Abstract

The catalytic function of lysyl hydroxylase-2 (LH2), a member of the Fe(II)/αKG-dependent oxygenase superfamily, is to catalyze the hydroxylation of lysine to hydroxylysine in collagen, resulting in stable hydroxylysine aldehyde-derived collagen cross- links (HLCCs). Reports show that high amounts of LH2 lead to the accumulation of HLCCs, causing fibrosis and specific types of cancer metastasis. Some members of the Fe(II)/αKG-dependent family have also been reported to have intramolecular O2 tunnels, which aid in transporting one of the required cosubstrates into the active site. While LH2 can be a promising target to combat these diseases, efficacious inhibitors are still lacking. We have used computational simulations to investigate a series of 44 small molecules as lead compounds for LH2 inhibition. Tunneling analyses indicate the existence of several intramolecular tunnels. The lengths of the calculated O2-transporting tunnels in holoenzymes are relatively longer than those in the apoenzyme, suggesting that the ligands may affect the enzyme’s structure and possibly block (at least partially) the tunnels. The sequence alignment analysis between LH enzymes from different organisms shows that all of the amino acid residues with the highest occurrence rate in the oxygen tunnels are conserved. Our results suggest that the enolate form of diketone compounds establishes stronger interactions with the Fe(II) in the active site. Branching the enolate compounds with functional groups such as phenyl and pyridinyl enhances the interaction with various residues around the active site. Our results provide information about possible leads for further LH2 inhibition design and development.

Document Type

Article

Publication Date

1-2023

Notes/Citation Information

© 2023 American Chemical Society

Digital Object Identifier (DOI)

https://doi.org/10.1021/acs.jcim.2c01448

Funding Information

This work was partially supported by the National Institutes of Health (R01GM108583, R01GM106137, R01GM114237, R01CA105155, R00CA225633, and P50CA070907) and National Science Foundation (CHE-2217856 and CHE-1856173), the Cancer Prevention and Research Institute of Texas (award numbers RP160657, RP210088, and RP160652), and the Welch Foundation (award number F-1390). Computational time was provided by the University of Texas at Dallas CyberInfrastructure Facilities and the University of North Texas CASCaM CRUNTCh3 high-performance cluster, which was partially supported by NSF grant Nos. CHE-1531468 and OAC- 2117247. Additional computing time from XSEDE Project TG-CHE160044 is gratefully acknowledged. E.A.V.-M. thanks CONACyT for funding.

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