Computational Antibody Papers
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annotation/numbering
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2025
TitleKey points
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.