Computational Antibody Papers
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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
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
Iterative Epitope Expansion Enables Structure-Guided de novo Design of IgE-Binding Miniproteins Targeting the FcεRI Interface
- non-antibody stuff
- protein design
- Describing a protocol to design mini-binders for a multi domain not that well characterized target using Latent-X1 and to lesser extent Chai.
- The protocol used Latent-X1 to generate de novo sequences and initial poses, which were then refolded using Chai-1 to ensure the designs were structurally consistent and plausible.
- The final rank was determined by the equation score = 2.0 * Binder PTM - 0.1 * min-iPAE - 0.1 * complex RMSD. This formula prioritized high global confidence (PTM) while penalizing designs where the Latent-X1 pose and Chai-1 refolded structure disagreed (iPAE and RMSD).
- To handle the complex, multidomain IgE interface, they first designed binders against a smaller, stable seed on the epsilon3 domain before iteratively expanding the interface toward the full receptor-binding site.
- Out of hundreds of generated designs, fewer than 80 candidates across two rounds were selected for wet-lab testing, resulting in a 6% hit rate and the identification of three specific IgE-binding miniproteins
- 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%.
2026-01-20
Context-aware Multi-Property Antibody Predictor: a Novel Framework Integrating Text and Protein Language Models
- developability
- Prompt-based, in-context prediction of antibody developability properties using large language models, rather than training separate predictors per property.
- As a baseline, they evaluate TxGemma, a therapeutics-specific multimodal LLM that supports task switching via prompts and is fine-tuned using LoRA.
- The study relies on a very large antibody dataset (~876k heavy chains) with in-silico–computed biophysical developability properties, combining sequence-based and structure-based predictors.
- Models are trained and evaluated using prompts that include antibody sequences together with partially observed property/value pairs, asking the model to infer a missing property for a query sequence.
- To prevent shortcut learning where the model ignores context and relies only on sequence, the authors introduce AB-context-aware training, which applies a random latent transformation jointly to context properties and targets during training, forcing explicit use of contextual information.
- By simulating batch effects, they show that standard fine-tuned TxGemma degrades sharply as batch bias increases (from ~0.99 Spearman ρ with no bias to ~0.95 with moderate bias and ~0.58 with strong bias), whereas context-aware training remains robust even under strong batch effects.
2026-01-20
Origin-1: a generative AI platform for de novo antibody design against novel epitopes
- protein design
- De novo platform for epitope-specific antibody design against “zero-prior” targets, i.e. antigen sites with no known antibody–antigen or protein–protein complex structures and limited homology to previously solved interfaces.
- The method combines three tightly integrated components: AbsciDiff, an all-atom diffusion model fine-tuned from Boltz-1 to generate epitope-conditioned antibody–antigen complex structures; IgDesign2, a structure-conditioned paired heavy–light CDR sequence design model; and AbsciBind, a modified AF-Unmasked / AlphaFold-Multimer–based scoring protocol using ipTM-derived interface confidence to rank and filter designs.
- The platform was evaluated on 10 zero-prior protein targets, with fewer than 100 antibody designs per target advanced to experimental testing; specific binders were successfully identified for 4 targets (COL6A3, AZGP1, CHI3L2, IL36RA).
- Experimental validation demonstrated both structural and functional accuracy, including cryo-EM confirmation at near-atomic resolution (DockQ 0.73–0.83) for two targets and AI-guided affinity maturation yielding a functional IL36RA antagonist with ~100 nM potency.
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.
2026-01-20
ImmunoGeNN: Accelerating Early Immunogenicity Assessment for Generative Design of Biologics
- developability
- Faster free to use version of NetMHCPIIpan for deimmunization.
- The model uses a small neural network (MLP) trained on one-hot encoded 15-mer peptides. To improve accuracy, it identifies the strongest 9-residue ‘binding core’ within those peptides and aligns them before scoring.
- The training data isn't experimental directly but distilled from NetMHCIIpan-4.3. The authors took 75,000 peptides, ran them through the original tool, and weighted the results by how common 97 different DRB1 alleles are in North America to create a single risk score.
- By predicting the final risk score in one pass, rather than calculating 97 individual allele bindings, it runs 300,000x faster while keeping a 95% correlation with the original tool's results.
- To prove it works on real drugs, they tested it against MAPPs data (physical peptide presentation) from vatreptacog alfa, a drug that failed clinical trials due to immune reactions. It successfully flagged the same high-risk mutations as the much slower original software.
- Its main value is the speed and differentiability wrt NetMHCIIpan, so it can sit in generative AI pipelines. It allows designers to screen millions of protein variants for "self" vs "non-self" peptides in minutes rather than weeks.
2026-01-09
NbBench: Benchmarking Language Models for Comprehensive Nanobody Tasks
- language models
- nanobodies
- Benchmarking of pretrained protein, antibody, and nanobody language model representations on a comprehensive suite of nanobody-specific tasks.
- The authors introduce eight tasks spanning variable-region annotation, CDR infilling, antigen binding prediction, paratope prediction, affinity prediction, polyreactivity, thermostability, and nanobody type classification (e.g. VHH, VNAR, conventional antibody chains).
- They evaluate generic protein LMs, antibody-specific LMs, and nanobody-specific LMs under a unified and standardized benchmark.
- All backbone models are kept frozen, with task-specific lightweight heads trained on top to isolate representational quality.
- No single model consistently outperforms others across all tasks, showing that nanobody-specific pretraining alone does not guarantee superior performance over antibody-specific or generic protein language models.
2026-01-09
Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties
- developability
- databases
- FLAb2 substantially expands existing antibody benchmarks, introducing the largest public dataset to date with a strong focus on developability rather than binding alone.
- A broad spectrum of models is evaluated, including generic protein language models, antibody-specific models, structure-aware predictors, and simple physics-based baselines such as charge and pI calculations.
- Zero-shot predictions from pretrained protein models are generally weak and unreliable for antibody developability. Surprisingly, simple charge-based features often outperform large models for properties such as aggregation, polyreactivity, and pharmacokinetics.
- Intrinsic properties (e.g. thermostability, expression) are substantially easier to predict than extrinsic or context-dependent properties such as polyreactivity, pharmacokinetics, or immunogenicity.
- Few-shot learning improves performance, but even the best models typically achieve only moderate correlations (ρ ≈ 0.4–0.6) on statistically robust datasets, highlighting the difficulty of the task.
- Incorporating structural information improves predictions, particularly in the zero-shot setting, and helps reduce biases present in sequence-only models.
- Many pretrained models primarily capture evolutionary signal, effectively measuring distance from germline rather than true developability. Encouragingly, this germline bias largely disappears once models are fine-tuned in a few-shot setting.
- Scaling model size alone provides limited benefit. Given sufficient training data, simple one-hot encodings paired with small neural networks can match or outperform billion-parameter protein language models, emphasizing that data quality and quantity matter more than model scale.