Computational design of environmental sensors for the potent opioid fentanyl
By M. J. Bick, Per Greisen, K. Morey, Mauricio S. Antunes, David La, B. Sankaran, L. Reymond, Kai Johnsson, J. Medford, David Baker. 2017
97 citations recorded by the source corpus.
We describe the computational design of proteins that bind the potent analgesic fentanyl. Our approach employs a fast docking algorithm to find shape complementary ligand placement in protein scaffolds, followed by design of the surrounding residues to optimize binding affinity. Co-crystal structures of the highest affinity binder reveal a highly preorganized binding site, and an overall architecture and ligand placement in close agreement with the design model. We use the designs to generate plant sensors for fentanyl by coupling ligand binding to design stability. The method should be generally useful for detecting toxic hydrophobic compounds in the environment.
Subfields: Cellular and Molecular Neuroscience, Computational Theory and Mathematics, Spectroscopy
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In Cellular and Molecular Neuroscience
In Computational Theory and Mathematics
- Xingjie Pan
- Russ B. Altman
- Po-Ssu Huang
- Frank DiMaio
- Tanja Kortemme
- Ivan V. Anishchenko
- Namrata Anand
- Minkyung Baek
In Spectroscopy
- Po-Ssu Huang
- Raphael R. Eguchi
- Russ B. Altman
- Namrata Anand-Achim
- Alexander Derry
- Carla P. Perez
- Frank DiMaio
- Tanja Kortemme
Citations in the corpus
- Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom (2023, background): “Previous efforts have involved docking molecules into large sets of native or expert-curated protein scaffold structures(40)-(41).”
- Protein sequence design with a learned potential (2022, background): “omputational protein design has emerged as a powerful tool for rational protein design, enabling signi fi cant achievements in the engineering of therapeutics 1 – 3 , 4 – 6 , enzymes 7,8 , and more 9 – 11 .”
- Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Early studies designed de novo binding sites by manually de fi ning side chains that form favorable interactions with ligands (11, 20, 26).”
- Protein sequence design with a learned potential (2020, background): “These advances have enabled significant achievements in the engineering of therapeutics [13, 14, 15], biosensors [16, 17, 18], enzymes [19, 20, 21, 22], and more [23, 24, 25, 26, 27].”