ArticleProceedings of the National Academy of Sciences of the United States of America2023
In silico evolution of autoinhibitory domains for a PD-L1 antagonist using deep learning models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 36 citations in OpenAlex.
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- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Optimized Quantitative Bacterial Two-Hybrid (qB2H) for Protein-Protein Interaction Assessment.Computational and structural biotechnology journal · 2026Article
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- Training bias and sequence alignments shape protein-peptide docking by AlphaFold and related methods.Protein science : a publication of the Protein Society · 2025Article
- State-Specific Peptide Design Targeting G Protein-Coupled Receptors.Journal of chemical information and modeling · 2025Article
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- Efficient protein structure generation with sparse denoising models.Nature machine intelligence · 2025Article
- Design of Ig-like binders targeting α-synuclein fibril for mitigating its pathological activities.Nature communications · 2025Article
- Design of linear and cyclic peptide binders from protein sequence information.Communications chemistry · 2025Article
- Next-Generation Vaccines for Co-Circulating PEDV and TGEV: Integrating Nucleic Acid Platforms, Mucosal Delivery, and AI-Driven Antigen Design.Transboundary and emerging diseases · 2025Review
- RoseTTAFold diffusion-guided short peptide design: a case study of binders against Keap1/Nrf2.Computational and structural biotechnology journal · 2025Article
- Tailored structured peptide design with a key-cutting machine approach.Nature machine intelligence · 2025Article
- Afpdb: an efficient structure manipulation package for AI protein design.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. With the goal of creating an anticancer agent that is inactive until reaching the tumor environment, we sought to create autoinhibited (or masked) forms of the PD-L1 antagonist that can be unmasked by tumor-enriched proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease-sensitive linker, and binding to PD-L1 was measured with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1, and the top-performing AiDs were selected for further characterization as single-domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (K
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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.