Unraveling the impact of ZG16B missense mutations: computational prediction of structural and functional consequences.

Abstract

Human Zymogen granule protein 16 homolog B (ZG16B), a secreted lectin, plays critical roles in tumor progression and metastasis through regulating autocrine and paracrine signaling. Its overexpression in multiple cancers, including pancreatic, colorectal, breast, ovarian, and prostate cancers, highlights its potential as a biomarker and therapeutic target. However, despite its elevated expression in multiple tumor types, the structural and functional consequences of genetic variants in ZG16B remain poorly understood. This study aimed to characterize deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the ZG16B gene and evaluate their potential structural and functional effects using a comprehensive in-silico pipeline. Out of 28 nsSNPs, four high-confidence deleterious variants W97R, E117K, Y142N, and P186L were prioritized based on predictions from SIFT, PolyPhen-2, SNAP2, Meta-SNP, and PhD-SNP. Homology models were built using SWISS-MODEL, and structural impacts were assessed with HOPE, Missense3D, and DynaMut, which indicated disrupted hydrogen bonding, altered hydrophobic cores, and local destabilization. Results from stability prediction using DUET, iStable, and MuPro further supported the destabilizing effects. Molecular dynamics simulations (500ns) showed increased root mean square deviation (RMSD) and altered root-mean-square fluctuation (RMSF) of the variants. Additionally, protein-protein interaction analysis using STRING and molecular docking revealed reduced binding affinity between mutant ZG16B and lysozyme (LYZ), with weakened hydrogen bonding and hydrophobic interactions. These findings suggest that while ZG16B is overexpressed in tumors, deleterious mutations may impair its function, contributing to disease progression through structural destabilization and disrupted protein interactions. The present study is expected to assist future research in ZG16B mutation interpretation, variant pathogenicity, and diagnostic approaches for cancers.

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Supplementary information: The online version contains supplementary material available at 10.1007/s40203-025-00366-w.