Computational Antibody Papers
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2025
TitleKey points
2025-10-28
Germline-aware deep learning models and benchmarks for predicting antibody VH–VL pairing
- developability
- ngs
- Introduced a novel pairing predictor for VhVl chains with a clever strategy to sample negative pairs.
- Defines three negative sampling strategies:
- Random pairing, where heavy and light chains are shuffled without constraints.
- V-gene mismatching, where non-native pairs are generated by combining VH and VL sequences drawn from different V-gene families, but within biologically plausible V-gene segments. This captures realistic but unobserved combinations that could occur during recombination.
- Full V(D)J mismatching, where heavy and light chains are paired using completely distinct germline origins across V, D, and J gene segments. This produces negative examples that are maximally diverse yet biologically meaningful, reflecting combinations never seen in natural repertoires.
- Shows that the space of possible VH–VL germline combinations is far larger than what is observed in public datasets, revealing non-random biological constraints on pairing.
- Demonstrates that models trained on V-gene and especially VDJ mismatched datasets achieve the highest and most generalizable performance, outperforming existing methods such as ImmunoMatch, p-IgGen, and Humatch — confirming that biologically grounded negative sampling is key to robust VH–VL pairing prediction.
2025-09-30
Advancing large-molecule discovery with a unified digital platform for data analysis and workflow management
- experimental techniques
- ngs
- annotation/numbering
- server/webapp
- Review of currently available large scale software for antibody analysis.
- Today’s biologics R&D is slowed by fragmented tools and manual data wrangling; the paper proposes a unified, open-architecture platform that spans registration, tracking, analysis, and decisions from discovery through developability.
- Key components are end-to-end registration of molecules/materials/assays; a harmonized data schema with normalized outputs; automated analytics with consistent QC; complete metadata capture and “data integrity by design.”
- The platform should natively interface with AI, enable multimodal foundation models and continuous “lab-in-the-loop” learning, and support federated approaches to counter data scarcity while preserving privacy.
- Dotmatics, Genedata, and Schrödinger each cover pieces (e.g., LiveDesign lacks end-to-end registration), and the authors stress regulatory-ready features.
2025-07-03
Developing drug-like single-domain antibodies (VHH) from in vitro libraries
- experimental techniques
- ngs
- Novel library design technique for VHHs that produces developable and humanized antibodies without the need for further optimization.
- The authors built a humanized VHH phage display library using four therapeutic VHH scaffolds, incorporating CDR1 and CDR2 sequences from human VH3 germline genes (filtered for sequence liabilities) and highly diverse CDR3s from CD19⁺ IgM⁺ human B cells.
- CDR1 and CDR2 libraries were filtered via yeast display for proper folding and protein A binding, while CDR3s were refined to remove poly-tyrosine stretches to reduce polyreactivity.
- An improved library version incorporated CDR1/2 variants selected for heat tolerance and further depleted CDR3s with poly-tyrosine motifs, increasing stability and developability.
- VHHs were tested for expression, thermal stability, aggregation, hydrophobicity, and polyreactivity, showing that the V2 library yielded a higher proportion of drug-like antibodies with favorable biophysical properties.
2025-02-20
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
- generative methods
- ngs
- Novel generative antibody method, CloneLM/CloneBO, following clonally plausible evolutionary paths.
- They train CloneLM, an autoregressive language model, on antibody clonal family data from the OAS. There were two separate models for heavy and light sequences. They use FastBCR to call clonal families.
- CloneLM generates new clonal families by conditioning on a given antibody sequence. They use a martingale posterior approach to ensure sampled sequences follow plausible evolutionary paths. So it takes antigen into account, but only by the virtue of the clonal family.
- For benchmarking they train a language model oracle on a real human clonal family and use it as a simulated fitness function.
- They further perform training on affinity and stability data to generate oracles for these and show that the newly generated sequences can be made to be more stable/have higher affinity.
2025-02-20
ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains
- developability
- ngs
- Method to predict heavy light chain pairing.
- Heavy light chain pairing has long been posited to be random, or at the very least VERY promiscuous. Authors check that via training their model on different portions of the variable region and showing that there is signal where full sequences are used.
- Authors curated a set of ca. 233k positive heavy/light chain pairs from OAS. Negative samples were made by random shuffling - so they could occur in nature, just were not observed in this ds.
- They use Antiberta2 as a basis for training the classification model.
- The model achieves 0.75 and 0.66 ROC AUC on two test sets - so there seems to be some signal there.
- When the model is split between lamdbas/kappas, it does better - though lambda have signal for kappas (remember that lambda is a rescue rearrangement for not-working kappa).
- Naive B-cell pairs have less predictability than mature ones.
2025-01-21
Antibody affinity engineering using antibody repertoire data and machine learning
- binding prediction
- ngs
- Novel experimental/computational workflow that demonstrates how little data might be needed to develop antibody affinity predictors.
- Mice were immunized with hen egg white lysozyme and via computational procedure of clustering with known binders 35 antibodies were characterized together with their affinities.
- These 35 antibodies were used to train the methods: Gaussian Process (GP) models with Matern and RBF kernels, Kernel Ridge Regression (KRR), Random Forest (RF) and Linear Regression (used as a baseline).
- Seed sequences were point or double-mutated and their affinity predicted using GP (that performed the best). Eight mutants predicted to span the whole range of affinities were selected for experimental testing and they had very good agreement with the predictions.