Methods for computer-assisted PROTAC design.


Journal

Methods in enzymology
ISSN: 1557-7988
Titre abrégé: Methods Enzymol
Pays: United States
ID NLM: 0212271

Informations de publication

Date de publication:
2023
Historique:
medline: 23 10 2023
pubmed: 20 10 2023
entrez: 20 10 2023
Statut: ppublish

Résumé

Proximity-induced pharmacology is an emerging field in chemical biology and drug discovery where a small molecule induces non-natural interactions between two proteins, leading to specific phenotypic responses. Proteolysis targeting chimeras (PROTACs) are the most mature examples, where ligands for an E3 ligase and a target protein are linked to induce the ubiquitination and subsequent degradation of the target. The discovery of PROTACs typically relies on a trial-and-error approach where chemical handles and linker chemistry, length and attachment points are systematically varied in the hope that one of the combinations will produce an active molecule. Novel computational methods and tools are developed in an attempt to rationalize and accelerate this process and differ significantly from traditional structure-based drug design approaches. In this chapter, we review three different solutions for computer-assisted PROTAC design: MOE, ICM and PRosettaC. Specifically, we describe protocols to predict the structure of ternary complexes (E3 ligase-PROTAC-target protein) and to screen virtually libraries of PROTAC candidates. We also provide troubleshooting tips. Rational PROTAC design is still in its infancy. By opening this space to users and developers, we hope that this methods article will contribute to much needed advancement in the field.

Identifiants

pubmed: 37858533
pii: S0076-6879(23)00228-8
doi: 10.1016/bs.mie.2023.06.020
pii:
doi:

Substances chimiques

Ubiquitin-Protein Ligases EC 2.3.2.27
Proteins 0

Types de publication

Review Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

311-340

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Evianne Rovers (E)

Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada.

Matthieu Schapira (M)

Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada. Electronic address: matthieu.schapira@utoronto.ca.

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Classifications MeSH