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Computational Design of Miniprotein Binders
- Source :
- Computational Peptide Science, Computational Peptide Science, 2405, Springer US, pp.361-382, 2022, Methods in Molecular Biology, ⟨10.1007/978-1-0716-1855-4_17⟩, Methods in Molecular Biology ISBN: 9781071618547
- Publication Year :
- 2022
- Publisher :
- HAL CCSD, 2022.
-
Abstract
- International audience; 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.
- Subjects :
- Protein–protein interaction
Binding affinity
Multistate protein design
[SDV.BIO]Life Sciences [q-bio]/Biotechnology
SARS-CoV-2
Miniprotein binders
Computational protein design
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
[INFO.INFO-BT]Computer Science [cs]/Biotechnology
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-07-161854-7
- ISBNs :
- 9781071618547
- Database :
- OpenAIRE
- Journal :
- Computational Peptide Science, Computational Peptide Science, 2405, Springer US, pp.361-382, 2022, Methods in Molecular Biology, ⟨10.1007/978-1-0716-1855-4_17⟩, Methods in Molecular Biology ISBN: 9781071618547
- Accession number :
- edsair.doi.dedup.....276744f59689bfc2305e0efa906c2ccf