ArticleNature communications2024
An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- A Synthetic Platform for Antibody Junctional Diversification Beyond Natural Constraints.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deciphering the evolutionary origin of the enantioselectivity of short-chain dehydrogenases from plants toward 1-borneol.Nature communications · 2026Article
- Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026Article
- ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries.Nature communications · 2026Article
- Human antibodies as emerging drugs for antimicrobial resistance.Trends in immunology · 2026Review
- Article
- Structural basis of chaperone mechanisms in cells and the evolutionary emergence of the protein world.bioRxiv : the preprint server for biology · 2026Article
- Article
- A bio-inspired computational pipeline for antibody screening and repurposing.Briefings in bioinformatics · 2026Article
- BCRInsight: an antibody language model to decode biological signals from BCR sequences.Briefings in bioinformatics · 2026Article
- Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.bioRxiv : the preprint server for biology · 2025Article
- Engineering mammalian protein secretion: Toward the convergence of high-throughput biology and computational methods.Cell systems · 2025Review
- Uncovering the molecular basis of kinase activity and substrate recognition with phospho-PCA.bioRxiv : the preprint server for biology · 2025Article
- Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface.bioRxiv : the preprint server for biology · 2025Article
- ADAPT-M: A workflow for rapid, quantitativebioRxiv : the preprint server for biology · 2025Article
- Separating selection from mutation in antibody language models.bioRxiv : the preprint server for biology · 2025Article
- Investigating the volume and diversity of data needed for generalizable antibody-antigen ΔΔG prediction.Nature computational science · 2025Article
- Retrospective SARS-CoV-2 human antibody development trajectories are largely sparse and permissive.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Application of Machine Learning on Antibody Discovery and Optimization.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.