ArticleMethods in molecular biology (Clifton, N.J.)2022
Computational Design of Miniprotein Binders.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development of Miniprotein Binders to Inhibit VEGF-Induced Corneal Neovascularization.Investigative ophthalmology & visual science · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Upstream open reading frames: new players in the landscape of cancer gene regulation.NAR cancer · 2024Review
- Computational design of BclxL inhibitors that target transmembrane domain interactions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.
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Registered trials
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