ArticleMolecular diversity2025
Optimizing antibody stability and efficacy in CD47- SIRPα inhibition via computational approaches.
Article in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy.RSC medicinal chemistry · 2026Review
- Macrophage polarization in hematologic cancers: mechanisms and therapeutic strategies.Blood research · 2026Review
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Authors and funding
2 authors.
Funding
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Abstract
CD47, a cell surface protein, serves as a "don't eat me" signal that prevents immune cells from engulfing healthy cells upon its interaction with SIRPα. Cancer cells exploit this mechanism by overexpressing CD47 to evade immune destruction. Blocking the interaction between CD47 and its receptor, SIRPα, is a promising therapeutic strategy. Targeting the interactions between these surface proteins with small molecules is quite challenging, and on the other hand, antibodies offer potential. However, the interactions between antigen (CD47) and antibody (B6H12.2) play a crucial role in this scenario, and increasing the affinity by mutating the interacting residues might impact the inclination and effectiveness of the antibody towards antigen. Thus, this study focuses on designing antibodies with increased affinity and stability towards the antigen compared to the wild-type. Residual scanning calculations were performed to mutate the interacting as well as the hydrophobic residues of the antibody and affinity was assessed. Computational approaches, including antigen-antibody docking studies and molecular dynamics simulations, were employed to evaluate the affinity, stability and therapeutic potential of these modified antibodies.
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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.