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Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties

Authors :
Davide Sala
Peter W. Hildebrand
Jens Meiler
Source :
Frontiers in Molecular Biosciences, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Determining the three-dimensional structure of proteins in their native functional states has been a longstanding challenge in structural biology. While integrative structural biology has been the most effective way to get a high-accuracy structure of different conformations and mechanistic insights for larger proteins, advances in deep machine-learning algorithms have paved the way to fully computational predictions. In this field, AlphaFold2 (AF2) pioneered ab initio high-accuracy single-chain modeling. Since then, different customizations have expanded the number of conformational states accessible through AF2. Here, we further expanded AF2 with the aim of enriching an ensemble of models with user-defined functional or structural features. We tackled two common protein families for drug discovery, G-protein-coupled receptors (GPCRs) and kinases. Our approach automatically identifies the best templates satisfying the specified features and combines those with genetic information. We also introduced the possibility of shuffling the selected templates to expand the space of solutions. In our benchmark, models showed the intended bias and great accuracy. Our protocol can thus be exploited for modeling user-defined conformational states in an automatic fashion.

Details

Language :
English
ISSN :
2296889X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Molecular Biosciences
Publication Type :
Academic Journal
Accession number :
edsdoj.7ad60cbd2fa9450285cc17b01fb01e18
Document Type :
article
Full Text :
https://doi.org/10.3389/fmolb.2023.1121962