First person – Jia Yu and Pei-Ju Liao

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Jia Yu and Pei-Ju Liao are co-first authors on ‘ Structural model of human PORCN illuminates disease-associated variants and drug-binding sites’, published in JCS. Jia is a senior postdoc in the lab of David Virshup at Duke-NUS Medical School, Singapore, investigating Wnt secretion and signalling; in particular, how Wnt trafficking and secretion is regulated by two integral membrane proteins, porcupine and WLS. Pei-Ju is a research assistant in the same lab, investigating protein–protein interactions in the systems biology of signalling pathways using protein structure modelling and protein complex simulation.

Structural model of PORCN illuminates disease-associated variants and drug binding sites

Wnt signaling is essential for normal development and is a therapeutic target in cancer. The enzyme PORCN, or porcupine, is a membrane-bound O-acyltransferase (MBOAT) that is required for the post-translational modification of all Wnts, adding an essential mono-unsaturated palmitoleic acid to a serine on the tip of Wnt hairpin 2. Inherited mutations in PORCN cause focal dermal hypoplasia, and therapeutic inhibition of PORCN slows the growth of Wnt-dependent cancers. Based on homology to mammalian MBOAT proteins we developed and validated a structural model of PORCN. The model accommodates palmitoleoyl-CoA and Wnt hairpin 2 in two tunnels in the conserved catalytic core, shedding light on the catalytic mechanism. The model predicts how previously uncharacterized human variants of uncertain significance can alter PORCN function. Drugs including ETC-159, IWP-L6 and LGK-974 dock in the PORCN catalytic site, providing insights into PORCN pharmacologic inhibition. This structural model enhances our mechanistic understanding of PORCN substrate recognition and catalysis as well as the inhibition of its enzymatic activity and can facilitate the development of improved inhibitors and the understanding of disease relevant PORCN mutants.

Structural model of PORCN illuminates disease-associated variants and drug binding sites

Wnt signaling is essential for normal development and is a therapeutic target in cancer. The enzyme PORCN, or porcupine, is a membrane-bound O-acyltransferase (MBOAT) that is required for the post-translational modification of all Wnts, adding an essential mono-unsaturated palmitoleic acid to a serine on the tip of Wnt hairpin 2. Inherited mutations in PORCN cause focal dermal hypoplasia, and therapeutic inhibition of PORCN slows the growth of Wnt-dependent cancers. Here, based on homology to mammalian MBOAT proteins we develop and validate a molecular structural model of PORCN. The model accommodates palmitoleoyl-CoA and Wnt hairpin 2 in two tunnels in the conserved catalytic core, shedding light on the catalytic mechanism. The model predicts how previously uncharacterized human variants of uncertain significance can alter PORCN function. Drugs including ETC-159, IWP-L6 and LGK-974 dock in the PORCN catalytic site, providing insights into PORCN pharmacologic inhibition. This structural model provides mechanistic insights into PORCN substrate recognition and catalysis as well as the inhibition of its enzymatic activity and can facilitate the development of improved inhibitors and the understanding of disease relevant PORCN mutants.

Transport of Ketoprofen in Mammalian Blood Plasma

SummaryKetoprofen is a popular non-steroidal anti-inflammatory drug (NSAID) transported in the bloodstream mainly by serum albumin (SA). Ketoprofen is known to have multiple side effects and interactions with hundreds of other drugs, which might be related to its vascular transport by SA. Our work reveals that ketoprofen binds to a different subset of drug binding sites on human SA than has been observed for other species, despite the conservation of drug sites between species. We discuss potential reasons for the observed differences in the drug’s preferences for particular sites, including ketoprofen binding determinants in mammalian SAs and the effect of fatty acids on drug binding. The presented results show that the SA drug sites to which a particular drug binds cannot be easily predicted based only on a complex of SA from another species and the conservation of drug sites between species.

Finding Druggable Sites in Proteins using TACTICS

Structure-based drug discovery efforts require knowledge of where drug-binding sites are located on target proteins. To address the challenge of finding druggable sites, we developed a machine-learning algorithm called TACTICS (Trajectory-based Analysis of Conformations To Identify Cryptic Sites), which uses an ensemble of molecular structures (such as molecular dynamics simulation data) as input. First, TACTICS uses k-means clustering to select a small number of conformations that represent the overall conformational heterogeneity of the data. Then, TACTICS uses a random forest model to identify potentially bindable residues in each selected conformation, based on protein motion and geometry. Lastly, residues in possible binding pockets are scored using fragment docking. As proof-of-principle, TACTICS was applied to the analysis of simulations of the SARS-CoV-2 main protease and methyltransferase and the Yersinia pestis aryl carrier protein. Our approach recapitulates known small-molecule binding sites and predicts the locations of sites not previously observed in experimentally determined structures. The TACTICS code is available at https://github.com/Albert-Lau-Lab/tactics_protein_analysis.