Increased slow dynamics defines ligandability of BTB domains

Vladlena Kharchenko, Brian M. Linhares, Megan Borregard, Iwona Czaban, Jolanta Grembecka, Mariusz Jaremko, Tomasz Cierpicki () and Łukasz Jaremko ()
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Vladlena Kharchenko: Biological and Environmental Science & Engineering (BESE), King Abdullah University of Science and Technology (KAUST)
Brian M. Linhares: University of Michigan
Megan Borregard: University of Michigan
Iwona Czaban: Biological and Environmental Science & Engineering (BESE), King Abdullah University of Science and Technology (KAUST)
Jolanta Grembecka: University of Michigan
Mariusz Jaremko: Biological and Environmental Science & Engineering (BESE), King Abdullah University of Science and Technology (KAUST)
Tomasz Cierpicki: University of Michigan
Łukasz Jaremko: Biological and Environmental Science & Engineering (BESE), King Abdullah University of Science and Technology (KAUST)

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

Date: 2022
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DOI: 10.1038/s41467-022-34599-6

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