ArticleACS central science2020
Computational Design of an Allosteric Antibody Switch by Deletion and Rescue of a Complex Structural Constellation.
Article in ACS central science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Integrative Computational Prediction Strategy for Antibody-Antigen Binding: A Case Study on Interleukin-1 Beta.Journal of chemical information and modeling · 2026Article
- Ligand-induced assembly of antibody variable fragments for the chemical regulation of biological processes.Cell chemical biology · 2025Article
- Design and engineering of genetically encoded protein biosensors for small molecules.Current opinion in biotechnology · 2022Review
- De novo protein fold families expand the designable ligand binding site space.PLoS computational biology · 2021Article
- The Masking Game: Design of Activatable Antibodies and Mimetics for Selective Therapeutics and Cell Control.ACS central science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Therapeutic monoclonal antibodies have transformed medicine, especially with regards to treating cancers and disorders of the immune system. More than 50 antibody-derived drugs have already reached the clinic, the majority of which target cytokines or cell-surface receptors. Unfortunately, many of these targets have pleiotropic functions: they serve multiple different roles, and often not all of these roles are disease-related. This can be problematic because antibodies act throughout the body, and systemic neutralization of such targets can lead to safety concerns. To address this, we have developed a strategy whereby an antibody's ability to recognize its antigen is modulated by a second layer of control, relying on addition of an exogenous small molecule. In previous studies, we began to explore this idea by introducing a deactivating tryptophan-to-glycine mutation in the domain-domain interface of a single-chain variable fragment (scFv), and then restoring activity by adding back indole to fit the designed cavity. Here, we now describe a novel computational strategy for enumerating larger cavities that can be formed by simultaneously introducing multiple adjacent large-to-small mutations; we then carry out a complementary virtual screen to identify druglike compounds to match each candidate cavity. We first demonstrate the utility of this strategy in a fluorescein-binding single-chain variable fragment (scFv) and experimentally characterize a triple mutant with reduced antigen-binding (Rip-3) that can be rescued using a complementary ligand (Stitch-3). Because our design is built upon conserved residues in the antibody framework, we then show that the same mutation/ligand pair can also be used to modulate antigen-binding in an scFv build from a completely unrelated framework. This set of residues is present in many therapeutic antibodies as well, suggesting that this mutation/ligand pair may serve as a general starting point for introducing ligand-dependence into many clinically relevant antibodies.
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.