Computational Antibody Papers
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2026
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2026-08-21
A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability
- binding prediction
- developability
- ngs
- Results from the antibody discovery competition, where ML methods faced off with experimental baselines.
- Participating organizations were evaluated across three tasks on a SARS-CoV-2 receptor-binding domain (RBD) dataset: (1) in silico affinity maturation by modifying non-HCDR3 CDRs using sequencing outputs, (2) affinity ranking of candidates within three HCDR3-clustered sequence datasets, and (3) out-of-library CDR optimization/design to generate novel high-affinity binders not present in the sequencing data.
- For lead optimization, top ML methods matched experimental performance by producing sub-100 pM binders (top ML design: 95 pM vs. best experimental control: 113 pM), though overall ML submissions underperformed relative to experimental sorting libraries. Notably, a simple non-ML statistical consensus baseline also achieved a highly competitive 540 pM affinity without using machine learning.
- For sequence ranking within HCDR3 clusters, almost all ML algorithms performed worse than random clone picking; only 9.8%-13.8% of ML submissions beat the cluster baseline clone compared to 39% from random selection. Additionally, out-of-library ML designs yielded a high proportion of non-binders (30.4%) and top-ranked affinity hits frequently suffered from severe biophysical developability failures.
- ML is genuinely useful for timeline reduction during lead optimization and affinity maturation, replacing 2-3 weeks of physical combination library construction with an in silico step when provided with deep, biologically grounded experimental datasets.
- Pretrained protein language models (PLMs) paired with structure-aware modules (e.g., pairformers) showed utility in exploring novel out-of-library sequence spaces. However, current ML models lack cross-task generalization, struggle with local epistasis, and fail to reliably co-optimize affinity alongside multiparametric biophysical developability.
2026-08-10
Analysing open-source protein folding models for nanobody binding prediction
- binding prediction
- nanobodies
- ngs
- Benchmarking the ability of co-folding models to distinguish nanobody binders and non-binders.
- Evaluated four state-of-the-art structure prediction models (AlphaFold3, Boltz-2, Chai-1, and IntFold) across true binder ranking, out-of-distribution (OOD) sequence detection, and mutational sensitivity in nanobody–antigen complexes.
- Discovered that no single confidence score excels across all tasks: while Boltz-2 achieved the highest median accuracy for true binder identification, local metrics like PLDDT (particularly in AlphaFold3) were far superior at catching OOD alanine-substituted sequences.
- Contributed a original in vivo camelid immunization dataset targeting CD33, revealing that all evaluated models struggle to generalize when discriminating enriched binders from realistic immune repertoire background sequences.
- Demonstrated that commonly used hard filtering thresholds (e.g., pAE < 10) can erroneously discard up to 75% of true binders, underscoring the need to combine complementary global interface and local CDR metrics rather than relying on single confidence scores.
2026-08-10
High-throughput machine learning-aided antibody discovery for cell surface antigens
- databases
- experimental techniques
- ngs
- ML-Optimized Paratope Representation (ARM).
- The authors engineered a synthetic yeast-display Fab library centered on a compact "Antigen Recognition Module" (ARM), a <100 nt sequence pairing heavy-chain CDRH3 sequence diversity with a light-chain barcode, providing a computationally lightweight paratope representation tailored for high-throughput sequencing and ML workflows.
- To overcome physical display biases (such as yeast growth rates or expression disparities), a k-mer-based logistic regression (LR) model was trained on sequence enrichment data from early sorting rounds (MACS to FACS1) to score clones based on sequence motifs.
- Applying the LR model successfully rescued functional, high-affinity binders for ROBO2N (11 binders) and PD-L2 from early sequence pools that had been depleted or overshadowed by dominant clones in later experimental cell-sorting rounds.
- The LR model trained on ROBO2N accurately predicted binding potency and epitope cluster preference for the closely related paralog ROBO1, while reliably scoring non-binding clones as low probability.
- The paper provides a publicly available dataset of over 68,000 unique target-associated ARM sequences alongside comprehensive biophysical characterizations for 486 antibodies, offering a structured foundation for downstream predictive models, affinity maturation, and zero-shot antibody design.
- A Unified Framework for Unsupervised AIRR Analytics
- immuneML introduces the first standardized environment to discover patterns, cluster sequences, and run robust stability validations on partially or imperfectly labeled adaptive immune receptor data.
- The platform systematically evaluates and compares generative machine learning models (such as LSTM and VAE) to determine how effectively they can engineer novel, antigen-specific immune sequences versus simply memorizing training data.
- It rigorously assesses how well different data representations, including advanced protein language models, capture true biological properties like epitope specificity and MHC restrictions, a utility proven on 48,000 experimental TCRβ sequences.
- It provides vital exploratory and dimensionality reduction tools to identify sequencing batch effects and data biases before running supervised diagnostics, demonstrated using a real-world single-cell dataset from 143 inflammatory bowel disease patients.
- Novel experimental and computational pipeline designed to characterize nanobody immune repertoires following immunization and phage display selection - NanoMAP.
- It introduces a flexible clustering method that identifies clonal families by grouping sequences with similar V/J segments and CDR lengths, then applying a unique merging step that allows for minor CDR variations.
- When benchmarked against MMseqs2 and Immcantation (SCOPer), NanoMAP scored higher on computational metrics (Silhouette, phenotypic quality, and stability) and showed better alignment with expert-curated "ground truth" labels.
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.