The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Abstract

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8.These structures were used to seed massively distributed molecular simulations that generated 2 six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

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“…Thanks to continuous advances in simulation performance, many well‐optimized MD engines like that of GROMACS have useful trajectory generation rates even without large‐scale parallelization. Additionally, problems that previously required the generation of a few long trajectories are nowadays often addressed by ensemble methods that instead need a large number of shorter trajectories . Hence, large compute resources with a single fast interconnect are often not a must.…”

“…Additionally, problems that previously required the generation of a few long trajectories are nowadays often addressed by ensemble methods that instead need a large number of shorter trajectories. [11][12][13][14][15] Hence, large compute resources with a single fast interconnect are often not a must. Instead, the ideal hardware in these instances is one with fast "islands" and a more modest interconnect between these.…”

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“…As for kinetics, the binding/efflux mechanism may differ 37 . As already noticed, 10 drugs with higher molecular weight binds to A state before B state, while drugs with lower molecular weight bind to B state directly (or binding to A state is transient and has not been seen yet).…”

“…36 As for kinetics, the binding/efflux mechanism may differ. 37 As already noticed, 10 drugs with higher molecular weight binds to A state before B state, while drugs with lower molecular weight bind to B F I G U R E 5 AcrB: Evolution of interfaces between subdomains within a monomer and across monomers. (Top: interfaces within a monomer.)…”