Deep learning guided design of dynamic proteins.

Autor:	Guo AB; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, CA 94143, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA., Akpinaroglu D; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, CA 94143, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA., Kelly MJS; Department of Pharmaceutical Chemistry, University of California, San Francisco; San Francisco, CA 94143, USA., Kortemme T; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco; San Francisco, CA 94143, USA.; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco; San Francisco, CA 94143, USA.; Quantitative Biosciences Institute, University of California, San Francisco; San Francisco, CA 94143, USA.; Chan Zuckerberg Biohub; San Francisco, CA 94143, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Jul 19. Date of Electronic Publication: 2024 Jul 19.
DOI:	10.1101/2024.07.17.603962
Abstrakt:	Deep learning has greatly advanced design of highly stable static protein structures, but the controlled conformational dynamics that are hallmarks of natural switch-like signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep-learning-guided approach for de novo design of dynamic changes between intra-domain geometries of proteins, similar to switch mechanisms prevalent in nature, with atom-level precision. We solve 4 structures validating the designed conformations, show microsecond transitions between them, and demonstrate that the conformational landscape can be modulated by orthosteric ligands and allosteric mutations. Physics-based simulations are in remarkable agreement with deep-learning predictions and experimental data, reveal distinct state-dependent residue interaction networks, and predict mutations that tune the designed conformational landscape. Our approach demonstrates that new modes of motion can now be realized through de novo design and provides a framework for constructing biology-inspired, tunable and controllable protein signaling behavior de novo . Competing Interests: Competing interests: Authors declare that they have no competing interests.
Databáze:	MEDLINE
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