ArticlemAbs
Reduction of therapeutic antibody self-association using yeast-display selections and machine learning.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 26 citations in OpenAlex.
- Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- In Vivo Auto-tuning of Antibody-Drug Conjugate Delivery to Maximize Efficacy Using High-Avidity, Low-Affinity Antibodies.Molecular cancer therapeutics · 2026Article
- Ultra-Dilute Developability Analysis of Antibody Self-Association and Non-Specific Binding.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics.Molecular pharmaceutics · 2025Article
- A Porcine Monoclonal Antibody Targeting a Conserved GP4 Epitope Protects against In Vivo Infection via the Induction of Broad-Spectrum PRRSV Neutralization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Investigating the volume and diversity of data needed for generalizable antibody-antigen ΔΔG prediction.Nature computational science · 2025Article
- Article
- Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and machine learning.Computational and structural biotechnology journal · 2024Article
- Deep learning-based design and experimental validation of a medicine-like human antibody library.Briefings in bioinformatics · 2024Article
- Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions.Cell reports · 2024Article
- A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage and its implications.Antibody therapeutics · 2024Article
- Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Bispecific antibody shuttles targeting CD98hc mediate efficient and long-lived brain delivery of IgGs.Cell chemical biology · 2024Article
- Internalization of therapeutic antibodies into dendritic cells as a risk factor for immunogenicity.Frontiers in immunology · 2024Article
- Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data.Bioinformatics (Oxford, England) · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Selection of biophysically favorable antibody variants using a modified Flp-In CHO mammalian display platform.Frontiers in bioengineering and biotechnology · 2023Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
Abstract
Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.
Indexed as
Identifiers
What OpenQuestion holds
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.