Human GNL3 (nucleostemin) is a recently discovered nucleolar protein with pivotal functions in maintaining genomic integrity and determining cell fates of various normal and cancerous stem cells. Recent reports suggest that targeting this GTP-binding protein may have therapeutic value in cancer. Although, sequence analyzing revealed that nucleostemin (NS) comprises 5 permuted GTP-binding motifs, a crystal structure for this protein is missing at Protein Data Bank (PDB). Obviously, any attempt for predicting of NS structure can further our knowledge on its functional sites and subsequently designing molecular inhibitors. Herein, we used bioinformatics tools and could model 262 amino acids of NS (132-393 aa). Initial models were built by MODELLER, refined with Scwrl4 program, and validated with ProsA and Jcsc databases as well as PSVS software. Then, the best quality model was chosen for motif and domain analyzing by Pfam, PROSITE and PRINTS. The final model was visualized by vmd program. This predicted model may pave the way for next studies regarding ligand binding states and interaction sites as well as screening of databases for potential inhibitors.