Cancer continues its alarming progression worldwide with 18.1 million new cases and 9.6 million mortalities in 2018, according to the GLOBOCAN 2018 (Global cancer statistics) . “Human phosphatidylethanolamine-binding protein 1 (hPEBP1)”, novel member of the PEBP family newly genes recently updated in the Universal Protein Resource UniProt (https://www.uniprot.org/), is implicated in many cellular processes, including “signal transduction, cell cycle, inflammation, adhesion, proliferation, differentiation, apoptosis, autophagy, circadian rhythm and mitotic spindle checkpoint” . Furthermore, this protein encodes a member of PEBP1 family of proteins and regulate multiple signaling pathways, namely the MAPK, NF-kappa B , and “glycogen synthase kinase-3 ( GSK-3 signaling pathways) . In addition, it can also inhibit the RAF1 kinase activity through inhibiting its activation and by dissociating the RAF1/MEK complex and acting as a competitive inhibitor of “MEK phosphorylation” . Also, it is associated with many human cancers by acting as a metastasis suppressor gene. Likewise, with multiple query sequences in the genome, it is necessary for the commitment of scientists and organizations to explore novel approaches for discovering the specific role of PEBP1 protein, that would open us a new ways in cancer prevention and therapy.
Ligand-based Computational approaches like 2D-QSAR analysis are widely investigated to describing the relationship between biological activities and molecular descriptors. These molecular descriptors are physicochemical properties, various topological indices, Molecular shape, Structural fragments, and Quantum chemistry (QC) descriptors . The selection of the accurate quantum chemical methods is of central importance in 2D-QSAR studies . There are many quantum chemical methods used in the geometry's optimization of molecules, we can cite the fast semi-empirical method (AM1) in terms of calculation which takes into account only valence electrons with the adjustable parameters . While “the density functional theory (DFT) method” including the electron-correlation to calculate energy of the system directly from the electron density . Therefore, the choice of the quantum chemical method may produce errors in the calculation of the molecular descriptor and, ultimately, a lack of adjustment in 2D-QSAR model development. Whereas, In CoMFA approach, we calculate the steric and electrostatic potentials around the molecules and we relates fields variations calculated to the biological activity . While, the CoMSIA strategy is introduced as extension of CoMFA analysis , with the five similarity indices which occupy the non-covalent interactions, namely hydrophobic effects, hydrogen-bond donor/acceptor potentials. However, as a result of the CoMFA and CoMSIA analyses, Contour areas are undertaken to locate the favored or unfavored regions surrounding molecules that would be needed to increase the biological activity. On the other hand, CoMFA and CoMSIA requires the best alignment of molecules to get better results, which poses a major problem in many cases of the 3D-QSAR studies. In order to overcome this problem, the newly Hologram QSAR (HQSAR) approach , which does not take into account molecular alignment in the QSAR model development is used. HQSAR analysis is based on the contribution of each fragment in the molecule to biological activity.
Homology modeling is commonly known approach as one of structure based methods that can predict with high accuracy the 3D structure of a protein from its amino acids sequence. Therefore, computational structural determination methods are needed to bridge this growing gap between the number of available sequences and the 3D structures of experimentally resolved proteins . Furthermore, in order to explore the binding affinity of the comparative protein model, molecular docking simulation is widely used to select the best ligand-protein interactions . On the other hand, molecular dynamics simulations are widely used to predict the stability of the protein-ligand interactions in the biological system .
Ursolic acid (3β-hydroxy-urs-12-en-28-oic acid) is a pentacyclic terpenoid, usually present in the stem bark, leaves or fruit peel . Recently, it has been reported that ursolic acid is one of the naturally abundant pentacyclic triterpene acid has vast pharmacological activities including antidiabetic, antioxidative , antiHIV, antihepatodamage, antimalarial, antimicrobial, cardiovascular, immunomodulatory effects, anti-inflammatory, and antitumor activities ,. However, UA has the following characteristics: low toxicity, liver protection and potential anti-cancer metastasis [19, 20]. In addition, recent progress in the clinical trial proved that Ursolic acid exhibit numerous anticancer effects with acceptable toxicity [21–23]. However, it is imperative to continue the research of the mechanism of action and signaling pathway studies of ursolic acid as a new anticancer drug.
The aim of the present study is to compare the accuracy of 2D-QSAR study by different theory levels, namely AM1 and DFT/(B3LYP/6-31G) methods by using Partial Least Squares (PLS) analysis. Meanwhile, the 3D-QSAR analysis has been undertaken to select the structural requirement needed to enhance the antiproliferative activities of ursolic acids through CoMFA, CoMSIA and HQSAR analyses with these two quantum optimization methods. Whereas, the homology modeling was carried out on PEBP1 query sequence as new target protein and its binding pocket was investigated by molecular docking simulation to explore the potential affinity of ursolic acid heterocyclic derivatives against the modeled PEBP1 protein.
At the end of this study, we predicted the design of three new heterocyclic usolic acid derivatives with higher in silico activities than the most active compound (M30) of the series studied, and then evaluated their binding affinity and stability respectively through molecular docking and molecular dynamics simulations.