The dynamic conformational landscape of the protein methyltransferase SETD8

Abstract

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape.

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SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome

“…[13][14][15] to the connection between phenotype and genotype. [16][17][18] Translational applications have included new means to combat antimicrobial resistance, Ebola virus, and SFTS virus. [19][20][21] Figure 1: Summary of Folding@home's computational power.…”

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of Cyclophilin A

“…Capturing the complete dynamics of proteins to understand their many functions has long been a goal of structural biology. Experimentally, trapping proteins into relevant conformational states and characterising these at atomic resolution provides tremendous insight [1][2][3] . Such static 'snapshots' are inherently incomplete and especially in cases where proteins are particularly dynamic, understanding function can require characterisations of conformational states that are formed only transiently, and populated as sparsely as a few per cent [4][5][6] .…”

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