated. The predictions showed good correlation with binding outcomes, particularly for identifying non-binders.
- Novel LLM (MINT) that natively encapsulates protein protein interactions.
- MINT (Multimeric INteraction Transformer) extends the ESM-2 protein language model by incorporating a cross-chain attention mechanism. This allows it to process multiple protein sequences simultaneously while preserving inter-sequence relationships and contextual information critical for modeling protein-protein interactions.
- MINT was trained on a large, curated subset of the STRING database, consisting of 96 million high-quality physical protein-protein interactions and 16.4 million unique protein sequences. The training employed a masked language modeling objective adapted for multimeric inputs.
- MINT was benchmarked on several general protein interaction tasks including binary interaction classification, binding affinity prediction (PDB-Bind), and mutational impact prediction (e.g., SKEMPI and MutationalPPI). It consistently outperformed existing PLMs, achieving state-of-the-art performance on multiple datasets such as a 29% improvement over baselines in SKEMPI.
- MINT outperformed antibody-specific models (e.g., IgBert, IgT5, and AbMap) on the FLAB benchmark and SARS-CoV-2 antibody mutant binding prediction tasks. It showed >10% performance improvement on three FLAB datasets and a 14% gain in low-data settings (0.5% training data) for SARS-CoV-2 binding predictions.