Computational Antibody Papers
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2026-09-29
Fast retrieval of structurally similar antibodies from large sequence databases with AbSLang
- structure prediction
- annotation/numbering
- Contrastive-learned sequence embeddings that predict antibody complementarity-determining region (CDR) structural similarity (RMSD) without explicit 3D structure generation, enabling ultra-fast structural retrieval across multi-million sequence databases.
- AbSlang is trained on SAbDab structure pairs- it predicts CDR RMSD with mean absolute errors (MAE = 0.42--0.56A) comparable to state-of-the-art 3D structure predictors like AlphaFold3 and ABodyBuilder3, while outperforming sequence-identity baselines.
- Using a twin neural network with 8 FlashAttention transformer encoder layers, AbSLang projects protein language model embeddings (IgT5 for paired VH/VL; ESM-C for single-domain VH/VHH) into 512-dimensional vectors trained against dynamic time warping (DTW) loop alignments.
- Integrated with FAISS product quantization, AbSLang-search queries 2.5 million paired sequences in 7 seconds using an 879 MB index (compared to 18 minutes and 508 GB of storage for ABodyBuilder3 PDB files) and searches 10 million heavy chains in 1.22 seconds.
- AbSLang-search matches explicit structure prediction workflows in identifying structurally similar antibodies (top-20 neighbor overlap of 13.60/20 vs. 14.40/20), enabling scalable mining of structurally convergent antibodies across distinct genetic lineages.
- AbSlang was not benchmarked against the likes of Foldseek.
- Numbering software for antibodies that is purely structural.
- Structure-based Antibody Renumbering (SAbR) infers standardized residue indices strictly from backbone heavy-atom coordinates.
- It matches the accuracy of sequence-based tools on standard antibodies while dramatically outperforming them on engineered antibodies with unusually long or unnatural CDR loops.
- The algorithm generates per-residue embeddings using a fine-tuned ProteinMPNN encoder and aligns query backbones to position-averaged reference embeddings built from over 100,000 IMGT-numbered ImmuneBuilder models, followed by deterministic framework anchor refinement for CDR loops.
- SAbR achieved a 97.8% framework accuracy on SAbDab test structures and >99% accuracy on de novo RFantibody designs, demonstrating robust handling of complex multidomain constructs (e.g., scFvs) and structural insertions that confound sequence alignment methods.
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.
- Fast and reliable numbering tool with an inbuilt free germline database, unifying functionalities of tools such as IgBlast, ANARCI etc.
- It can number both amino acid and nucleotide sequences.
- Rather than using statistical methods such as HMMs, MMSeqs-like methodology was used for rapid alignment.
- Alignments are more accurate than existing methods, with speed improvement, running on a CPU.
- Fast, alignment-based antibody numbering tool, significantly outperforming existing software in processing speed.
- Uses a simplified global alignment with a custom scoring matrix, facilitating rapid numbering of millions of sequences efficiently.
- Ensures accuracy comparable to established methods (ANARCI, AbNum) while numbering large-scale antibody datasets.
- Emphasizes interpretability and robustness, providing transparent sequence scoring useful for humanization tasks.
- New version of ANARCI - using language models.
- Employs a Seq2Seq language model eliminating the need for alignment-based numbering, thus generalizing well to novel sequences.
- Provides numbering that matches existing methods for >99.99% conserved residues and >99.94% CDR regions.
- Improved speed of the original HMM-based ANARCI when GPU is available.
- Can be fine-tuned for rare immunoglobulin domains (e.g., shark VNAR sequences, T-cell receptors), offering customizable antibody numbering workflows.
- Offline toolkit for antibody numbering and CDR delineation (ABRs).
- Provides an offline toolkit integrating five established antibody numbering schemes (Kabat, Chothia, IMGT, Aho, Martin).
- Uses IMGT as the source of Germlines.
- Allows prediction of Complementarity-Determining Regions (CDRs) and Antigen-Binding Regions (ABRs) through Hidden Markov Models (HMMs).
- Addresses data security concerns by enabling offline usage, beneficial for therapeutic antibody development.
- Achieves high recall (0.92) in identifying ABRs, making it superior to existing tools which rely heavily on online services.
2024-08-28
For antibody sequence generative modeling, mixture models may be all you need
- annotation/numbering
- Authors introduce AntPack - software for rapid numbering of antibody sequences, germline identification and humanization.
- Authors use a mixture model (so not ML!) on millions of sequences from NGS.
- The sequences are pre-numbered to standardize them and then assigned to clusters which offer explainability on germline assignment and residue probability at a given position.
- The method is very fast in comparison to HMM-based approaches such as ANARCI.
- Method is available via https://github.com/Wang-lab-UCSD/AntPack
2024-07-30
Digger: directed annotation of immunoglobulin and T cell receptor V, D, and J gene sequences and assemblies
- annotation/numbering
- Novel tool to automatically annotate immunoglobulin (and t-cell) genes from assemblies.
- They compared the human manual annotations from IMGT to what they identify and they were in close agreement for functional and open reading frame genes.
- By automating annotation, strict rules can be enforced avoiding manual curation errors.
- Software is available at: https://github.com/williamdlees/Digger
2024-07-09
The immunogenicity of human-origin therapeutic antibodies are associated with V gene usage
- developability
- annotation/numbering
- They demonstrate that germline usage and distance from the germline are correlated with ADA.
- They use OAS as a reference for natural NGS sequences, but they exclude studies that focus on single isotopes such as IgM, IgG.
- They collected ADA points from therapeutics from IMGT and related these to FDA labels in the first instance and heterogenous search second.
- If they had multiple values for ADA, they then take the maximum value.
- There are 14 amino acid differences between the IGHV4-34*10 and IGHV4-34*11 alleles, a 14.4% sequence difference.
- Healthy donors have similar gene usages as diseased ones.
- They show that V gene usage has correlation with ADA just as the number of mutations.