Computational Antibody Papers
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TitleKey points
- Measuring the effects of protein energetics versus actual protein-protein binding.
- The authors used AlphaSeq to measure 7,185 single and double mutations across four VHH-antigen complexes to capture changes in observed affinity.
- By using "control VHHs" that bind to non-overlapping epitopes, they successfully separated "protein-quality" (folding/stability) effects from true "protein-interaction" (interface) changes.
- The study found that 83.6% - 98.9% of antigen mutations negatively impact affinity, primarily by degrading the protein's overall quality rather than disrupting specific interface energetics.
- Benchmarking showed that sota models like ESM-IF1 and ThermoMPNN are effective at predicting protein-quality changes but struggle to accurately predict specific protein-interaction effects.
2026-04-03
AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
- binding prediction
- Case study of training of an affinity prediction algorithm on anti-sars-cov-2 antibodies.
- Authors fine-tuned a BERT-based model, Ab-Affinity, specifically to predict the binding affinity of antibodies against the SARS-CoV-2 spike protein.
- They utilized a dataset of 71,834 unique antibodies (preprocessed from 104,972 variants) derived from three parental "seed" binders with experimentally measured affinities.
- The model employs a BERT-based encoder (specifically ESM-2) with an added fully connected regression layer to predict continuous binding scores.
- Ab-Affinity achieved higher Pearson and Spearman correlation coefficients on the test set than existing LLM-based methods like DG-Affinity, ESM-2, and AbLang. But the baselines were not fine tuned on their data.
2026-03-13
Contrastive learning for antibody-antigen sequence-to-specificity prediction
- binding prediction
- CALM, a "sequence-native" foundation model that maps antibody and antigen primary sequences without requiring structural inference.
- CALM employs modality-specific encoders (AntiBERTy for antibodies, ESM-2 for antigens) to align cognate pairs in a shared embedding space using cosine similarity.
- Authors evaluate performance by the model's ability to pick the correct partner from a candidate pool in both directions ab->ag, ag->ab.
- Calm uses optional structural masks to restrict inputs to paratope and epitope residues, which significantly reduces sequence noise and improves accuracy (but clearly needs a structure).
- CALM achieves Top-1 of 2% in strict out-of-distribution tests, representing a 3x to 46x improvement over random baselines despite a low-data regime.
- They lay out an autoregressive decoder for de novo design, though this generative component was not trained or tested in this study.
2026-03-13
Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores
- binding prediction
- nanobodies
- structure prediction
- The authors evaluated AlphaFold3, Boltz-2, and Chai-1 on their ability to distinguish cognate (correct) nanobody-antigen pairs from incorrect, non-binding pairings.
- They used 106 experimental complexes and generated a combinatorial matrix of 11,132 shuffled non-cognate pairings to serve as ground-truth "incorrect" decoys.
- Internal confidence scores (specifically ipTM) were very weakly predictive of true binding. In terms of Average Precision (PR-AUC), AF3 performed best, followed by Chai-1 and then Boltz-2.
- Increased sampling improves structural geometry but does not help models "select" the correct binder. Most quality gains occur within 10–25 samples; deeper sampling primarily increases the number of plausible-looking false positives.
- 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
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.
2026-02-05
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.
2026-01-20
Machine learning enables efficient and effective affinity maturation of nanobodies
- ngs
- binding prediction
- Novel framework that identifies high-affinity leads using data from only a single round of FACS, significantly reducing the labor and reagents required for traditional multi-round affinity maturation campaigns.
- Models were trained using log enrichment ratios (continuous) or binary labels (enriched vs. depleted), calculated by normalizing post-sorting FACS abundance against pre-sorting MACS abundance to account for expression biases.
- They benchmarked linear/logistic regression and CNNs against a semi-supervised ESM2-MLP approach ; notably, the linear models often outperformed deeper architectures in ranking validated substitutions and offered superior interpretability for identifying confounding signals like polyreactivity.
- By generalizing information across all sequences, ML models effectively separated "affinity-driving" mutations from "passenger" substitutions, identifying sub-nanomolar binders that were not prioritized by traditional, more laborious raw sequencing count analysis.
- The best-performing models were leveraged within a Gibbs sampling protocol to design novel sequences unseen in the original experiment, ultimately yielding multiple improved binders with up to a ~2500-fold affinity increase over the wild-type.
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.